diff --git a/README.md b/README.md
index 71193d5f3851f8aa0d2900814906c9d2e0203fc8..6a59e2ce9c9b05ed6a611dc4c9165d04ab0ba522 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,10 @@ sdk_version: 5.4.0
 app_file: app.py
 pinned: false
 license: apache-2.0
+models:
+  - ibm/materials.smi-ted
+  - ibm/materials.selfies-ted
+  - ibm/materials.mhg-ged
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..920995e3b5ae2fa967f79258a42480bb8d0cabeb
--- /dev/null
+++ b/app.py
@@ -0,0 +1,717 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+import matplotlib.pyplot as plt
+from PIL import Image
+from rdkit.Chem import Descriptors, QED, Draw
+from rdkit.Chem.Crippen import MolLogP
+import pandas as pd
+from rdkit.Contrib.SA_Score import sascorer
+from rdkit.Chem import DataStructs, AllChem
+from transformers import BartForConditionalGeneration, AutoTokenizer, AutoModel
+from transformers.modeling_outputs import BaseModelOutput
+import selfies as sf
+from rdkit import Chem
+import torch
+import numpy as np
+import umap
+import pickle
+import xgboost as xgb
+from sklearn.svm import SVR
+from sklearn.linear_model import LinearRegression
+from sklearn.kernel_ridge import KernelRidge
+import json
+
+import os
+
+os.environ["OMP_MAX_ACTIVE_LEVELS"] = "1"
+
+# my_theme = gr.Theme.from_hub("ysharma/steampunk")
+# my_theme = gr.themes.Glass()
+
+"""
+# カスタムテーマ設定
+theme = gr.themes.Default().set(
+    body_background_fill="#000000",  # 背景色を黒に設定
+    text_color="#FFFFFF",            # テキスト色を白に設定
+)
+"""
+"""
+import sys
+sys.path.append("models")
+sys.path.append("../models")
+sys.path.append("../")"""
+
+
+# Get the current file's directory
+base_dir = os.path.dirname(__file__)
+print("Base Dir : ", base_dir)
+
+import models.fm4m as fm4m
+
+
+# Function to display molecule image from SMILES
+def smiles_to_image(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    if mol:
+        img = Draw.MolToImage(mol)
+        return img
+    return None
+
+
+# Function to get canonical SMILES
+def get_canonical_smiles(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    if mol:
+        return Chem.MolToSmiles(mol, canonical=True)
+    return None
+
+
+# Dictionary for SMILES strings and corresponding images (you can replace with your actual image paths)
+smiles_image_mapping = {
+    "Mol 1": {"smiles": "C=C(C)CC(=O)NC[C@H](CO)NC(=O)C=Cc1ccc(C)c(Cl)c1", "image": "img/img1.png"},
+    # Example SMILES for ethanol
+    "Mol 2": {"smiles": "C=CC1(CC(=O)NC[C@@H](CCCC)NC(=O)c2cc(Cl)cc(Br)c2)CC1", "image": "img/img2.png"},
+    # Example SMILES for butane
+    "Mol 3": {"smiles": "C=C(C)C[C@H](NC(C)=O)C(=O)N1CC[C@H](NC(=O)[C@H]2C[C@@]2(C)Br)C(C)(C)C1",
+              "image": "img/img3.png"},  # Example SMILES for ethylamine
+    "Mol 4": {"smiles": "C=C1CC(CC(=O)N[C@H]2CCN(C(=O)c3ncccc3SC)C23CC3)C1", "image": "img/img4.png"},
+    # Example SMILES for diethyl ether
+    "Mol 5": {"smiles": "C=CCS[C@@H](C)CC(=O)OCC", "image": "img/img5.png"}  # Example SMILES for chloroethane
+}
+
+datasets = [" ","BACE", "ESOL", "Custom Dataset"]
+
+models_enabled = ["SELFIES-TED", "MHG-GED", "MolFormer", "SMI-TED"]
+
+fusion_available = ["Concat"]
+
+global log_df
+log_df = pd.DataFrame(columns=["Selected Models", "Dataset", "Task", "Result"])
+
+
+def log_selection(models, dataset, task_type, result, log_df):
+    # Append the new entry to the DataFrame
+    new_entry = {"Selected Models": str(models), "Dataset": dataset, "Task": task_type, "Result": result}
+    updated_log_df = log_df.append(new_entry, ignore_index=True)
+    return updated_log_df
+
+
+# Function to handle evaluation and logging
+def save_rep(models, dataset, task_type, eval_output):
+    return
+def evaluate_and_log(models, dataset, task_type, eval_output):
+    task_dic = {'Classification': 'CLS', 'Regression': 'RGR'}
+    result = f"{eval_output}"#display_eval(models, dataset, task_type, fusion_type=None)
+    result = result.replace(" Score", "")
+
+    new_entry = {"Selected Models": str(models), "Dataset": dataset, "Task": task_dic[task_type], "Result": result}
+    new_entry_df = pd.DataFrame([new_entry])
+
+    log_df = pd.read_csv('log.csv', index_col=0)
+    log_df = pd.concat([new_entry_df, log_df])
+
+    log_df.to_csv('log.csv')
+
+    return log_df
+
+
+log_df = pd.read_csv('log.csv', index_col=0)
+
+
+# Load images for selection
+def load_image(path):
+    return Image.open(smiles_image_mapping[path]["image"])# Image.1open(path)
+
+
+# Function to handle image selection
+def handle_image_selection(image_key):
+    smiles = smiles_image_mapping[image_key]["smiles"]
+    mol_image = smiles_to_image(smiles)
+    return smiles, mol_image
+
+
+def calculate_properties(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    if mol:
+        qed = QED.qed(mol)
+        logp = MolLogP(mol)
+        sa = sascorer.calculateScore(mol)
+        wt = Descriptors.MolWt(mol)
+        return qed, sa, logp, wt
+    return None, None, None, None
+
+
+# Function to calculate Tanimoto similarity
+def calculate_tanimoto(smiles1, smiles2):
+    mol1 = Chem.MolFromSmiles(smiles1)
+    mol2 = Chem.MolFromSmiles(smiles2)
+    if mol1 and mol2:
+        # fp1 = FingerprintMols.FingerprintMol(mol1)
+        # fp2 = FingerprintMols.FingerprintMol(mol2)
+        fp1 = AllChem.GetMorganFingerprintAsBitVect(mol1, 2)
+        fp2 = AllChem.GetMorganFingerprintAsBitVect(mol2, 2)
+        return round(DataStructs.FingerprintSimilarity(fp1, fp2), 2)
+    return None
+
+
+#with open("models/selfies_model/bart-2908.pickle", "rb") as input_file:
+#    gen_model, gen_tokenizer = pickle.load(input_file)
+
+gen_tokenizer = AutoTokenizer.from_pretrained("ibm/materials.selfies-ted")
+gen_model = BartForConditionalGeneration.from_pretrained("ibm/materials.selfies-ted")
+
+
+def generate(latent_vector, mask):
+    encoder_outputs = BaseModelOutput(latent_vector)
+    decoder_output = gen_model.generate(encoder_outputs=encoder_outputs, attention_mask=mask,
+                                        max_new_tokens=64, do_sample=True, top_k=5, top_p=0.95, num_return_sequences=1)
+    selfies = gen_tokenizer.batch_decode(decoder_output, skip_special_tokens=True)
+    outs = []
+    for i in selfies:
+        outs.append(sf.decoder(i.replace("] [", "][")))
+    return outs
+
+
+def perturb_latent(latent_vecs, noise_scale=0.5):
+    modified_vec = torch.tensor(np.random.uniform(0, 1, latent_vecs.shape) * noise_scale,
+                                dtype=torch.float32) + latent_vecs
+    return modified_vec
+
+
+def encode(selfies):
+    encoding = gen_tokenizer(selfies, return_tensors='pt', max_length=128, truncation=True, padding='max_length')
+    input_ids = encoding['input_ids']
+    attention_mask = encoding['attention_mask']
+    outputs = gen_model.model.encoder(input_ids=input_ids, attention_mask=attention_mask)
+    model_output = outputs.last_hidden_state
+
+    """input_mask_expanded = attention_mask.unsqueeze(-1).expand(model_output.size()).float()
+    sum_embeddings = torch.sum(model_output * input_mask_expanded, 1)
+    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    model_output = sum_embeddings / sum_mask"""
+    return model_output, attention_mask
+
+
+# Function to generate canonical SMILES and molecule image
+def generate_canonical(smiles):
+    s = sf.encoder(smiles)
+    selfie = s.replace("][", "] [")
+    latent_vec, mask = encode([selfie])
+    gen_mol = None
+    for i in range(5, 51):
+        noise = i / 10
+        perturbed_latent = perturb_latent(latent_vec, noise_scale=noise)
+        gen = generate(perturbed_latent, mask)
+        gen_mol = Chem.MolToSmiles(Chem.MolFromSmiles(gen[0]))
+        if gen_mol != Chem.MolToSmiles(Chem.MolFromSmiles(smiles)): break
+
+    if gen_mol:
+        # Calculate properties for ref and gen molecules
+        ref_properties = calculate_properties(smiles)
+        gen_properties = calculate_properties(gen_mol)
+        tanimoto_similarity = calculate_tanimoto(smiles, gen_mol)
+
+        # Prepare the table with ref mol and gen mol
+        data = {
+            "Property": ["QED", "SA", "LogP", "Mol Wt", "Tanimoto Similarity"],
+            "Reference Mol": [ref_properties[0], ref_properties[1], ref_properties[2], ref_properties[3],
+                              tanimoto_similarity],
+            "Generated Mol": [gen_properties[0], gen_properties[1], gen_properties[2], gen_properties[3], ""]
+        }
+        df = pd.DataFrame(data)
+
+        # Display molecule image of canonical smiles
+        mol_image = smiles_to_image(gen_mol)
+
+        return df, gen_mol, mol_image
+    return "Invalid SMILES", None, None
+
+
+# Function to display evaluation score
+def display_eval(selected_models, dataset, task_type, downstream, fusion_type):
+    result = None
+
+    try:
+        downstream_model = downstream.split("*")[0].lstrip()
+        downstream_model = downstream_model.rstrip()
+        hyp_param = downstream.split("*")[-1].lstrip()
+        hyp_param = hyp_param.rstrip()
+        hyp_param = hyp_param.replace("nan", "float('nan')")
+        params = eval(hyp_param)
+    except:
+        downstream_model = downstream.split("*")[0].lstrip()
+        downstream_model = downstream_model.rstrip()
+        params = None
+
+
+
+
+    try:
+        if not selected_models:
+            return "Please select at least one enabled model."
+
+        if task_type == "Classification":
+            global roc_auc, fpr, tpr, x_batch, y_batch
+        elif task_type == "Regression":
+            global RMSE, y_batch_test, y_prob
+
+        if len(selected_models) > 1:
+            if task_type == "Classification":
+                #result, roc_auc, fpr, tpr, x_batch, y_batch = fm4m.multi_modal(model_list=selected_models,
+                #                                                               downstream_model="XGBClassifier",
+                #                                                               dataset=dataset.lower())
+                if downstream_model == "Default Settings":
+                    downstream_model = "DefaultClassifier"
+                    params = None
+                result, roc_auc, fpr, tpr, x_batch, y_batch = fm4m.multi_modal(model_list=selected_models,
+                                                                                               downstream_model=downstream_model,
+                                                                                               params = params,
+                                                                                               dataset=dataset)
+
+            elif task_type == "Regression":
+                #result, RMSE, y_batch_test, y_prob = fm4m.multi_modal(model_list=selected_models,
+                #                                                      downstream_model="XGBRegressor",
+                #                                                      dataset=dataset.lower())
+
+                if downstream_model == "Default Settings":
+                    downstream_model = "DefaultRegressor"
+                    params = None
+
+                result, RMSE, y_batch_test, y_prob, x_batch, y_batch = fm4m.multi_modal(model_list=selected_models,
+                                                                      downstream_model=downstream_model,
+                                                                      params=params,
+                                                                      dataset=dataset)
+
+        else:
+            if task_type == "Classification":
+                #result, roc_auc, fpr, tpr, x_batch, y_batch = fm4m.single_modal(model=selected_models[0],
+                #                                                                downstream_model="XGBClassifier",
+                #                                                                dataset=dataset.lower())
+                if downstream_model == "Default Settings":
+                    downstream_model = "DefaultClassifier"
+                    params = None
+
+                result, roc_auc, fpr, tpr, x_batch, y_batch = fm4m.single_modal(model=selected_models[0],
+                                                                                downstream_model=downstream_model,
+                                                                                params=params,
+                                                                                dataset=dataset)
+
+            elif task_type == "Regression":
+                #result, RMSE, y_batch_test, y_prob = fm4m.single_modal(model=selected_models[0],
+                #                                                       downstream_model="XGBRegressor",
+                #                                                       dataset=dataset.lower())
+
+                if downstream_model == "Default Settings":
+                    downstream_model = "DefaultRegressor"
+                    params = None
+
+                result, RMSE, y_batch_test, y_prob, x_batch, y_batch = fm4m.single_modal(model=selected_models[0],
+                                                                       downstream_model=downstream_model,
+                                                                       params=params,
+                                                                       dataset=dataset)
+
+        if result == None:
+            result = "Data & Model Setting is incorrect"
+    except Exception as e:
+        return f"An error occurred: {e}"
+    return f"{result}"
+
+
+# Function to handle plot display
+def display_plot(plot_type):
+    fig, ax = plt.subplots()
+
+    if plot_type == "Latent Space":
+        global x_batch, y_batch
+        ax.set_title("T-SNE Plot")
+        # reducer = umap.UMAP(metric='euclidean', n_neighbors=  10, n_components=2, low_memory=True, min_dist=0.1, verbose=False)
+        # features_umap = reducer.fit_transform(x_batch[:500])
+        # x = y_batch.values[:500]
+        # index_0 = [index for index in range(len(x)) if x[index] == 0]
+        # index_1 = [index for index in range(len(x)) if x[index] == 1]
+        class_0 = x_batch  # features_umap[index_0]
+        class_1 = y_batch  # features_umap[index_1]
+
+        """with open("latent_multi_bace.pkl", "rb") as f:
+            class_0, class_1 = pickle.load(f)
+        """
+        plt.scatter(class_1[:, 0], class_1[:, 1], c='red', label='Class 1')
+        plt.scatter(class_0[:, 0], class_0[:, 1], c='blue', label='Class 0')
+
+        ax.set_xlabel('Feature 1')
+        ax.set_ylabel('Feature 2')
+        ax.set_title('Dataset Distribution')
+
+    elif plot_type == "ROC-AUC":
+        global roc_auc, fpr, tpr
+        ax.set_title("ROC-AUC Curve")
+        try:
+            ax.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.4f})')
+            ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+            ax.set_xlim([0.0, 1.0])
+            ax.set_ylim([0.0, 1.05])
+        except:
+            pass
+        ax.set_xlabel('False Positive Rate')
+        ax.set_ylabel('True Positive Rate')
+        ax.set_title('Receiver Operating Characteristic')
+        ax.legend(loc='lower right')
+
+    elif plot_type == "Parity Plot":
+        global RMSE, y_batch_test, y_prob
+        ax.set_title("Parity plot")
+
+        # change format
+        try:
+            print(y_batch_test)
+            print(y_prob)
+            y_batch_test = np.array(y_batch_test, dtype=float)
+            y_prob = np.array(y_prob, dtype=float)
+            ax.scatter(y_batch_test, y_prob, color="blue", label=f"Predicted vs Actual (RMSE: {RMSE:.4f})")
+            min_val = min(min(y_batch_test), min(y_prob))
+            max_val = max(max(y_batch_test), max(y_prob))
+            ax.plot([min_val, max_val], [min_val, max_val], 'r-')
+
+        except:
+
+            y_batch_test = []
+            y_prob = []
+            RMSE = None
+            print(y_batch_test)
+            print(y_prob)
+
+
+
+
+
+        ax.set_xlabel('Actual Values')
+        ax.set_ylabel('Predicted Values')
+
+        ax.legend(loc='lower right')
+    return fig
+
+
+# Predefined dataset paths (these should be adjusted to your file paths)
+predefined_datasets = {
+    "BACE": f"./data/bace/train.csv, ./data/bace/test.csv, smiles, Class",
+    "ESOL": f"./data/esol/train.csv, ./data/esol/test.csv, smiles, prop",
+}
+
+
+# Function to load a predefined dataset from the local path
+def load_predefined_dataset(dataset_name):
+    val = predefined_datasets.get(dataset_name)
+    try: file_path = val.split(",")[0]
+    except:file_path=False
+
+    if file_path:
+        df = pd.read_csv(file_path)
+        return df.head(), gr.update(choices=list(df.columns)), gr.update(choices=list(df.columns)), f"{dataset_name.lower()}"
+    return pd.DataFrame(), gr.update(choices=[]), gr.update(choices=[]), f"Dataset not found"
+
+
+# Function to display the head of the uploaded CSV file
+def display_csv_head(file):
+    if file is not None:
+        # Load the CSV file into a DataFrame
+        df = pd.read_csv(file.name)
+        return df.head(), gr.update(choices=list(df.columns)), gr.update(choices=list(df.columns))
+    return pd.DataFrame(), gr.update(choices=[]), gr.update(choices=[])
+
+
+# Function to handle dataset selection (predefined or custom)
+def handle_dataset_selection(selected_dataset):
+    if selected_dataset == "Custom Dataset":
+        # Show file upload fields for train and test datasets if "Custom Dataset" is selected
+        return gr.update(visible=True), gr.update(visible=True),  gr.update(visible=True), gr.update(visible=True), gr.update(
+            visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)
+    else:
+        #[dataset_name, train_file, train_display, test_file, test_display, predefined_display,
+        # input_column_selector, output_column_selector]
+
+
+
+        # Load the predefined dataset from its local path
+        #return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(
+        #    visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+        #return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(
+        #    visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(
+            visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+
+
+# Function to select input and output columns and display a message
+def select_columns(input_column, output_column, train_data, test_data,dataset_name):
+    if input_column and output_column:
+        return f"{train_data.name},{test_data.name},{input_column},{output_column},{dataset_name}"
+    return "Please select both input and output columns."
+
+def set_dataname(dataset_name, dataset_selector ):
+    if dataset_selector == "Custom Dataset":
+        return f"{dataset_name}"
+    return f"{dataset_selector}"
+
+# Function to create model based on user input
+def create_model(model_name, max_depth=None, n_estimators=None, alpha=None, degree=None, kernel=None):
+    if model_name == "XGBClassifier":
+        model = xgb.XGBClassifier(objective='binary:logistic',eval_metric= 'auc', max_depth=max_depth, n_estimators=n_estimators, alpha=alpha)
+    elif model_name == "SVR":
+        model = SVR(degree=degree, kernel=kernel)
+    elif model_name == "Kernel Ridge":
+        model = KernelRidge(alpha=alpha, degree=degree, kernel=kernel)
+    elif model_name == "Linear Regression":
+        model = LinearRegression()
+    elif model_name == "Default - Auto":
+        model = "Default Settings"
+        return f"{model}"
+    else:
+        return "Model not supported."
+
+    return f"{model_name} * {model.get_params()}"
+def model_selector(model_name):
+    # Dynamically return the appropriate hyperparameter components based on the selected model
+    if model_name == "XGBClassifier":
+        return (
+            gr.Slider(1, 10, label="max_depth"),
+            gr.Slider(50, 500, label="n_estimators"),
+            gr.Slider(0.1, 10.0, step=0.1, label="alpha")
+        )
+    elif model_name == "SVR":
+        return (
+            gr.Slider(1, 5, label="degree"),
+            gr.Dropdown(["rbf", "poly", "linear"], label="kernel")
+        )
+    elif model_name == "Kernel Ridge":
+        return (
+            gr.Slider(0.1, 10.0, step=0.1, label="alpha"),
+            gr.Slider(1, 5, label="degree"),
+            gr.Dropdown(["rbf", "poly", "linear"], label="kernel")
+        )
+    elif model_name == "Linear Regression":
+        return ()  # No hyperparameters for Linear Regression
+    else:
+        return ()
+
+
+
+# Define the Gradio layout
+# with gr.Blocks(theme=my_theme) as demo:
+with gr.Blocks() as demo:
+    with gr.Row():
+        # Left Column
+        with gr.Column():
+            gr.HTML('''
+           <div style="background-color: #6A8EAE; color: #FFFFFF; padding: 10px;">
+                <h3 style="color: #FFFFFF; margin: 0;font-size: 20px;"> Data & Model Setting</h3>
+            </div>
+            ''')
+            # gr.Markdown("## Data & Model Setting")
+            #dataset_dropdown = gr.Dropdown(choices=datasets, label="Select Dat")
+
+            # Dropdown menu for predefined datasets including "Custom Dataset" option
+            dataset_selector = gr.Dropdown(label="Select Dataset",
+                                           choices=list(predefined_datasets.keys()) + ["Custom Dataset"])
+            # Display the message for selected columns
+            selected_columns_message = gr.Textbox(label="Selected Columns Info", visible=False)
+
+            with gr.Accordion("Dataset Settings", open=True):
+                # File upload options for custom dataset (train and test)
+                dataset_name = gr.Textbox(label="Dataset Name", visible=False)
+                train_file = gr.File(label="Upload Custom Train Dataset", file_types=[".csv"], visible=False)
+                train_display = gr.Dataframe(label="Train Dataset Preview (First 5 Rows)", visible=False, interactive=False)
+
+                test_file = gr.File(label="Upload Custom Test Dataset", file_types=[".csv"], visible=False)
+                test_display = gr.Dataframe(label="Test Dataset Preview (First 5 Rows)", visible=False, interactive=False)
+
+                # Predefined dataset displays
+                predefined_display = gr.Dataframe(label="Predefined Dataset Preview (First 5 Rows)", visible=False,
+                                                  interactive=False)
+
+
+
+                # Dropdowns for selecting input and output columns for the custom dataset
+                input_column_selector = gr.Dropdown(label="Select Input Column", choices=[], visible=False)
+                output_column_selector = gr.Dropdown(label="Select Output Column", choices=[], visible=False)
+
+                #selected_columns_message = gr.Textbox(label="Selected Columns Info", visible=True)
+
+                # When a dataset is selected, show either file upload fields (for custom) or load predefined datasets
+                dataset_selector.change(handle_dataset_selection,
+                                        inputs=dataset_selector,
+                                        outputs=[dataset_name, train_file, train_display, test_file, test_display, predefined_display,
+                                                 input_column_selector, output_column_selector])
+
+                # When a predefined dataset is selected, load its head and update column selectors
+                dataset_selector.change(load_predefined_dataset,
+                                        inputs=dataset_selector,
+                                        outputs=[predefined_display, input_column_selector, output_column_selector, selected_columns_message])
+
+                # When a custom train file is uploaded, display its head and update column selectors
+                train_file.change(display_csv_head, inputs=train_file,
+                                  outputs=[train_display, input_column_selector, output_column_selector])
+
+                # When a custom test file is uploaded, display its head
+                test_file.change(display_csv_head, inputs=test_file,
+                                 outputs=[test_display, input_column_selector, output_column_selector])
+
+                dataset_selector.change(set_dataname,
+                                    inputs=[dataset_name, dataset_selector],
+                                    outputs=dataset_name)
+
+                # Update the selected columns information when dropdown values are changed
+                input_column_selector.change(select_columns,
+                                             inputs=[input_column_selector, output_column_selector, train_file, test_file, dataset_name],
+                                             outputs=selected_columns_message)
+
+                output_column_selector.change(select_columns,
+                                              inputs=[input_column_selector, output_column_selector, train_file, test_file, dataset_name],
+                                              outputs=selected_columns_message)
+
+            model_checkbox = gr.CheckboxGroup(choices=models_enabled, label="Select Model")
+
+            # Add disabled checkboxes for GNN and FNN
+            # gnn_checkbox = gr.Checkbox(label="GNN (Disabled)", value=False, interactive=False)
+            # fnn_checkbox = gr.Checkbox(label="FNN (Disabled)", value=False, interactive=False)
+
+            task_radiobutton = gr.Radio(choices=["Classification", "Regression"], label="Task Type")
+
+            ####### adding hyper parameter tuning ###########
+            model_name = gr.Dropdown(["Default - Auto", "XGBClassifier", "SVR", "Kernel Ridge", "Linear Regression"], label="Select Downstream Model")
+            with gr.Accordion("Downstream Hyperparameter Settings", open=True):
+                # Create placeholders for hyperparameter components
+                max_depth = gr.Slider(1, 20, step=1,visible=False, label="max_depth")
+                n_estimators = gr.Slider(100, 5000, step=100, visible=False, label="n_estimators")
+                alpha = gr.Slider(0.1, 10.0, step=0.1, visible=False, label="alpha")
+                degree = gr.Slider(1, 20, step=1,visible=False, label="degree")
+                kernel = gr.Dropdown(choices=["rbf", "poly", "linear"], visible=False, label="kernel")
+
+                # Output textbox
+                output = gr.Textbox(label="Loaded Parameters")
+
+
+            # Dynamically show relevant hyperparameters based on selected model
+            def update_hyperparameters(model_name):
+                if model_name == "XGBClassifier":
+                    return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(
+                        visible=False), gr.update(visible=False)
+                elif model_name == "SVR":
+                    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(
+                        visible=True), gr.update(visible=True)
+                elif model_name == "Kernel Ridge":
+                    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(
+                        visible=True), gr.update(visible=True)
+                elif model_name == "Linear Regression":
+                    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(
+                        visible=False), gr.update(visible=False)
+                elif model_name == "Default - Auto":
+                    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(
+                        visible=False), gr.update(visible=False)
+
+
+            # When model is selected, update which hyperparameters are visible
+            model_name.change(update_hyperparameters, inputs=[model_name],
+                              outputs=[max_depth, n_estimators, alpha, degree, kernel])
+
+            # Submit button to create the model with selected hyperparameters
+            submit_button = gr.Button("Create Downstream Model")
+
+
+            # Function to handle model creation based on input parameters
+            def on_submit(model_name, max_depth, n_estimators, alpha, degree, kernel):
+                if model_name == "XGBClassifier":
+                    return create_model(model_name, max_depth=max_depth, n_estimators=n_estimators, alpha=alpha)
+                elif model_name == "SVR":
+                    return create_model(model_name, degree=degree, kernel=kernel)
+                elif model_name == "Kernel Ridge":
+                    return create_model(model_name, alpha=alpha, degree=degree, kernel=kernel)
+                elif model_name == "Linear Regression":
+                    return create_model(model_name)
+                elif model_name == "Default - Auto":
+                    return create_model(model_name)
+
+            # When the submit button is clicked, run the on_submit function
+            submit_button.click(on_submit, inputs=[model_name, max_depth, n_estimators, alpha, degree, kernel],
+                                outputs=output)
+            ###### End of hyper param tuning #########
+
+            fusion_radiobutton = gr.Radio(choices=fusion_available, label="Fusion Type")
+
+
+
+            eval_button = gr.Button("Train downstream model")
+            #eval_button.style(css_class="custom-button-left")
+
+        # Middle Column
+        with gr.Column():
+            gr.HTML('''
+           <div style="background-color: #8F9779; color: #FFFFFF; padding: 10px;">
+                <h3 style="color: #FFFFFF; margin: 0;font-size: 20px;"> Downstream Task 1: Property Prediction</h3>
+            </div>
+            ''')
+            # gr.Markdown("## Downstream task Result")
+            eval_output = gr.Textbox(label="Train downstream model")
+
+            plot_radio = gr.Radio(choices=["ROC-AUC", "Parity Plot", "Latent Space"], label="Select Plot Type")
+            plot_output = gr.Plot(label="Visualization")#, height=250, width=250)
+
+            #download_rep = gr.Button("Download representation")
+
+            create_log = gr.Button("Store log")
+
+            log_table = gr.Dataframe(value=log_df, label="Log of Selections and Results", interactive=False)
+
+            eval_button.click(display_eval,
+                              inputs=[model_checkbox, selected_columns_message, task_radiobutton, output, fusion_radiobutton],
+                              outputs=eval_output)
+
+            plot_radio.change(display_plot, inputs=plot_radio, outputs=plot_output)
+
+
+            # Function to gather selected models
+            def gather_selected_models(*models):
+                selected = [model for model in models if model]
+                return selected
+
+
+            create_log.click(evaluate_and_log, inputs=[model_checkbox, dataset_name, task_radiobutton, eval_output],
+                             outputs=log_table)
+            #download_rep.click(save_rep, inputs=[model_checkbox, dataset_name, task_radiobutton, eval_output],
+            #                 outputs=None)
+
+        # Right Column
+        with gr.Column():
+            gr.HTML('''
+           <div style="background-color: #D2B48C; color: #FFFFFF; padding: 10px;">
+                <h3 style="color: #FFFFFF; margin: 0;font-size: 20px;"> Downstream Task 2: Molecule Generation</h3>
+            </div>
+            ''')
+            # gr.Markdown("## Molecular Generation")
+            smiles_input = gr.Textbox(label="Input SMILES String")
+            image_display = gr.Image(label="Molecule Image", height=250, width=250)
+            # Show images for selection
+            with gr.Accordion("Select from sample molecules", open=False):
+                image_selector = gr.Radio(
+                    choices=list(smiles_image_mapping.keys()),
+                    label="Select from sample molecules",
+                    value=None,
+                    #item_images=[load_image(smiles_image_mapping[key]["image"]) for key in smiles_image_mapping.keys()]
+                )
+                image_selector.change(load_image, image_selector, image_display)
+            generate_button = gr.Button("Generate")
+            gen_image_display = gr.Image(label="Generated Molecule Image", height=250, width=250)
+            generated_output = gr.Textbox(label="Generated Output")
+            property_table = gr.Dataframe(label="Molecular Properties Comparison")
+
+
+
+            # Handle image selection
+            image_selector.change(handle_image_selection, inputs=image_selector, outputs=[smiles_input, image_display])
+            smiles_input.change(smiles_to_image, inputs=smiles_input, outputs=image_display)
+
+            # Generate button to display canonical SMILES and molecule image
+            generate_button.click(generate_canonical, inputs=smiles_input,
+                                  outputs=[property_table, generated_output, gen_image_display])
+
+
+if __name__ == "__main__":
+    demo.launch(share=True)
diff --git a/data/.DS_Store b/data/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..78d8eceaef3d7ed8d4f8e591a142681709abd9be
Binary files /dev/null and b/data/.DS_Store differ
diff --git a/data/bace/test.csv b/data/bace/test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..72844be802bc4a039882ec57181873af407a9a92
--- /dev/null
+++ b/data/bace/test.csv
@@ -0,0 +1,153 @@
+smiles,CID,Class,,pIC50,MW,AlogP,HBA,HBD,RB,HeavyAtomCount,ChiralCenterCount,ChiralCenterCountAllPossible,RingCount,PSA,Estate,MR,Polar,sLi_Key,ssBe_Key,ssssBem_Key,sBH2_Key,ssBH_Key,sssB_Key,ssssBm_Key,sCH3_Key,dCH2_Key,ssCH2_Key,tCH_Key,dsCH_Key,aaCH_Key,sssCH_Key,ddC_Key,tsC_Key,dssC_Key,aasC_Key,aaaC_Key,ssssC_Key,sNH3_Key,sNH2_Key,ssNH2_Key,dNH_Key,ssNH_Key,aaNH_Key,tN_Key,sssNH_Key,dsN_Key,aaN_Key,sssN_Key,ddsN_Key,aasN_Key,ssssN_Key,daaN_Key,sOH_Key,dO_Key,ssO_Key,aaO_Key,aOm_Key,sOm_Key,sF_Key,sSiH3_Key,ssSiH2_Key,sssSiH_Key,ssssSi_Key,sPH2_Key,ssPH_Key,sssP_Key,dsssP_Key,ddsP_Key,sssssP_Key,sSH_Key,dS_Key,ssS_Key,aaS_Key,dssS_Key,ddssS_Key,ssssssS_Key,Sm_Key,sCl_Key,sGeH3_Key,ssGeH2_Key,sssGeH_Key,ssssGe_Key,sAsH2_Key,ssAsH_Key,sssAs_Key,dsssAs_Key,ddsAs_Key,sssssAs_Key,sSeH_Key,dSe_Key,ssSe_Key,aaSe_Key,dssSe_Key,ssssssSe_Key,ddssSe_Key,sBr_Key,sSnH3_Key,ssSnH2_Key,sssSnH_Key,ssssSn_Key,sI_Key,sPbH3_Key,ssPbH2_Key,sssPbH_Key,ssssPb_Key,sLi_Cnt,ssBe_Cnt,ssssBem_Cnt,sBH2_Cnt,ssBH_Cnt,sssB_Cnt,ssssBm_Cnt,sCH3_Cnt,dCH2_Cnt,ssCH2_Cnt,tCH_Cnt,dsCH_Cnt,aaCH_Cnt,sssCH_Cnt,ddC_Cnt,tsC_Cnt,dssC_Cnt,aasC_Cnt,aaaC_Cnt,ssssC_Cnt,sNH3_Cnt,sNH2_Cnt,ssNH2_Cnt,dNH_Cnt,ssNH_Cnt,aaNH_Cnt,tN_Cnt,sssNH_Cnt,dsN_Cnt,aaN_Cnt,sssN_Cnt,ddsN_Cnt,aasN_Cnt,ssssN_Cnt,daaN_Cnt,sOH_Cnt,dO_Cnt,ssO_Cnt,aaO_Cnt,aOm_Cnt,sOm_Cnt,sF_Cnt,sSiH3_Cnt,ssSiH2_Cnt,sssSiH_Cnt,ssssSi_Cnt,sPH2_Cnt,ssPH_Cnt,sssP_Cnt,dsssP_Cnt,ddsP_Cnt,sssssP_Cnt,sSH_Cnt,dS_Cnt,ssS_Cnt,aaS_Cnt,dssS_Cnt,ddssS_Cnt,ssssssS_Cnt,Sm_Cnt,sCl_Cnt,sGeH3_Cnt,ssGeH2_Cnt,sssGeH_Cnt,ssssGe_Cnt,sAsH2_Cnt,ssAsH_Cnt,sssAs_Cnt,dsssAs_Cnt,ddsAs_Cnt,sssssAs_Cnt,sSeH_Cnt,dSe_Cnt,ssSe_Cnt,aaSe_Cnt,dssSe_Cnt,ssssssSe_Cnt,ddssSe_Cnt,sBr_Cnt,sSnH3_Cnt,ssSnH2_Cnt,sssSnH_Cnt,ssssSn_Cnt,sI_Cnt,sPbH3_Cnt,ssPbH2_Cnt,sssPbH_Cnt,ssssPb_Cnt,sLi_Sum,ssBe_Sum,ssssBem_Sum,sBH2_Sum,ssBH_Sum,sssB_Sum,ssssBm_Sum,sCH3_Sum,dCH2_Sum,ssCH2_Sum,tCH_Sum,dsCH_Sum,aaCH_Sum,sssCH_Sum,ddC_Sum,tsC_Sum,dssC_Sum,aasC_Sum,aaaC_Sum,ssssC_Sum,sNH3_Sum,sNH2_Sum,ssNH2_Sum,dNH_Sum,ssNH_Sum,aaNH_Sum,tN_Sum,sssNH_Sum,dsN_Sum,aaN_Sum,sssN_Sum,ddsN_Sum,aasN_Sum,ssssN_Sum,daaN_Sum,sOH_Sum,dO_Sum,ssO_Sum,aaO_Sum,aOm_Sum,sOm_Sum,sF_Sum,sSiH3_Sum,ssSiH2_Sum,sssSiH_Sum,ssssSi_Sum,sPH2_Sum,ssPH_Sum,sssP_Sum,dsssP_Sum,ddsP_Sum,sssssP_Sum,sSH_Sum,dS_Sum,ssS_Sum,aaS_Sum,dssS_Sum,ddssS_Sum,ssssssS_Sum,Sm_Sum,sCl_Sum,sGeH3_Sum,ssGeH2_Sum,sssGeH_Sum,ssssGe_Sum,sAsH2_Sum,ssAsH_Sum,sssAs_Sum,dsssAs_Sum,ddsAs_Sum,sssssAs_Sum,sSeH_Sum,dSe_Sum,ssSe_Sum,aaSe_Sum,dssSe_Sum,ssssssSe_Sum,ddssSe_Sum,sBr_Sum,sSnH3_Sum,ssSnH2_Sum,sssSnH_Sum,ssssSn_Sum,sI_Sum,sPbH3_Sum,ssPbH2_Sum,sssPbH_Sum,ssssPb_Sum,sLi_Avg,ssBe_Avg,ssssBem_Avg,sBH2_Avg,ssBH_Avg,sssB_Avg,ssssBm_Avg,sCH3_Avg,dCH2_Avg,ssCH2_Avg,tCH_Avg,dsCH_Avg,aaCH_Avg,sssCH_Avg,ddC_Avg,tsC_Avg,dssC_Avg,aasC_Avg,aaaC_Avg,ssssC_Avg,sNH3_Avg,sNH2_Avg,ssNH2_Avg,dNH_Avg,ssNH_Avg,aaNH_Avg,tN_Avg,sssNH_Avg,dsN_Avg,aaN_Avg,sssN_Avg,ddsN_Avg,aasN_Avg,ssssN_Avg,daaN_Avg,sOH_Avg,dO_Avg,ssO_Avg,aaO_Avg,aOm_Avg,sOm_Avg,sF_Avg,sSiH3_Avg,ssSiH2_Avg,sssSiH_Avg,ssssSi_Avg,sPH2_Avg,ssPH_Avg,sssP_Avg,dsssP_Avg,ddsP_Avg,sssssP_Avg,sSH_Avg,dS_Avg,ssS_Avg,aaS_Avg,dssS_Avg,ddssS_Avg,ssssssS_Avg,Sm_Avg,sCl_Avg,sGeH3_Avg,ssGeH2_Avg,sssGeH_Avg,ssssGe_Avg,sAsH2_Avg,ssAsH_Avg,sssAs_Avg,dsssAs_Avg,ddsAs_Avg,sssssAs_Avg,sSeH_Avg,dSe_Avg,ssSe_Avg,aaSe_Avg,dssSe_Avg,ssssssSe_Avg,ddssSe_Avg,sBr_Avg,sSnH3_Avg,ssSnH2_Avg,sssSnH_Avg,ssssSn_Avg,sI_Avg,sPbH3_Avg,ssPbH2_Avg,sssPbH_Avg,ssssPb_Avg,First Zagreb (ZM1),First Zagreb index by valence vertex degrees (ZM1V),Second Zagreb (ZM2),Second Zagreb index by valence vertex degrees (ZM2V),Polarity (Pol),Narumi Simple Topological (NST),Narumi Harmonic Topological (NHT),Narumi Geometric Topological (NGT),Total structure connectivity (TSC),Wiener (W),Mean Wiener (MW),Xu (Xu),Quadratic (QIndex),Radial centric (RC),Mean Square Distance Balaban (MSDB),Superpendentic (SP),Harary (Har),Log of product of row sums (LPRS),Pogliani (Pog),Schultz Molecular Topological (SMT),Schultz Molecular Topological by valence vertex degrees (SMTV),Mean Distance Degree Deviation (MDDD),Ramification (Ram),Gutman Molecular Topological (GMT),Gutman MTI by valence vertex degrees (GMTV),Average vertex distance degree (AVDD),Unipolarity (UP),Centralization (CENT),Variation (VAR),Molecular electrotopological variation (MEV),Maximal electrotopological positive variation (MEPV),Maximal electrotopological negative variation (MENV),Eccentric connectivity (ECCc),Eccentricity (ECC),Average eccentricity (AECC),Eccentric (DECC),Valence connectivity index chi-0 (vX0),Valence connectivity index chi-1 (vX1),Valence connectivity index chi-2 (vX2),Valence connectivity index chi-3 (vX3),Valence connectivity index chi-4 (vX4),Valence connectivity index chi-5 (vX5),Average valence connectivity index chi-0 (AvX0),Average valence connectivity index chi-1 (AvX1),Average valence connectivity index chi-2 (AvX2),Average valence connectivity index chi-3 (AvX3),Average valence connectivity index chi-4 (AvX4),Average valence connectivity index chi-5 (AvX5),Quasi Wiener (QW),First Mohar (FM),Second Mohar (SM),Spanning tree number (STN),Kier benzene-likeliness index (KBLI),Topological charge index of order 1 (TCI1),Topological charge index of order 2 (TCI2),Topological charge index of order 3 (TCI3),Topological charge index of order 4 (TCI4),Topological charge index of order 5 (TCI5),Topological charge index of order 6 (TCI6),Topological charge index of order 7 (TCI7),Topological charge index of order 8 (TCI8),Topological charge index of order 9 (TCI9),Topological charge index of order 10 (TCI10),Mean topological charge index of order 1 (MTCI1),Mean topological charge index of order 2 (MTCI2),Mean topological charge index of order 3 (MTCI3),Mean topological charge index of order 4 (MTCI4),Mean topological charge index of order 5 (MTCI5),Mean topological charge index of order 6 (MTCI6),Mean topological charge index of order 7 (MTCI7),Mean topological charge index of order 8 (MTCI8),Mean topological charge index of order 9 (MTCI9),Mean topological charge index of order 10 (MTCI10),Global topological charge (GTC),Hyper-distance-path index (HDPI),Reciprocal hyper-distance-path index (RHDPI),Square reciprocal distance sum (SRDS),Modified Randic connectivity (MRC),Balaban centric (BC),Lopping centric (LC),Kier Hall electronegativity (KHE),Sum of topological distances between N..N (STD(N N)),Sum of topological distances between N..O (STD(N O)),Sum of topological distances between N..S (STD(N S)),Sum of topological distances between N..P (STD(N P)),Sum of topological distances between N..F (STD(N F)),Sum of topological distances between N..Cl (STD(N Cl)),Sum of topological distances between N..Br (STD(N Br)),Sum of topological distances between N..I (STD(N I)),Sum of topological distances between O..O (STD(O O)),Sum of topological distances between O..S (STD(O S)),Sum of topological distances between O..P (STD(O P)),Sum of topological distances between O..F (STD(O F)),Sum of topological distances between O..Cl (STD(O Cl)),Sum of topological distances between O..Br (STD(O Br)),Sum of topological distances between O..I (STD(O I)),Sum of topological distances between S..S (STD(S S)),Sum of topological distances between S..P (STD(S P)),Sum of topological distances between S..F (STD(S F)),Sum of topological distances between S..Cl (STD(S Cl)),Sum of topological distances between S..Br (STD(S Br)),Sum of topological distances between S..I (STD(S I)),Sum of topological distances between P..P (STD(P P)),Sum of topological distances between P..F (STD(P F)),Sum of topological distances between P..Cl (STD(P Cl)),Sum of topological distances between P..Br (STD(P Br)),Sum of topological distances between P..I (STD(P I)),Sum of topological distances between F..F (STD(F F)),Sum of topological distances between F..Cl (STD(F Cl)),Sum of topological distances between F..Br (STD(F Br)),Sum of topological distances between F..I (STD(F I)),Sum of topological distances between Cl..Cl (STD(Cl Cl)),Sum of topological distances between Cl..Br (STD(Cl Br)),Sum of topological distances between Cl..I (STD(Cl I)),Sum of topological distances between Br..Br (STD(Br Br)),Sum of topological distances between Br..I (STD(Br I)),Sum of topological distances between I..I (STD(I I)),Wiener-type index from Z weighted distance matrix - Barysz matrix (WhetZ),Wiener-type index from electronegativity weighted distance matrix (Whete),Wiener-type index from mass weighted distance matrix (Whetm),Wiener-type index from van der waals weighted distance matrix (Whetv),Wiener-type index from polarizability weighted distance matrix (Whetp),Balaban-type index from Z weighted distance matrix - Barysz matrix (JhetZ),Balaban-type index from electronegativity weighted distance matrix (Jhete),Balaban-type index from mass weighted distance matrix (Jhetm),Balaban-type index from van der waals weighted distance matrix (Jhetv),Balaban-type index from polarizability weighted distance matrix (Jhetp),Topological diameter (TD),Topological radius (TR),Petitjean 2D shape (PJ2DS),Balaban distance connectivity index (J),Solvation connectivity index chi-0 (SCIX0),Solvation connectivity index chi-1 (SCIX1),Solvation connectivity index chi-2 (SCIX2),Solvation connectivity index chi-3 (SCIX3),Solvation connectivity index chi-4 (SCIX4),Solvation connectivity index chi-5 (SCIX5),Connectivity index chi-0 (CIX0),Connectivity chi-1 [Randic connectivity] (CIX1),Connectivity index chi-2 (CIX2),Connectivity index chi-3 (CIX3),Connectivity index chi-4 (CIX4),Connectivity index chi-5 (CIX5),Average connectivity index chi-0 (ACIX0),Average connectivity index chi-1 (ACIX1),Average connectivity index chi-2 (ACIX2),Average connectivity index chi-3 (ACIX3),Average connectivity index chi-4 (ACIX4),Average connectivity index chi-5 (ACIX5),reciprocal distance Randic-type index (RDR),reciprocal distance square Randic-type index (RDSR),1-path Kier alpha-modified shape index (KAMS1),2-path Kier alpha-modified shape index (KAMS2),3-path Kier alpha-modified shape index (KAMS3),Kier flexibility (KF),path/walk 2 - Randic shape index (RSIpw2),path/walk 3 - Randic shape index (RSIpw3),path/walk 4 - Randic shape index (RSIpw4),path/walk 5 - Randic shape index (RSIpw5),E-state topological parameter (ETP),Ring Count 3 (RNGCNT3),Ring Count 4 (RNGCNT4),Ring Count 5 (RNGCNT5),Ring Count 6 (RNGCNT6),Ring Count 7 (RNGCNT7),Ring Count 8 (RNGCNT8),Ring Count 9 (RNGCNT9),Ring Count 10 (RNGCNT10),Ring Count 11 (RNGCNT11),Ring Count 12 (RNGCNT12),Ring Count 13 (RNGCNT13),Ring Count 14 (RNGCNT14),Ring Count 15 (RNGCNT15),Ring Count 16 (RNGCNT16),Ring Count 17 (RNGCNT17),Ring Count 18 (RNGCNT18),Ring Count 19 (RNGCNT19),Ring Count 20 (RNGCNT20),Atom Count (ATMCNT),Bond Count (BNDCNT),Atoms in Ring System (ATMRNGCNT),Bonds in Ring System (BNDRNGCNT),Cyclomatic number (CYCLONUM),Number of ring systems (NRS),Normalized number of ring systems (NNRS),Ring Fusion degree (RFD),Ring perimeter (RNGPERM),Ring bridge count (RNGBDGE),Molecule cyclized degree (MCD),Ring Fusion density (RFDELTA),Ring complexity index (RCI),Van der Waals surface area (VSA),MR1 (MR1),MR2 (MR2),MR3 (MR3),MR4 (MR4),MR5 (MR5),MR6 (MR6),MR7 (MR7),MR8 (MR8),ALOGP1 (ALOGP1),ALOGP2 (ALOGP2),ALOGP3 (ALOGP3),ALOGP4 (ALOGP4),ALOGP5 (ALOGP5),ALOGP6 (ALOGP6),ALOGP7 (ALOGP7),ALOGP8 (ALOGP8),ALOGP9 (ALOGP9),ALOGP10 (ALOGP10),PEOE1 (PEOE1),PEOE2 (PEOE2),PEOE3 (PEOE3),PEOE4 (PEOE4),PEOE5 (PEOE5),PEOE6 (PEOE6),PEOE7 (PEOE7),PEOE8 (PEOE8),PEOE9 (PEOE9),PEOE10 (PEOE10),PEOE11 (PEOE11),PEOE12 (PEOE12),PEOE13 (PEOE13),PEOE14 (PEOE14),canvasUID
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diff --git a/data/bace/train.csv b/data/bace/train.csv
new file mode 100644
index 0000000000000000000000000000000000000000..bff085503c518d59e55eae129ab2d93c6594cd98
--- /dev/null
+++ b/data/bace/train.csv
@@ -0,0 +1,1210 @@
+smiles,CID,Class,,pIC50,MW,AlogP,HBA,HBD,RB,HeavyAtomCount,ChiralCenterCount,ChiralCenterCountAllPossible,RingCount,PSA,Estate,MR,Polar,sLi_Key,ssBe_Key,ssssBem_Key,sBH2_Key,ssBH_Key,sssB_Key,ssssBm_Key,sCH3_Key,dCH2_Key,ssCH2_Key,tCH_Key,dsCH_Key,aaCH_Key,sssCH_Key,ddC_Key,tsC_Key,dssC_Key,aasC_Key,aaaC_Key,ssssC_Key,sNH3_Key,sNH2_Key,ssNH2_Key,dNH_Key,ssNH_Key,aaNH_Key,tN_Key,sssNH_Key,dsN_Key,aaN_Key,sssN_Key,ddsN_Key,aasN_Key,ssssN_Key,daaN_Key,sOH_Key,dO_Key,ssO_Key,aaO_Key,aOm_Key,sOm_Key,sF_Key,sSiH3_Key,ssSiH2_Key,sssSiH_Key,ssssSi_Key,sPH2_Key,ssPH_Key,sssP_Key,dsssP_Key,ddsP_Key,sssssP_Key,sSH_Key,dS_Key,ssS_Key,aaS_Key,dssS_Key,ddssS_Key,ssssssS_Key,Sm_Key,sCl_Key,sGeH3_Key,ssGeH2_Key,sssGeH_Key,ssssGe_Key,sAsH2_Key,ssAsH_Key,sssAs_Key,dsssAs_Key,ddsAs_Key,sssssAs_Key,sSeH_Key,dSe_Key,ssSe_Key,aaSe_Key,dssSe_Key,ssssssSe_Key,ddssSe_Key,sBr_Key,sSnH3_Key,ssSnH2_Key,sssSnH_Key,ssssSn_Key,sI_Key,sPbH3_Key,ssPbH2_Key,sssPbH_Key,ssssPb_Key,sLi_Cnt,ssBe_Cnt,ssssBem_Cnt,sBH2_Cnt,ssBH_Cnt,sssB_Cnt,ssssBm_Cnt,sCH3_Cnt,dCH2_Cnt,ssCH2_Cnt,tCH_Cnt,dsCH_Cnt,aaCH_Cnt,sssCH_Cnt,ddC_Cnt,tsC_Cnt,dssC_Cnt,aasC_Cnt,aaaC_Cnt,ssssC_Cnt,sNH3_Cnt,sNH2_Cnt,ssNH2_Cnt,dNH_Cnt,ssNH_Cnt,aaNH_Cnt,tN_Cnt,sssNH_Cnt,dsN_Cnt,aaN_Cnt,sssN_Cnt,ddsN_Cnt,aasN_Cnt,ssssN_Cnt,daaN_Cnt,sOH_Cnt,dO_Cnt,ssO_Cnt,aaO_Cnt,aOm_Cnt,sOm_Cnt,sF_Cnt,sSiH3_Cnt,ssSiH2_Cnt,sssSiH_Cnt,ssssSi_Cnt,sPH2_Cnt,ssPH_Cnt,sssP_Cnt,dsssP_Cnt,ddsP_Cnt,sssssP_Cnt,sSH_Cnt,dS_Cnt,ssS_Cnt,aaS_Cnt,dssS_Cnt,ddssS_Cnt,ssssssS_Cnt,Sm_Cnt,sCl_Cnt,sGeH3_Cnt,ssGeH2_Cnt,sssGeH_Cnt,ssssGe_Cnt,sAsH2_Cnt,ssAsH_Cnt,sssAs_Cnt,dsssAs_Cnt,ddsAs_Cnt,sssssAs_Cnt,sSeH_Cnt,dSe_Cnt,ssSe_Cnt,aaSe_Cnt,dssSe_Cnt,ssssssSe_Cnt,ddssSe_Cnt,sBr_Cnt,sSnH3_Cnt,ssSnH2_Cnt,sssSnH_Cnt,ssssSn_Cnt,sI_Cnt,sPbH3_Cnt,ssPbH2_Cnt,sssPbH_Cnt,ssssPb_Cnt,sLi_Sum,ssBe_Sum,ssssBem_Sum,sBH2_Sum,ssBH_Sum,sssB_Sum,ssssBm_Sum,sCH3_Sum,dCH2_Sum,ssCH2_Sum,tCH_Sum,dsCH_Sum,aaCH_Sum,sssCH_Sum,ddC_Sum,tsC_Sum,dssC_Sum,aasC_Sum,aaaC_Sum,ssssC_Sum,sNH3_Sum,sNH2_Sum,ssNH2_Sum,dNH_Sum,ssNH_Sum,aaNH_Sum,tN_Sum,sssNH_Sum,dsN_Sum,aaN_Sum,sssN_Sum,ddsN_Sum,aasN_Sum,ssssN_Sum,daaN_Sum,sOH_Sum,dO_Sum,ssO_Sum,aaO_Sum,aOm_Sum,sOm_Sum,sF_Sum,sSiH3_Sum,ssSiH2_Sum,sssSiH_Sum,ssssSi_Sum,sPH2_Sum,ssPH_Sum,sssP_Sum,dsssP_Sum,ddsP_Sum,sssssP_Sum,sSH_Sum,dS_Sum,ssS_Sum,aaS_Sum,dssS_Sum,ddssS_Sum,ssssssS_Sum,Sm_Sum,sCl_Sum,sGeH3_Sum,ssGeH2_Sum,sssGeH_Sum,ssssGe_Sum,sAsH2_Sum,ssAsH_Sum,sssAs_Sum,dsssAs_Sum,ddsAs_Sum,sssssAs_Sum,sSeH_Sum,dSe_Sum,ssSe_Sum,aaSe_Sum,dssSe_Sum,ssssssSe_Sum,ddssSe_Sum,sBr_Sum,sSnH3_Sum,ssSnH2_Sum,sssSnH_Sum,ssssSn_Sum,sI_Sum,sPbH3_Sum,ssPbH2_Sum,sssPbH_Sum,ssssPb_Sum,sLi_Avg,ssBe_Avg,ssssBem_Avg,sBH2_Avg,ssBH_Avg,sssB_Avg,ssssBm_Avg,sCH3_Avg,dCH2_Avg,ssCH2_Avg,tCH_Avg,dsCH_Avg,aaCH_Avg,sssCH_Avg,ddC_Avg,tsC_Avg,dssC_Avg,aasC_Avg,aaaC_Avg,ssssC_Avg,sNH3_Avg,sNH2_Avg,ssNH2_Avg,dNH_Avg,ssNH_Avg,aaNH_Avg,tN_Avg,sssNH_Avg,dsN_Avg,aaN_Avg,sssN_Avg,ddsN_Avg,aasN_Avg,ssssN_Avg,daaN_Avg,sOH_Avg,dO_Avg,ssO_Avg,aaO_Avg,aOm_Avg,sOm_Avg,sF_Avg,sSiH3_Avg,ssSiH2_Avg,sssSiH_Avg,ssssSi_Avg,sPH2_Avg,ssPH_Avg,sssP_Avg,dsssP_Avg,ddsP_Avg,sssssP_Avg,sSH_Avg,dS_Avg,ssS_Avg,aaS_Avg,dssS_Avg,ddssS_Avg,ssssssS_Avg,Sm_Avg,sCl_Avg,sGeH3_Avg,ssGeH2_Avg,sssGeH_Avg,ssssGe_Avg,sAsH2_Avg,ssAsH_Avg,sssAs_Avg,dsssAs_Avg,ddsAs_Avg,sssssAs_Avg,sSeH_Avg,dSe_Avg,ssSe_Avg,aaSe_Avg,dssSe_Avg,ssssssSe_Avg,ddssSe_Avg,sBr_Avg,sSnH3_Avg,ssSnH2_Avg,sssSnH_Avg,ssssSn_Avg,sI_Avg,sPbH3_Avg,ssPbH2_Avg,sssPbH_Avg,ssssPb_Avg,First Zagreb (ZM1),First Zagreb index by valence vertex degrees (ZM1V),Second Zagreb (ZM2),Second Zagreb index by valence vertex degrees (ZM2V),Polarity (Pol),Narumi Simple Topological (NST),Narumi Harmonic Topological (NHT),Narumi Geometric Topological (NGT),Total structure connectivity (TSC),Wiener (W),Mean Wiener (MW),Xu (Xu),Quadratic (QIndex),Radial centric (RC),Mean Square Distance Balaban (MSDB),Superpendentic (SP),Harary (Har),Log of product of row sums (LPRS),Pogliani (Pog),Schultz Molecular Topological (SMT),Schultz Molecular Topological by valence vertex degrees (SMTV),Mean Distance Degree Deviation (MDDD),Ramification (Ram),Gutman Molecular Topological (GMT),Gutman MTI by valence vertex degrees (GMTV),Average vertex distance degree (AVDD),Unipolarity (UP),Centralization (CENT),Variation (VAR),Molecular electrotopological variation (MEV),Maximal electrotopological positive variation (MEPV),Maximal electrotopological negative variation (MENV),Eccentric connectivity (ECCc),Eccentricity (ECC),Average eccentricity (AECC),Eccentric (DECC),Valence connectivity index chi-0 (vX0),Valence connectivity index chi-1 (vX1),Valence connectivity index chi-2 (vX2),Valence connectivity index chi-3 (vX3),Valence connectivity index chi-4 (vX4),Valence connectivity index chi-5 (vX5),Average valence connectivity index chi-0 (AvX0),Average valence connectivity index chi-1 (AvX1),Average valence connectivity index chi-2 (AvX2),Average valence connectivity index chi-3 (AvX3),Average valence connectivity index chi-4 (AvX4),Average valence connectivity index chi-5 (AvX5),Quasi Wiener (QW),First Mohar (FM),Second Mohar (SM),Spanning tree number (STN),Kier benzene-likeliness index (KBLI),Topological charge index of order 1 (TCI1),Topological charge index of order 2 (TCI2),Topological charge index of order 3 (TCI3),Topological charge index of order 4 (TCI4),Topological charge index of order 5 (TCI5),Topological charge index of order 6 (TCI6),Topological charge index of order 7 (TCI7),Topological charge index of order 8 (TCI8),Topological charge index of order 9 (TCI9),Topological charge index of order 10 (TCI10),Mean topological charge index of order 1 (MTCI1),Mean topological charge index of order 2 (MTCI2),Mean topological charge index of order 3 (MTCI3),Mean topological charge index of order 4 (MTCI4),Mean topological charge index of order 5 (MTCI5),Mean topological charge index of order 6 (MTCI6),Mean topological charge index of order 7 (MTCI7),Mean topological charge index of order 8 (MTCI8),Mean topological charge index of order 9 (MTCI9),Mean topological charge index of order 10 (MTCI10),Global topological charge (GTC),Hyper-distance-path index (HDPI),Reciprocal hyper-distance-path index (RHDPI),Square reciprocal distance sum (SRDS),Modified Randic connectivity (MRC),Balaban centric (BC),Lopping centric (LC),Kier Hall electronegativity (KHE),Sum of topological distances between N..N (STD(N N)),Sum of topological distances between N..O (STD(N O)),Sum of topological distances between N..S (STD(N S)),Sum of topological distances between N..P (STD(N P)),Sum of topological distances between N..F (STD(N F)),Sum of topological distances between N..Cl (STD(N Cl)),Sum of topological distances between N..Br (STD(N Br)),Sum of topological distances between N..I (STD(N I)),Sum of topological distances between O..O (STD(O O)),Sum of topological distances between O..S (STD(O S)),Sum of topological distances between O..P (STD(O P)),Sum of topological distances between O..F (STD(O F)),Sum of topological distances between O..Cl (STD(O Cl)),Sum of topological distances between O..Br (STD(O Br)),Sum of topological distances between O..I (STD(O I)),Sum of topological distances between S..S (STD(S S)),Sum of topological distances between S..P (STD(S P)),Sum of topological distances between S..F (STD(S F)),Sum of topological distances between S..Cl (STD(S Cl)),Sum of topological distances between S..Br (STD(S Br)),Sum of topological distances between S..I (STD(S I)),Sum of topological distances between P..P (STD(P P)),Sum of topological distances between P..F (STD(P F)),Sum of topological distances between P..Cl (STD(P Cl)),Sum of topological distances between P..Br (STD(P Br)),Sum of topological distances between P..I (STD(P I)),Sum of topological distances between F..F (STD(F F)),Sum of topological distances between F..Cl (STD(F Cl)),Sum of topological distances between F..Br (STD(F Br)),Sum of topological distances between F..I (STD(F I)),Sum of topological distances between Cl..Cl (STD(Cl Cl)),Sum of topological distances between Cl..Br (STD(Cl Br)),Sum of topological distances between Cl..I (STD(Cl I)),Sum of topological distances between Br..Br (STD(Br Br)),Sum of topological distances between Br..I (STD(Br I)),Sum of topological distances between I..I (STD(I I)),Wiener-type index from Z weighted distance matrix - Barysz matrix (WhetZ),Wiener-type index from electronegativity weighted distance matrix (Whete),Wiener-type index from mass weighted distance matrix (Whetm),Wiener-type index from van der waals weighted distance matrix (Whetv),Wiener-type index from polarizability weighted distance matrix (Whetp),Balaban-type index from Z weighted distance matrix - Barysz matrix (JhetZ),Balaban-type index from electronegativity weighted distance matrix (Jhete),Balaban-type index from mass weighted distance matrix (Jhetm),Balaban-type index from van der waals weighted distance matrix (Jhetv),Balaban-type index from polarizability weighted distance matrix (Jhetp),Topological diameter (TD),Topological radius (TR),Petitjean 2D shape (PJ2DS),Balaban distance connectivity index (J),Solvation connectivity index chi-0 (SCIX0),Solvation connectivity index chi-1 (SCIX1),Solvation connectivity index chi-2 (SCIX2),Solvation connectivity index chi-3 (SCIX3),Solvation connectivity index chi-4 (SCIX4),Solvation connectivity index chi-5 (SCIX5),Connectivity index chi-0 (CIX0),Connectivity chi-1 [Randic connectivity] (CIX1),Connectivity index chi-2 (CIX2),Connectivity index chi-3 (CIX3),Connectivity index chi-4 (CIX4),Connectivity index chi-5 (CIX5),Average connectivity index chi-0 (ACIX0),Average connectivity index chi-1 (ACIX1),Average connectivity index chi-2 (ACIX2),Average connectivity index chi-3 (ACIX3),Average connectivity index chi-4 (ACIX4),Average connectivity index chi-5 (ACIX5),reciprocal distance Randic-type index (RDR),reciprocal distance square Randic-type index (RDSR),1-path Kier alpha-modified shape index (KAMS1),2-path Kier alpha-modified shape index (KAMS2),3-path Kier alpha-modified shape index (KAMS3),Kier flexibility (KF),path/walk 2 - Randic shape index (RSIpw2),path/walk 3 - Randic shape index (RSIpw3),path/walk 4 - Randic shape index (RSIpw4),path/walk 5 - Randic shape index (RSIpw5),E-state topological parameter (ETP),Ring Count 3 (RNGCNT3),Ring Count 4 (RNGCNT4),Ring Count 5 (RNGCNT5),Ring Count 6 (RNGCNT6),Ring Count 7 (RNGCNT7),Ring Count 8 (RNGCNT8),Ring Count 9 (RNGCNT9),Ring Count 10 (RNGCNT10),Ring Count 11 (RNGCNT11),Ring Count 12 (RNGCNT12),Ring Count 13 (RNGCNT13),Ring Count 14 (RNGCNT14),Ring Count 15 (RNGCNT15),Ring Count 16 (RNGCNT16),Ring Count 17 (RNGCNT17),Ring Count 18 (RNGCNT18),Ring Count 19 (RNGCNT19),Ring Count 20 (RNGCNT20),Atom Count (ATMCNT),Bond Count (BNDCNT),Atoms in Ring System (ATMRNGCNT),Bonds in Ring System (BNDRNGCNT),Cyclomatic number (CYCLONUM),Number of ring systems (NRS),Normalized number of ring systems (NNRS),Ring Fusion degree (RFD),Ring perimeter (RNGPERM),Ring bridge count (RNGBDGE),Molecule cyclized degree (MCD),Ring Fusion density (RFDELTA),Ring complexity index (RCI),Van der Waals surface area (VSA),MR1 (MR1),MR2 (MR2),MR3 (MR3),MR4 (MR4),MR5 (MR5),MR6 (MR6),MR7 (MR7),MR8 (MR8),ALOGP1 (ALOGP1),ALOGP2 (ALOGP2),ALOGP3 (ALOGP3),ALOGP4 (ALOGP4),ALOGP5 (ALOGP5),ALOGP6 (ALOGP6),ALOGP7 (ALOGP7),ALOGP8 (ALOGP8),ALOGP9 (ALOGP9),ALOGP10 (ALOGP10),PEOE1 (PEOE1),PEOE2 (PEOE2),PEOE3 (PEOE3),PEOE4 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+O=C1N(C)C(=NC(=C1)C1CC1c1cc(ccc1)-c1ccccc1)N,BACE_1546,0,,2.7332981,317.3844,3.8594999,2,1,3,24,0,2,4,60.91,52.167999,96.124901,45.526001,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,10,2,0,0,1,5,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6389,0,2.5778999,0,0,36.071701,3.2850001,0,0,0.89130002,10.942,0,0,0,9.0653,0,0,0,0,0,0,0,6.1992998,0,0,2.7390001,0,0,0,14.7239,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6389,0,2.5778999,0,0,3.6071999,1.6425,0,0,0.89130002,2.1884,0,0,0,9.0653,0,0,0,0,0,0,0,6.1992998,0,0,2.7390001,0,0,0,14.7239,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,132,304,159,332,38,18.205276,2,2.1351848,0.23436964,1429,5.177536,23.42338,21,2.7364404,0.25513268,106.28735,85.860619,114.28432,50.5,6379,10052,19.423611,9,6746,16457,119.08334,87,770,78,18.227772,4.9181366,1.7627904,533,243,10.125,1.5520834,13.515336,8.1210213,6.3766365,4.7489052,3.2496593,2.0827236,0.56313896,0.30077857,0.1635035,0.091325104,0.049237262,0.028144913,1087,111.20558,4.1670799,648,0.9023357,4.5,3,1.826389,1.1427778,0.67722219,0.47115645,0.32121599,0.17283951,0.16125,0.1278441,0.16666667,0.083333336,0.048062865,0.0368638,0.023352491,0.018846259,0.013965912,0.008641975,0.009485294,0.009131721,0.40921861,5673,53.793056,85.860619,71.682465,0,0,11.75,6,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1115.006,1115.5006,1115.0853,1235.9275,1268.3036,1.658366,1.6577148,1.6582627,1.518328,1.4855038,13,7,0.85714287,1.3070025,16.681435,11.63103,10.693436,9.2491379,7.2863159,4.8880477,16.681435,11.63103,10.693436,8.8103056,7.2863159,4.7973256,0.69505972,0.43077889,0.27419066,0.17980215,0.11039873,0.066629522,3.6984255,199.48766,17.415638,8.5895061,7.0387812,6.2329888,0.5905754,0.35497102,0.18869491,0.10679358,52.166668,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,27,21,21,4,4,1,1,38,-17,0.875,-1.6190476,0.19047619,371.32993,0,0,0,0,0,0,0,371.32993,13.383018,105.55712,47.451828,0,6.2579937,0,4.988153,0,0,193.69182,0,30.490465,9.4417677,0,0,77.219978,9.3162336,95.907784,112.60972,20.071724,9.3681593,0,6.9041042,0,1546
diff --git a/data/bace/valid.csv b/data/bace/valid.csv
new file mode 100644
index 0000000000000000000000000000000000000000..55340af0567b2ec930a3dcc1dc31a1c02d1e98e6
--- /dev/null
+++ b/data/bace/valid.csv
@@ -0,0 +1,152 @@
+smiles,CID,Class,,pIC50,MW,AlogP,HBA,HBD,RB,HeavyAtomCount,ChiralCenterCount,ChiralCenterCountAllPossible,RingCount,PSA,Estate,MR,Polar,sLi_Key,ssBe_Key,ssssBem_Key,sBH2_Key,ssBH_Key,sssB_Key,ssssBm_Key,sCH3_Key,dCH2_Key,ssCH2_Key,tCH_Key,dsCH_Key,aaCH_Key,sssCH_Key,ddC_Key,tsC_Key,dssC_Key,aasC_Key,aaaC_Key,ssssC_Key,sNH3_Key,sNH2_Key,ssNH2_Key,dNH_Key,ssNH_Key,aaNH_Key,tN_Key,sssNH_Key,dsN_Key,aaN_Key,sssN_Key,ddsN_Key,aasN_Key,ssssN_Key,daaN_Key,sOH_Key,dO_Key,ssO_Key,aaO_Key,aOm_Key,sOm_Key,sF_Key,sSiH3_Key,ssSiH2_Key,sssSiH_Key,ssssSi_Key,sPH2_Key,ssPH_Key,sssP_Key,dsssP_Key,ddsP_Key,sssssP_Key,sSH_Key,dS_Key,ssS_Key,aaS_Key,dssS_Key,ddssS_Key,ssssssS_Key,Sm_Key,sCl_Key,sGeH3_Key,ssGeH2_Key,sssGeH_Key,ssssGe_Key,sAsH2_Key,ssAsH_Key,sssAs_Key,dsssAs_Key,ddsAs_Key,sssssAs_Key,sSeH_Key,dSe_Key,ssSe_Key,aaSe_Key,dssSe_Key,ssssssSe_Key,ddssSe_Key,sBr_Key,sSnH3_Key,ssSnH2_Key,sssSnH_Key,ssssSn_Key,sI_Key,sPbH3_Key,ssPbH2_Key,sssPbH_Key,ssssPb_Key,sLi_Cnt,ssBe_Cnt,ssssBem_Cnt,sBH2_Cnt,ssBH_Cnt,sssB_Cnt,ssssBm_Cnt,sCH3_Cnt,dCH2_Cnt,ssCH2_Cnt,tCH_Cnt,dsCH_Cnt,aaCH_Cnt,sssCH_Cnt,ddC_Cnt,tsC_Cnt,dssC_Cnt,aasC_Cnt,aaaC_Cnt,ssssC_Cnt,sNH3_Cnt,sNH2_Cnt,ssNH2_Cnt,dNH_Cnt,ssNH_Cnt,aaNH_Cnt,tN_Cnt,sssNH_Cnt,dsN_Cnt,aaN_Cnt,sssN_Cnt,ddsN_Cnt,aasN_Cnt,ssssN_Cnt,daaN_Cnt,sOH_Cnt,dO_Cnt,ssO_Cnt,aaO_Cnt,aOm_Cnt,sOm_Cnt,sF_Cnt,sSiH3_Cnt,ssSiH2_Cnt,sssSiH_Cnt,ssssSi_Cnt,sPH2_Cnt,ssPH_Cnt,sssP_Cnt,dsssP_Cnt,ddsP_Cnt,sssssP_Cnt,sSH_Cnt,dS_Cnt,ssS_Cnt,aaS_Cnt,dssS_Cnt,ddssS_Cnt,ssssssS_Cnt,Sm_Cnt,sCl_Cnt,sGeH3_Cnt,ssGeH2_Cnt,sssGeH_Cnt,ssssGe_Cnt,sAsH2_Cnt,ssAsH_Cnt,sssAs_Cnt,dsssAs_Cnt,ddsAs_Cnt,sssssAs_Cnt,sSeH_Cnt,dSe_Cnt,ssSe_Cnt,aaSe_Cnt,dssSe_Cnt,ssssssSe_Cnt,ddssSe_Cnt,sBr_Cnt,sSnH3_Cnt,ssSnH2_Cnt,sssSnH_Cnt,ssssSn_Cnt,sI_Cnt,sPbH3_Cnt,ssPbH2_Cnt,sssPbH_Cnt,ssssPb_Cnt,sLi_Sum,ssBe_Sum,ssssBem_Sum,sBH2_Sum,ssBH_Sum,sssB_Sum,ssssBm_Sum,sCH3_Sum,dCH2_Sum,ssCH2_Sum,tCH_Sum,dsCH_Sum,aaCH_Sum,sssCH_Sum,ddC_Sum,tsC_Sum,dssC_Sum,aasC_Sum,aaaC_Sum,ssssC_Sum,sNH3_Sum,sNH2_Sum,ssNH2_Sum,dNH_Sum,ssNH_Sum,aaNH_Sum,tN_Sum,sssNH_Sum,dsN_Sum,aaN_Sum,sssN_Sum,ddsN_Sum,aasN_Sum,ssssN_Sum,daaN_Sum,sOH_Sum,dO_Sum,ssO_Sum,aaO_Sum,aOm_Sum,sOm_Sum,sF_Sum,sSiH3_Sum,ssSiH2_Sum,sssSiH_Sum,ssssSi_Sum,sPH2_Sum,ssPH_Sum,sssP_Sum,dsssP_Sum,ddsP_Sum,sssssP_Sum,sSH_Sum,dS_Sum,ssS_Sum,aaS_Sum,dssS_Sum,ddssS_Sum,ssssssS_Sum,Sm_Sum,sCl_Sum,sGeH3_Sum,ssGeH2_Sum,sssGeH_Sum,ssssGe_Sum,sAsH2_Sum,ssAsH_Sum,sssAs_Sum,dsssAs_Sum,ddsAs_Sum,sssssAs_Sum,sSeH_Sum,dSe_Sum,ssSe_Sum,aaSe_Sum,dssSe_Sum,ssssssSe_Sum,ddssSe_Sum,sBr_Sum,sSnH3_Sum,ssSnH2_Sum,sssSnH_Sum,ssssSn_Sum,sI_Sum,sPbH3_Sum,ssPbH2_Sum,sssPbH_Sum,ssssPb_Sum,sLi_Avg,ssBe_Avg,ssssBem_Avg,sBH2_Avg,ssBH_Avg,sssB_Avg,ssssBm_Avg,sCH3_Avg,dCH2_Avg,ssCH2_Avg,tCH_Avg,dsCH_Avg,aaCH_Avg,sssCH_Avg,ddC_Avg,tsC_Avg,dssC_Avg,aasC_Avg,aaaC_Avg,ssssC_Avg,sNH3_Avg,sNH2_Avg,ssNH2_Avg,dNH_Avg,ssNH_Avg,aaNH_Avg,tN_Avg,sssNH_Avg,dsN_Avg,aaN_Avg,sssN_Avg,ddsN_Avg,aasN_Avg,ssssN_Avg,daaN_Avg,sOH_Avg,dO_Avg,ssO_Avg,aaO_Avg,aOm_Avg,sOm_Avg,sF_Avg,sSiH3_Avg,ssSiH2_Avg,sssSiH_Avg,ssssSi_Avg,sPH2_Avg,ssPH_Avg,sssP_Avg,dsssP_Avg,ddsP_Avg,sssssP_Avg,sSH_Avg,dS_Avg,ssS_Avg,aaS_Avg,dssS_Avg,ddssS_Avg,ssssssS_Avg,Sm_Avg,sCl_Avg,sGeH3_Avg,ssGeH2_Avg,sssGeH_Avg,ssssGe_Avg,sAsH2_Avg,ssAsH_Avg,sssAs_Avg,dsssAs_Avg,ddsAs_Avg,sssssAs_Avg,sSeH_Avg,dSe_Avg,ssSe_Avg,aaSe_Avg,dssSe_Avg,ssssssSe_Avg,ddssSe_Avg,sBr_Avg,sSnH3_Avg,ssSnH2_Avg,sssSnH_Avg,ssssSn_Avg,sI_Avg,sPbH3_Avg,ssPbH2_Avg,sssPbH_Avg,ssssPb_Avg,First Zagreb (ZM1),First Zagreb index by valence vertex degrees (ZM1V),Second Zagreb (ZM2),Second Zagreb index by valence vertex degrees (ZM2V),Polarity (Pol),Narumi Simple Topological (NST),Narumi Harmonic Topological (NHT),Narumi Geometric Topological (NGT),Total structure connectivity (TSC),Wiener (W),Mean Wiener (MW),Xu (Xu),Quadratic (QIndex),Radial centric (RC),Mean Square Distance Balaban (MSDB),Superpendentic (SP),Harary (Har),Log of product of row sums (LPRS),Pogliani (Pog),Schultz Molecular Topological (SMT),Schultz Molecular Topological by valence vertex degrees (SMTV),Mean Distance Degree Deviation (MDDD),Ramification (Ram),Gutman Molecular Topological (GMT),Gutman MTI by valence vertex degrees (GMTV),Average vertex distance degree (AVDD),Unipolarity (UP),Centralization (CENT),Variation (VAR),Molecular electrotopological variation (MEV),Maximal electrotopological positive variation (MEPV),Maximal electrotopological negative variation (MENV),Eccentric connectivity (ECCc),Eccentricity (ECC),Average eccentricity (AECC),Eccentric (DECC),Valence connectivity index chi-0 (vX0),Valence connectivity index chi-1 (vX1),Valence connectivity index chi-2 (vX2),Valence connectivity index chi-3 (vX3),Valence connectivity index chi-4 (vX4),Valence connectivity index chi-5 (vX5),Average valence connectivity index chi-0 (AvX0),Average valence connectivity index chi-1 (AvX1),Average valence connectivity index chi-2 (AvX2),Average valence connectivity index chi-3 (AvX3),Average valence connectivity index chi-4 (AvX4),Average valence connectivity index chi-5 (AvX5),Quasi Wiener (QW),First Mohar (FM),Second Mohar (SM),Spanning tree number (STN),Kier benzene-likeliness index (KBLI),Topological charge index of order 1 (TCI1),Topological charge index of order 2 (TCI2),Topological charge index of order 3 (TCI3),Topological charge index of order 4 (TCI4),Topological charge index of order 5 (TCI5),Topological charge index of order 6 (TCI6),Topological charge index of order 7 (TCI7),Topological charge index of order 8 (TCI8),Topological charge index of order 9 (TCI9),Topological charge index of order 10 (TCI10),Mean topological charge index of order 1 (MTCI1),Mean topological charge index of order 2 (MTCI2),Mean topological charge index of order 3 (MTCI3),Mean topological charge index of order 4 (MTCI4),Mean topological charge index of order 5 (MTCI5),Mean topological charge index of order 6 (MTCI6),Mean topological charge index of order 7 (MTCI7),Mean topological charge index of order 8 (MTCI8),Mean topological charge index of order 9 (MTCI9),Mean topological charge index of order 10 (MTCI10),Global topological charge (GTC),Hyper-distance-path index (HDPI),Reciprocal hyper-distance-path index (RHDPI),Square reciprocal distance sum (SRDS),Modified Randic connectivity (MRC),Balaban centric (BC),Lopping centric (LC),Kier Hall electronegativity (KHE),Sum of topological distances between N..N (STD(N N)),Sum of topological distances between N..O (STD(N O)),Sum of topological distances between N..S (STD(N S)),Sum of topological distances between N..P (STD(N P)),Sum of topological distances between N..F (STD(N F)),Sum of topological distances between N..Cl (STD(N Cl)),Sum of topological distances between N..Br (STD(N Br)),Sum of topological distances between N..I (STD(N I)),Sum of topological distances between O..O (STD(O O)),Sum of topological distances between O..S (STD(O S)),Sum of topological distances between O..P (STD(O P)),Sum of topological distances between O..F (STD(O F)),Sum of topological distances between O..Cl (STD(O Cl)),Sum of topological distances between O..Br (STD(O Br)),Sum of topological distances between O..I (STD(O I)),Sum of topological distances between S..S (STD(S S)),Sum of topological distances between S..P (STD(S P)),Sum of topological distances between S..F (STD(S F)),Sum of topological distances between S..Cl (STD(S Cl)),Sum of topological distances between S..Br (STD(S Br)),Sum of topological distances between S..I (STD(S I)),Sum of topological distances between P..P (STD(P P)),Sum of topological distances between P..F (STD(P F)),Sum of topological distances between P..Cl (STD(P Cl)),Sum of topological distances between P..Br (STD(P Br)),Sum of topological distances between P..I (STD(P I)),Sum of topological distances between F..F (STD(F F)),Sum of topological distances between F..Cl (STD(F Cl)),Sum of topological distances between F..Br (STD(F Br)),Sum of topological distances between F..I (STD(F I)),Sum of topological distances between Cl..Cl (STD(Cl Cl)),Sum of topological distances between Cl..Br (STD(Cl Br)),Sum of topological distances between Cl..I (STD(Cl I)),Sum of topological distances between Br..Br (STD(Br Br)),Sum of topological distances between Br..I (STD(Br I)),Sum of topological distances between I..I (STD(I I)),Wiener-type index from Z weighted distance matrix - Barysz matrix (WhetZ),Wiener-type index from electronegativity weighted distance matrix (Whete),Wiener-type index from mass weighted distance matrix (Whetm),Wiener-type index from van der waals weighted distance matrix (Whetv),Wiener-type index from polarizability weighted distance matrix (Whetp),Balaban-type index from Z weighted distance matrix - Barysz matrix (JhetZ),Balaban-type index from electronegativity weighted distance matrix (Jhete),Balaban-type index from mass weighted distance matrix (Jhetm),Balaban-type index from van der waals weighted distance matrix (Jhetv),Balaban-type index from polarizability weighted distance matrix (Jhetp),Topological diameter (TD),Topological radius (TR),Petitjean 2D shape (PJ2DS),Balaban distance connectivity index (J),Solvation connectivity index chi-0 (SCIX0),Solvation connectivity index chi-1 (SCIX1),Solvation connectivity index chi-2 (SCIX2),Solvation connectivity index chi-3 (SCIX3),Solvation connectivity index chi-4 (SCIX4),Solvation connectivity index chi-5 (SCIX5),Connectivity index chi-0 (CIX0),Connectivity chi-1 [Randic connectivity] (CIX1),Connectivity index chi-2 (CIX2),Connectivity index chi-3 (CIX3),Connectivity index chi-4 (CIX4),Connectivity index chi-5 (CIX5),Average connectivity index chi-0 (ACIX0),Average connectivity index chi-1 (ACIX1),Average connectivity index chi-2 (ACIX2),Average connectivity index chi-3 (ACIX3),Average connectivity index chi-4 (ACIX4),Average connectivity index chi-5 (ACIX5),reciprocal distance Randic-type index (RDR),reciprocal distance square Randic-type index (RDSR),1-path Kier alpha-modified shape index (KAMS1),2-path Kier alpha-modified shape index (KAMS2),3-path Kier alpha-modified shape index (KAMS3),Kier flexibility (KF),path/walk 2 - Randic shape index (RSIpw2),path/walk 3 - Randic shape index (RSIpw3),path/walk 4 - Randic shape index (RSIpw4),path/walk 5 - Randic shape index (RSIpw5),E-state topological parameter (ETP),Ring Count 3 (RNGCNT3),Ring Count 4 (RNGCNT4),Ring Count 5 (RNGCNT5),Ring Count 6 (RNGCNT6),Ring Count 7 (RNGCNT7),Ring Count 8 (RNGCNT8),Ring Count 9 (RNGCNT9),Ring Count 10 (RNGCNT10),Ring Count 11 (RNGCNT11),Ring Count 12 (RNGCNT12),Ring Count 13 (RNGCNT13),Ring Count 14 (RNGCNT14),Ring Count 15 (RNGCNT15),Ring Count 16 (RNGCNT16),Ring Count 17 (RNGCNT17),Ring Count 18 (RNGCNT18),Ring Count 19 (RNGCNT19),Ring Count 20 (RNGCNT20),Atom Count (ATMCNT),Bond Count (BNDCNT),Atoms in Ring System (ATMRNGCNT),Bonds in Ring System (BNDRNGCNT),Cyclomatic number (CYCLONUM),Number of ring systems (NRS),Normalized number of ring systems (NNRS),Ring Fusion degree (RFD),Ring perimeter (RNGPERM),Ring bridge count (RNGBDGE),Molecule cyclized degree (MCD),Ring Fusion density (RFDELTA),Ring complexity index (RCI),Van der Waals surface area (VSA),MR1 (MR1),MR2 (MR2),MR3 (MR3),MR4 (MR4),MR5 (MR5),MR6 (MR6),MR7 (MR7),MR8 (MR8),ALOGP1 (ALOGP1),ALOGP2 (ALOGP2),ALOGP3 (ALOGP3),ALOGP4 (ALOGP4),ALOGP5 (ALOGP5),ALOGP6 (ALOGP6),ALOGP7 (ALOGP7),ALOGP8 (ALOGP8),ALOGP9 (ALOGP9),ALOGP10 (ALOGP10),PEOE1 (PEOE1),PEOE2 (PEOE2),PEOE3 (PEOE3),PEOE4 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diff --git a/data/esol/test.csv b/data/esol/test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..bda47fd774fa00084e7f4620068670a63a15bbf3
--- /dev/null
+++ b/data/esol/test.csv
@@ -0,0 +1,109 @@
+,selfies,prop,smiles
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+22,[C] [O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-3.057,COc1ccc(Cl)cc1
+23,[O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=N] [Ring1] [=Branch1],-4.2010000000000005,o1c2ccccc2c3ccccc13
+24,[C] [=C] [C] [=C] [N] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [Ring1] [#Branch2] [=C] [Ring1] [=C],-3.846,c3ccc2nc1ccccc1cc2c3
+25,[C] [C] [C] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [P] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [P] [C] [C] [C] [Ring2] [Ring1] [Ring2] [Branch1] [C] [O] [C] [=Branch1] [C] [=O] [C] [O],-2.893,CC12CC(=O)C3C(CCC4=CC(=O)CCC34C)C2CCC1(O)C(=O)CO
+26,[C] [C] [C] [=C] [C] [=C] [C] [Branch1] [Ring1] [C] [C] [=C] [Ring1] [Branch2] [N] [Branch1] [Ring2] [C] [O] [C] [C] [=Branch1] [C] [=O] [C] [Cl],-3.319,CCc1cccc(CC)c1N(COC)C(=O)CCl 
+27,[C] [C] [C] [C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-4.157,CCCCN(C)C(=O)Nc1ccc(Cl)c(Cl)c1
+28,[C] [S] [C] [=Branch1] [C] [=S] [N] [C] [Ring1] [=Branch1] [=O],-0.396,C1SC(=S)NC1(=O)
+29,[O] [C] [=C] [C] [=C] [Branch1] [Branch2] [C] [Branch1] [C] [O] [=C] [Ring1] [#Branch1] [C] [O] [C] [=C] [C] [Branch1] [C] [O] [=C] [C] [Branch1] [C] [O] [=C] [Ring1] [Branch2] [C] [=Branch1] [C] [=O] [C] [=Ring1] [=N] [O],-2.7310000000000003,Oc1ccc(c(O)c1)c3oc2cc(O)cc(O)c2c(=O)c3O 
+30,[C] [N] [Branch1] [C] [C] [C] [=N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [C],-3.164,CN(C)C=Nc1ccc(Cl)cc1C
+31,[N] [C] [=Branch1] [C] [=O] [N] [C] [N] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [=Branch1] [=O],0.652,NC(=O)NC1NC(=O)NC1=O 
+32,[Cl] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-4.063,Clc1cccc2ccccc12
+33,[O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-3.352,Oc1ccc(Cl)c(Cl)c1
+34,[C] [C] [Branch1] [C] [C] [C] [Branch1] [#Branch1] [C] [=C] [Branch1] [C] [Cl] [Cl] [C] [Ring1] [Branch2] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [Ring1] [C] [#N] [C] [=C] [C] [=C] [C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [Ring1] [=N],-6.775,CC1(C)C(C=C(Cl)Cl)C1C(=O)OC(C#N)c2cccc(Oc3ccccc3)c2
+35,[C] [=C] [C] [=C] [NH1] [N] [=N] [C] [Ring1] [Branch1] [=C] [Ring1] [=Branch2],-2.21,c2ccc1[nH]nnc1c2
+36,[C] [C] [Branch1] [C] [C] [C] [Branch2] [Ring1] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [=Branch1] [C] [=C] [Ring1] [=Branch1] [Cl] [C] [Branch1] [C] [F] [Branch1] [C] [F] [F] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [Ring1] [C] [#N] [C] [=C] [C] [=C] [C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [Ring1] [=N],-8.057,CC(C)C(Nc1ccc(cc1Cl)C(F)(F)F)C(=O)OC(C#N)c2cccc(Oc3ccccc3)c2
+37,[C] [C] [C],-1.5530000000000002,CCC
+38,[C] [C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [O] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-3.792,C1Cc2cccc3cccc1c23
+39,[C] [C] [C] [#C],-1.092,CCC#C
+40,[Cl] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-3.5580000000000003,Clc1ccc(Cl)cc1
+41,[C] [C] [=C] [NH1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch2] [Ring1] [=Branch1],-2.9810000000000003,Cc1c[nH]c2ccccc12 
+42,[C] [C] [#N],0.152,CC#N
+43,[C] [C] [C] [C] [O],-0.688,CCCCO
+44,[C] [C] [=Branch1] [C] [=C] [C] [=Branch1] [C] [=C] [C],-2.052,CC(=C)C(=C)C
+45,[C] [C] [C] [Branch1] [C] [C] [C] [C] [O],-1.308,CCC(C)CCO
+46,[Cl] [C] [=C] [C] [=C] [Branch1] [=Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [Cl] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [Cl],-7.192,Clc1ccc(c(Cl)c1Cl)c2ccc(Cl)c(Cl)c2Cl 
+47,[C] [C] [=C] [C] [=Branch2] [Ring1] [=Branch1] [=C] [C] [=C] [Ring1] [=Branch1] [N] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [Branch1] [C] [F] [Branch1] [C] [F] [F] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.945,Cc1cc(ccc1NS(=O)(=O)C(F)(F)F)S(=O)(=O)c2ccccc2
+48,[O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [Cl],-3.22,Oc1ccc(Cl)cc1Cl
+49,[C] [N] [C] [=Branch2] [Ring1] [Ring2] [=C] [Branch1] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [S] [Ring1] [O] [=Branch1] [C] [=O] [=O] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=N] [Ring1] [=Branch1],-3.4730000000000003,CN2C(=C(O)c1ccccc1S2(=O)=O)C(=O)Nc3ccccn3 
+50,[C] [C] [C] [C] [C] [C] [Branch1] [S] [C] [C] [C] [=C] [C] [Branch1] [C] [O] [=C] [C] [=C] [Ring1] [O] [Ring1] [#Branch1] [C] [Ring1] [#C] [C] [C] [C] [Ring2] [Ring1] [C] [=O],-3.872,CC12CCC3C(CCc4cc(O)ccc34)C2CCC1=O
+51,[C] [C] [=C] [C] [=C] [C] [=C] [Branch1] [C] [C] [C] [=C] [C] [=C] [Ring1] [O] [Ring1] [#Branch1],-4.147,Cc1cccc2c(C)cccc12
+52,[N] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [O] [N] [C] [N] [S] [Ring1] [=Branch1] [=Branch1] [C] [=O] [=O] [C] [=C] [Ring1] [N] [Cl],-1.72,NS(=O)(=O)c2cc1c(NCNS1(=O)=O)cc2Cl 
+53,[O] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [N] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-2.725,Oc1cccc2cccnc12 
+54,[C] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Ring1] [#Branch2],-3.447,C1CCc2ccccc2C1
+55,[C] [C] [O] [C] [Branch1] [C] [C] [O] [C] [C],-0.899,CCOC(C)OCC
+56,[C] [C] [C] [C] [Ring1] [Ring1] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [Branch1] [C] [Ring1] [Branch2] [=O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-3.464,CC12CC2(C)C(=O)N(C1=O)c3cc(Cl)cc(Cl)c3
+57,[C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=C] [Ring1] [=Branch1],-4.87,Cc1c2ccccc2cc3ccccc13
+58,[C] [C] [C] [C] [O] [C],-1.072,CCCCOC
+59,[C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [Ring1] [#Branch1] [C] [C] [C] [C] [C] [C] [C] [Branch1] [#Branch1] [C] [=Branch1] [C] [=O] [C] [O] [C] [Ring1] [=Branch2] [Branch1] [N] [C] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [#Branch1] [Ring1] [=C] [C] [=O],-3.0660000000000003,CC13CCC(=O)C=C1CCC4C2CCC(C(=O)CO)C2(CC(O)C34)C=O 
+60,[C] [C] [C] [Branch1] [=Branch1] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [O] [=O],-1.6030000000000002,CCC1(C(C)C)C(=O)NC(=O)NC1=O 
+61,[C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1],-2.761,CCOC(=O)c1ccc(O)cc1
+62,[C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring2] [Ring1] [C] [C] [=C] [Ring2] [Ring1] [C] [C] [Ring1] [S] [=C] [Ring1] [=C] [C] [Ring1] [N] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-6.885,c1cc2ccc3ccc4ccc5ccc6ccc1c7c2c3c4c5c67
+63,[C] [C] [N] [C] [=C] [C] [Branch1] [=Branch1] [N] [Branch1] [C] [C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch2] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring2] [Ring1] [Ring2] [Ring1] [=Branch1],-4.408,CCN2c1cc(N(C)C)cc(C)c1NC(=O)c3cccnc23 
+64,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-3.301,CN(C)C(=O)Nc1ccc(Cl)c(Cl)c1
+65,[C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [C],-3.3080000000000003,CCCCCC(C)C
+66,[C] [O] [C] [=C] [C] [=C] [Branch1] [C] [N] [N] [=C] [Branch1] [#C] [N] [=C] [Ring1] [#Branch1] [C] [Branch1] [Ring1] [O] [C] [=C] [Ring1] [=N] [O] [C] [N] [C] [C] [N] [Branch1] [Branch1] [C] [C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [O] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [O],-3.958,COc2cc1c(N)nc(nc1c(OC)c2OC)N3CCN(CC3)C(=O)OCC(C)(C)O 
+67,[C] [=C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [#Branch1] [C] [=C] [Ring1] [O],-0.636,c1cC2C(=O)NC(=O)C2cc1
+68,[C] [C] [C] [=O],-0.3939999999999999,CCC=O
+69,[Cl] [C] [=C] [C] [=C] [Branch2] [Ring1] [=Branch2] [C] [N] [Branch1] [Branch2] [C] [C] [C] [C] [C] [Ring1] [Branch1] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [Ring2] [Ring1] [=Branch1],-5.126,Clc1ccc(CN(C2CCCC2)C(=O)Nc3ccccc3)cc1
+70,[C] [C] [C] [C] [C] [Branch1] [Ring1] [C] [C] [C] [=O],-2.232,CCCCC(CC)C=O
+71,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring1] [C] [C] [C] [C] [C] [Branch1] [C] [C] [C],-2.312,O=C1NC(=O)NC(=O)C1(CC)CCC(C)C
+72,[C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.857,CC(=O)Nc1ccccc1
+73,[C] [=N] [C] [=C] [C] [Branch1] [#Branch1] [C] [=Branch1] [C] [=O] [N] [N] [=C] [Ring1] [#Branch2],-0.7170000000000001,c1nccc(C(=O)NN)c1
+74,[C] [C] [Branch1] [C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [Ring2] [C] [Ring1] [=Branch1] [C] [Ring1] [=Branch2] [=O],-2.158,CC2(C)C1CCC(C)(C1)C2=O
+75,[C] [O] [C] [=C] [N] [=C] [C] [=N] [C] [=N] [C] [Ring1] [=Branch1] [=N] [Ring1] [#Branch2],-1.589,COc2cnc1cncnc1n2
+76,[C] [N] [C] [=Branch1] [C] [=O] [C] [=C] [Branch1] [C] [C] [O] [P] [=Branch1] [C] [=O] [Branch1] [Ring1] [O] [C] [O] [C],-0.949,CNC(=O)C=C(C)OP(=O)(OC)OC
+77,[O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Branch1] [Ring1] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring2] [Ring1] [C] [Ring1] [=Branch1],-3.784,O2c1ccccc1N(CC)C(=O)c3ccccc23 
+78,[C] [=C] [C] [=C] [C] [=C] [Branch1] [Ring1] [O] [C] [C] [Branch1] [Branch2] [C] [C] [=C] [Branch1] [C] [C] [C] [=C] [Ring1] [=N] [O] [C] [Ring1] [P] [=O],-4.0760000000000005,c1cc2ccc(OC)c(CC=C(C)(C))c2oc1=O 
+79,[C] [C] [C] [S] [C] [C] [C],-2.307,CCCSCCC
+80,[C] [O] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-2.948,CON(C)C(=O)Nc1ccc(Cl)cc1
+81,[C] [C] [O] [C] [C],-0.718,CCOCC
+82,[C] [C] [C] [C] [C] [C] [Branch1] [S] [C] [C] [C] [=C] [C] [Branch1] [C] [O] [=C] [C] [=C] [Ring1] [O] [Ring1] [#Branch1] [C] [Ring1] [#C] [C] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [Ring1] [O],-3.858,CC34CCC1C(CCc2cc(O)ccc12)C3CC(O)C4O 
+83,[C] [C] [N] [C] [=N] [C] [Branch1] [C] [Cl] [=N] [C] [Branch1] [O] [N] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [#N] [=N] [Ring1] [=N],-2.49,CCNc1nc(Cl)nc(NC(C)(C)C#N)n1
+84,[C] [C] [Branch1] [C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [O],-1.6469999999999998,CC(C)CC(C)(C)O
+85,[Cl] [C] [=C] [C] [=C] [C] [Branch1] [C] [Br] [=C] [Ring1] [#Branch1],-3.928,Clc1cccc(Br)c1
+86,[C] [C] [C] [C] [C] [C] [Branch1] [C] [O] [C] [C],-2.033,CCCCCC(O)CC
+87,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring1] [C] [C] [C] [C] [=C] [Branch1] [C] [C] [C],-2.126,O=C1NC(=O)NC(=O)C1(CC)CC=C(C)C
+88,[C] [C] [C] [Branch1] [C] [C] [C] [Branch1] [#Branch1] [C] [C] [Branch1] [C] [Br] [=C] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [N] [=O],-2.766,CCC(C)C1(CC(Br)=C)C(=O)NC(=O)NC1=O 
+89,[C] [O] [C] [=Branch1] [C] [=O] [C],-0.416,COC(=O)C
+90,[C] [C] [Branch1] [C] [C] [C] [=C] [C] [=C] [Branch1] [C] [C] [C] [=C] [Ring1] [#Branch1] [O],-3.129,CC(C)c1ccc(C)cc1O
+91,[C],-0.636,C
+92,[N] [C] [=N] [C] [Branch1] [C] [O] [=N] [C] [N] [=C] [NH1] [C] [Ring1] [#Branch2] [=Ring1] [Branch1],-1.74,Nc1nc(O)nc2nc[nH]c12 
+93,[F] [C] [=C] [C] [=C] [C] [Branch1] [C] [F] [=C] [Ring1] [#Branch1] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-4.692,Fc1cccc(F)c1C(=O)NC(=O)Nc2ccc(Cl)cc2
+94,[C] [C] [C] [C] [C] [Branch1] [Branch1] [C] [C] [Ring1] [=Branch1] [C] [Branch1] [C] [C] [Branch1] [C] [C] [O] [Ring1] [#Branch2],-2.579,CC12CCC(CC1)C(C)(C)O2
+95,[C] [C] [O],0.02,CCO
+96,[C] [=C] [Branch2] [Ring1] [C] [N] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [C] [C] [=C] [C] [=C] [Ring1] [P],-2.29,c1c(NC(=O)OC(C)C(=O)NCC)cccc1
+97,[C] [C] [Branch1] [C] [C] [=C] [C] [C] [Branch2] [Ring1] [#Branch2] [C] [=Branch1] [C] [=O] [O] [C] [C] [=C] [C] [=C] [C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [Ring1] [=N] [C] [Ring2] [Ring1] [Ring2] [Branch1] [C] [C] [C],-6.763,CC(C)=CC3C(C(=O)OCc2cccc(Oc1ccccc1)c2)C3(C)C 
+98,[C] [C] [C] [C] [N] [C] [=Branch1] [C] [=O] [N] [C] [Branch1] [Branch2] [N] [C] [=Branch1] [C] [=O] [O] [C] [=N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=C] [Ring1] [=Branch1],-2.902,CCCCNC(=O)n1c(NC(=O)OC)nc2ccccc12
+99,[C] [N] [Branch1] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.542,CN(C)c1ccccc1
+100,[C] [O] [C] [=Branch1] [C] [=O] [C] [=C],-0.878,COC(=O)C=C
+101,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [=N] [O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [C] [=C] [Ring1] [=C],-4.477,CN(C)C(=O)Nc2ccc(Oc1ccc(Cl)cc1)cc2
+102,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [=Branch1] [C] [Branch1] [C] [C] [C] [C] [C] [=C] [Branch1] [C] [C] [C],-2.465,O=C1NC(=O)NC(=O)C1(C(C)C)CC=C(C)C
+103,[C] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1] [C],-2.6210000000000004,Cc1ccc(O)cc1C
+104,[Cl] [C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=N] [Ring1] [=Branch1] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-3.833,Clc1cccc(n1)C(Cl)(Cl)Cl
+105,[C] [C] [=Branch1] [C] [=O] [O] [C] [Branch2] [Ring1] [=C] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [C] [Ring1] [O] [C] [C] [C] [Ring2] [Ring1] [C] [Ring1] [#C] [C] [C] [#C],-4.2410000000000005,CC(=O)OC3(CCC4C2CCC1=CC(=O)CCC1C2CCC34C)C#C
+106,[C] [N] [C] [=Branch1] [C] [=O] [O] [N] [=C] [Branch1] [Ring2] [C] [S] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-2.7,CNC(=O)ON=C(CSC)C(C)(C)C 
+107,[C] [C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [O],-2.033,CCCCCCC(C)O
diff --git a/data/esol/train.csv b/data/esol/train.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2948f4a0ae3ffd44b919c2fbbdcd6a34aab9950b
--- /dev/null
+++ b/data/esol/train.csv
@@ -0,0 +1,855 @@
+,selfies,prop,smiles
+0,[O] [C] [C] [O] [C] [Branch2] [Ring2] [Ring1] [O] [C] [C] [O] [C] [Branch1] [#C] [O] [C] [Branch1] [Ring1] [C] [#N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [=N] [O],-0.974,OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O 
+1,[C] [C] [O] [C] [=C] [C] [=Ring1] [Branch1] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.885,Cc1occc1C(=O)Nc2ccccc2
+2,[C] [C] [Branch1] [C] [C] [=C] [C] [C] [C] [Branch1] [C] [C] [=C] [C] [=O],-2.579,CC(C)=CCCC(C)=CC(=O)
+3,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=C],-6.617999999999999,c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43
+4,[C] [C] [=C] [S] [C] [=Ring1] [Branch1],-2.232,c1ccsc1
+5,[C] [=C] [C] [=C] [S] [C] [=N] [C] [Ring1] [Branch1] [=C] [Ring1] [=Branch2],-2.733,c2ccc1scnc1c2 
+6,[Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [C] [=C] [Ring1] [#Branch1] [Cl],-6.545,Clc1cc(Cl)c(c(Cl)c1)c2c(Cl)cccc2Cl
+7,[C] [C] [C] [C] [C] [C] [Branch1] [S] [C] [C] [C] [=C] [C] [Branch1] [C] [O] [=C] [C] [=C] [Ring1] [O] [Ring1] [#Branch1] [C] [Ring1] [#C] [C] [C] [C] [Ring2] [Ring1] [C] [O],-4.138,CC12CCC3C(CCc4cc(O)ccc34)C2CCC1O
+8,[C] [O] [C] [=C] [C] [O] [C] [C] [O] [C] [=C] [C] [C] [Branch2] [Ring1] [N] [O] [C] [Ring1] [Branch1] [=C] [C] [=C] [Ring1] [=Branch2] [C] [=Branch1] [C] [=O] [C] [Ring1] [=C] [C] [=Ring2] [Ring1] [C] [C] [=C] [Ring2] [Ring1] [=Branch1] [O] [C] [C] [Branch1] [C] [C] [=C],-5.246,COc5cc4OCC3Oc2c1CC(Oc1ccc2C(=O)C3c4cc5OC)C(C)=C 
+9,[O] [=C] [C] [C] [C] [N] [Ring1] [Branch1],0.243,O=C1CCCN1
+10,[Cl] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-4.063,Clc1ccc2ccccc2c1
+11,[C] [C] [C] [C] [=C],-2.01,CCCC=C
+12,[C] [C] [C] [Branch1] [N] [C] [=Branch1] [C] [=O] [N] [C] [N] [C] [Ring1] [#Branch1] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.897,CCC1(C(=O)NCNC1=O)c2ccccc2
+13,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C],-5.45,CCCCCCCCCCCCCC
+14,[C] [C] [Branch1] [C] [C] [Cl],-1.585,CC(C)Cl
+15,[C] [C] [C] [Branch1] [C] [C] [C] [O],-1.027,CCC(C)CO
+16,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [O] [C] [=C] [C] [Branch1] [C] [C] [=N] [C] [=Branch1] [Ring2] [=N] [Ring1] [#Branch1] [C] [Branch1] [C] [C] [C],-3.989,CCOP(=S)(OCC)Oc1cc(C)nc(n1)C(C)C
+17,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [O],-3.096,CCCCCCCCCC(C)O
+18,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [Cl],-6.627000000000001,Clc1ccc(c(Cl)c1)c2c(Cl)ccc(Cl)c2Cl 
+19,[O] [=C] [NH1] [C] [C] [C] [C] [C] [=Ring1] [Branch1] [C] [=Branch1] [C] [=O] [N] [Ring1] [#Branch2] [C] [C] [C] [C] [C] [C] [Ring1] [=Branch1],-3.355,O=c2[nH]c1CCCc1c(=O)n2C3CCCCC3
+20,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [S] [C] [S] [C] [C],-3.747,CCOP(=S)(OCC)SCSCC
+21,[C] [C] [O] [C] [=C] [C] [=C] [Branch1] [#Branch1] [N] [C] [=Branch1] [C] [=O] [C] [C] [=C] [Ring1] [#Branch2],-2.342,CCOc1ccc(NC(=O)C)cc1
+22,[C] [C] [C] [C] [C] [C] [C] [O],-1.751,CCCCCCCO
+23,[C] [N] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C] [N] [=C] [NH1] [C] [=Ring1] [Branch1] [C] [Ring1] [O] [=O],-1.452,Cn1c(=O)n(C)c2nc[nH]c2c1=O
+24,[Cl] [C] [Branch1] [C] [Cl] [=C] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-6.553,ClC(Cl)=C(c1ccc(Cl)cc1)c2ccc(Cl)cc2
+25,[C] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C],-2.608,CCCCCCCC(=O)OC
+26,[C] [C] [C] [=C] [C] [=C] [Branch1] [Ring1] [C] [C] [C] [=C] [Ring1] [Branch2],-3.633,CCc1ccc(CC)cc1
+27,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [S] [C] [S] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-4.367,CCOP(=S)(OCC)SCSC(C)(C)C
+28,[C] [O] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [Branch2] [Ring1] [C] [O] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1] [=C] [Ring1] [P],-4.229,COC(=O)Nc1cccc(OC(=O)Nc2cccc(C)c2)c1
+29,[Cl] [C] [=Branch1] [C] [=C] [Cl],-1.939,ClC(=C)Cl
+30,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Ring1] [=Branch2],-4.478,Cc1cccc2c1Cc3ccccc32
+31,[N] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.857,N(c1ccccc1)c2ccccc2
+32,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [S] [C] [C] [C] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.297,CN(C)C(=O)SCCCCOc1ccccc1
+33,[C] [C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Branch1] [O] [C] [C] [C] [S] [C] [C] [=Branch1] [C] [=O] [N] [C] [C] [C] [C] [C] [Ring1] [=Branch1] [C],-4.637,CCCOP(=S)(OCCC)SCC(=O)N1CCCCC1C
+34,[C] [C] [C] [C] [C] [C] [C] [I],-3.904,CCCCCCCI
+35,[C] [=C] [Branch1] [C] [Cl] [C] [=C] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.685,c1c(Cl)cccc1c2ccccc2
+36,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [Branch1] [=Branch1] [C] [C] [C] [Ring1] [Ring2] [C] [=Branch1] [C] [=O] [N] [Ring1] [O],-0.527,O=C2NC(=O)C1(CCC1)C(=O)N2
+37,[C] [C] [Branch1] [C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [C] [C] [Ring1] [#Branch1] [O],-2.782,CC(C)C1CCC(C)CC1O 
+38,[C] [C] [Branch1] [C] [C] [O] [C] [=O],-0.684,CC(C)OC=O
+39,[C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [O],-1.6780000000000002,CCCCCC(C)O
+40,[C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [#Branch1],-3.012,CC(=O)Nc1ccc(Br)cc1
+41,[C] [=C] [Branch1] [C] [C] [C] [=C] [N] [=C] [Branch1] [C] [C] [C] [=C] [C] [Ring1] [#Branch1] [=C] [Ring1] [N],-3.342,c2c(C)cc1nc(C)ccc1c2 
+42,[C] [C] [C] [C] [C] [C] [C] [#C],-2.509,CCCCCCC#C
+43,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [=C] [Ring1] [#C] [Ring1] [#Branch2],-5.568,c1ccc2c(c1)ccc3c4ccccc4ccc23
+44,[C] [C] [C] [Branch1] [C] [C] [N] [C] [=Branch1] [C] [=O] [NH1] [C] [Branch1] [C] [C] [=C] [Branch1] [C] [Br] [C] [Ring1] [=Branch2] [=O],-3.419,CCC(C)n1c(=O)[nH]c(C)c(Br)c1=O 
+45,[Cl] [C] [=C] [C] [=C] [C] [=Branch1] [Branch1] [=C] [Ring1] [=Branch1] [Cl] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-7.185,Clc1cccc(c1Cl)c2c(Cl)c(Cl)cc(Cl)c2Cl 
+46,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O],-2.281,Cc1ccccc1O
+47,[C] [C] [Branch1] [C] [C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-3.631,CC(C)CCC(C)(C)C
+48,[C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [=C] [Ring1] [#Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring2] [Ring1] [C] [Ring1] [=Branch1],-5.931,Cc1cc2c3ccccc3ccc2c4ccccc14
+49,[C] [C] [C] [C] [=Branch1] [C] [=O] [C],-0.846,CCCC(=O)C
+50,[Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [=Branch1] [Branch1] [=C] [Ring1] [Branch2] [Cl] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-8.304,Clc1cc(Cl)c(Cl)c(c1Cl)c2c(Cl)c(Cl)cc(Cl)c2Cl 
+51,[C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [C],-1.545,CCCOC(=O)CC
+52,[C] [C] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [F] [C] [Branch1] [P] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Ring1] [N] [Ring1] [#Branch1] [C] [C] [Ring2] [Ring1] [C] [C] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [=Branch1] [Branch1] [C] [O] [C] [=Branch1] [C] [=O] [C] [O],-2.734,CC34CC(O)C1(F)C(CCC2=CC(=O)C=CC12C)C3CC(O)C4(O)C(=O)CO
+53,[C] [C] [N] [C] [=N] [C] [Branch1] [C] [Cl] [=N] [C] [=Branch1] [Ring2] [=N] [Ring1] [#Branch1] [N] [Branch1] [Ring1] [C] [C] [C] [C],-3.233,CCNc1nc(Cl)nc(n1)N(CC)CC
+54,[N] [C] [=Branch1] [C] [=O] [C] [=C] [N] [=C] [C] [=N] [Ring1] [=Branch1],-0.674,NC(=O)c1cnccn1
+55,[C] [C] [C] [Branch1] [C] [Br] [Branch1] [Ring1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [Branch1] [C] [N] [=O],-2.198,CCC(Br)(CC)C(=O)NC(N)=O
+56,[Cl] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl],-4.984,Clc1ccccc1c2ccccc2Cl 
+57,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring2] [C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.36,O=C1NC(=O)NC(=O)C1(CC=C)c1ccccc1
+58,[C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C],-1.899,CCCCC(=O)OCC
+59,[O] [=C] [N] [Branch1] [N] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [Ring2] [N] [Ring1] [#C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.1530000000000005,O=C1N(COC(=O)CCCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3
+60,[Cl] [C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [Cl],-5.7620000000000005,Clc1cccc(c1)c2cc(Cl)ccc2Cl 
+61,[C] [C] [C] [Br],-1.949,CCCBr
+62,[C] [C] [C] [C] [C] [O] [C] [Branch1] [=Branch2] [C] [N] [C] [=N] [C] [=N] [Ring1] [Branch1] [Branch1] [Ring2] [O] [Ring1] [O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [Cl],-4.603,CCCC1COC(Cn2cncn2)(O1)c3ccc(Cl)cc3Cl
+63,[C] [C] [=N] [C] [=N] [C] [=N] [C] [=C] [N] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-1.24,Cc1ncnc2nccnc12
+64,[N] [C] [=Branch1] [C] [=S] [N],0.3289999999999999,NC(=S)N
+65,[C] [C] [=C] [C] [=C] [Branch1] [C] [C] [C] [=C] [Ring1] [#Branch1],-3.035,Cc1ccc(C)cc1
+66,[C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [C],-3.6010000000000004,CCc1ccccc1CC
+67,[Cl] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-4.215,ClC(Cl)(Cl)C(Cl)(Cl)Cl
+68,[C] [C] [Branch1] [C] [C] [C] [Branch2] [Ring1] [=C] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [Ring1] [C] [#N] [C] [=C] [C] [=C] [C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [Ring1] [=N] [C] [=C] [C] [=C] [Branch1] [#Branch1] [O] [C] [Branch1] [C] [F] [F] [C] [=C] [Ring1] [#Branch2],-6.877999999999999,CC(C)C(C(=O)OC(C#N)c1cccc(Oc2ccccc2)c1)c3ccc(OC(F)F)cc3
+69,[C] [C] [N] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring1] [P] [Ring1] [=Branch1],-4.423,CCN2c1cc(Cl)ccc1NC(=O)c3cccnc23 
+70,[C] [C] [C] [C] [Branch1] [C] [C] [C] [Branch1] [Ring2] [C] [C] [=C] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=S] [N] [C] [Ring1] [O] [=O],-3.063,CCCC(C)C1(CC=C)C(=O)NC(=S)NC1=O 
+71,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [N] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-4.957,c1ccc2c(c1)c3cccc4cccc2c34
+72,[C] [C] [C] [O] [C] [Branch1] [C] [C] [C],-1.354,CCCOC(C)C
+73,[C] [C] [=C] [C] [Branch1] [C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [O],-4.147,Cc1cc(C)c2ccccc2c1
+74,[C] [C] [C] [=Branch1] [P] [=C] [Branch1] [Ring1] [C] [C] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1],-5.074,CCC(=C(CC)c1ccc(O)cc1)c2ccc(O)cc2 
+75,[C] [Branch1] [Ring1] [C] [#N] [=C] [Branch1] [C] [Cl] [C] [Branch1] [Ring1] [C] [#N] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [Ring1] [=N],-3.995,c1(C#N)c(Cl)c(C#N)c(Cl)c(Cl)c(Cl)1
+76,[C] [O] [C] [Ring1] [Ring1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.826,C1OC1c2ccccc2 
+77,[C] [C] [C] [C] [C] [C] [Branch1] [P] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [S] [C] [C] [C] [Ring2] [Ring1] [Ring1] [C] [=Branch1] [C] [=O] [C] [O],-3.939,CC12CCC3C(CCC4=CC(=O)CCC34C)C2CCC1C(=O)CO
+78,[C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [=Branch2] [O],0.601,C1OC(O)C(O)C(O)C1O
+79,[Cl] [C] [Cl],-1.156,ClCCl
+80,[C] [C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-4.1,CCc1cccc2ccccc12
+81,[C] [O] [C] [=O],-0.048,COC=O
+82,[C] [C] [=C] [NH1] [C] [=Branch1] [C] [=O] [NH1] [C] [Ring1] [#Branch1] [=O],-0.78,Cc1c[nH]c(=O)[nH]c1=O 
+83,[C] [C] [Branch1] [C] [C] [C],-1.891,CC(C)C
+84,[O] [C] [C] [O] [C] [Branch1] [=Branch2] [C] [Branch1] [C] [O] [C] [Ring1] [=Branch1] [O] [N] [C] [=N] [C] [=C] [Branch1] [C] [O] [N] [=C] [N] [=C] [Ring1] [#Branch2] [Ring1] [#Branch1],-0.8340000000000001,OCC1OC(C(O)C1O)n2cnc3c(O)ncnc23
+85,[C] [C] [C] [C] [C],-2.261,CCCCC
+86,[C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O],-1.991,c1ccccc1O
+87,[N] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [Ring1] [#Branch2] [=C] [Ring1] [=C],-3.789,Nc3ccc2cc1ccccc1cc2c3 
+88,[C] [=C] [C] [=C] [C] [=N] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-2.531,c1ccc2cnccc2c1
+89,[C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [S] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [=Branch1] [C] [Branch1] [C] [C] [C] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-5.106,COP(=S)(OC)SCC(=O)N(C(C)C)c1ccc(Cl)cc1
+90,[C] [C] [C] [C] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.22,CCCCCCc1ccccc1
+91,[Cl] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.5280000000000005,Clc1ccccc1c2ccccc2
+92,[C] [C] [C] [C] [=Branch1] [C] [=C] [C],-2.3480000000000003,CCCC(=C)C
+93,[C] [C] [Branch1] [C] [C] [C] [Branch1] [C] [C] [C] [Branch1] [C] [C] [C],-3.276,CC(C)C(C)C(C)C
+94,[Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-5.167999999999999,Clc1cc(Cl)c(Cl)c(Cl)c1Cl
+95,[C] [C] [C] [C] [C] [C] [C] [C] [C] [=C],-3.781,CCCCCCCCC=C
+96,[C] [C] [Branch1] [C] [C] [C] [Branch1] [#Branch1] [C] [=C] [Branch1] [C] [Cl] [Cl] [C] [Ring1] [Branch2] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [Ring1] [C] [#N] [C] [=C] [C] [=C] [Branch1] [C] [F] [C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [Ring1] [=C],-6.84,CC1(C)C(C=C(Cl)Cl)C1C(=O)OC(C#N)c2ccc(F)c(Oc3ccccc3)c2
+97,[C] [=C] [C] [=N] [C] [=C] [Ring1] [=Branch1],-1.481,c1ccncc1
+98,[C] [C] [C] [C] [C] [C] [C] [Br],-3.366,CCCCCCCBr
+99,[C] [C] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [F] [C] [Branch1] [P] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [N] [Ring1] [#Branch1] [C] [C] [Ring2] [Ring1] [C] [C] [C] [C] [Ring2] [Ring1] [Branch1] [Branch1] [C] [O] [C] [=Branch1] [C] [=O] [C] [O],-3.172,CC34CC(O)C1(F)C(CCC2=CC(=O)CCC12C)C3CCC4(O)C(=O)CO 
+100,[C] [C] [S] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O] [C] [=Branch1] [C] [=O] [N] [C],-2.855,CCSCc1ccccc1OC(=O)NC 
+101,[C] [C] [=C] [C] [C] [Branch1] [Branch1] [C] [C] [Ring1] [=Branch1] [C] [Branch1] [C] [C] [=C],-3.429,CC1=CCC(CC1)C(C)=C
+102,[C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Ring1] [=Branch2],-3.057,C1Cc2ccccc2C1
+103,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [Branch1] [Branch1] [C] [C] [Ring1] [Ring1] [C] [=Branch1] [C] [=O] [N] [Ring1] [#Branch2],-0.088,O=C2NC(=O)C1(CC1)C(=O)N2 
+104,[Cl] [C] [=C] [C] [=C] [C] [Branch1] [C] [I] [=C] [Ring1] [#Branch1],-4.384,Clc1cccc(I)c1
+105,[Br] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-4.434,Brc1cccc2ccccc12
+106,[C] [C] [/C] [=C] [/C],-2.076,CC/C=C/C
+107,[C] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [=N] [Ring1] [#Branch1],-2.0980000000000003,Cc1cccc(C)n1
+108,[Cl] [C] [=C] [Branch1] [C] [Cl] [Cl],-2.312,ClC=C(Cl)Cl
+109,[N] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-2.721,Nc1cccc2ccccc12
+110,[O] [C] [=N] [C] [=C] [N] [=C] [C] [=N] [C] [Ring1] [=Branch1] [=N] [Ring1] [#Branch2],-1.404,Oc2ncc1nccnc1n2
+111,[C] [O],0.441,CO
+112,[C] [C] [C] [Branch1] [Branch2] [C] [C] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [=N] [=O],-2.312,CCC1(CCC(C)C)C(=O)NC(=O)NC1=O
+113,[C] [C] [C] [=Branch1] [C] [=O] [C],-0.491,CCC(=O)C
+114,[F] [C] [=C] [NH1] [C] [=Branch1] [C] [=O] [NH1] [C] [Ring1] [#Branch1] [=O],-0.792,Fc1c[nH]c(=O)[nH]c1=O 
+115,[N] [C] [=N] [C] [=N] [C] [N] [Branch1] [Branch2] [C] [=C] [C] [Ring1] [=Branch2] [=Ring1] [Branch1] [C] [O] [C] [Branch1] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [Branch2] [O],-0.892,Nc1ncnc2n(ccc12)C3OC(CO)C(O)C3O 
+116,[O] [C] [=C] [C] [=C] [C] [Branch1] [C] [O] [=C] [Ring1] [#Branch1],-1.59,Oc1cccc(O)c1
+117,[C] [C] [C] [C] [C] [C] [O],-1.3969999999999998,CCCCCCO
+118,[C] [C] [C] [C] [C] [Cl],-2.294,CCCCCCl
+119,[C] [=C] [C] [=C],-1.376,C=CC=C
+120,[O] [C] [=C] [C] [=C] [C] [C] [C] [C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-3.0860000000000003,Oc2ccc1CCCCc1c2 
+121,[N] [C] [=Branch1] [C] [=O] [C] [Cl],-0.106,NC(=O)CCl 
+122,[C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [I] [C] [=C] [Ring1] [Branch2] [Cl],-6.148,COP(=S)(OC)Oc1cc(Cl)c(I)cc1Cl
+123,[C] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-3.297,Cc1ccc(Cl)cc1
+124,[C] [C] [=C] [C] [=C] [Branch2] [Ring1] [=C] [O] [P] [=Branch1] [C] [=O] [Branch1] [=N] [O] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [C] [=C] [Ring2] [Ring1] [=Branch2],-6.39,Cc1ccc(OP(=O)(Oc2cccc(C)c2)Oc3ccccc3C)cc1
+125,[C] [C] [C] [C] [C] [C] [=O],-1.457,CCCCCC=O
+126,[C] [C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [C] [N] [C] [=C] [Ring1] [#Branch1],-3.039,CCCCOC(=O)c1ccc(N)cc1
+127,[O] [C] [=C] [C] [Branch1] [C] [C] [=C] [C] [=C] [Ring1] [#Branch1] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Branch1] [C] [N] [=C] [N] [=C] [Ring2] [Ring1] [Ring1] [Ring1] [#Branch1],-3.049,O2c1cc(C)ccc1N(C)C(=O)c3cc(N)cnc23 
+128,[C] [C] [Branch1] [C] [C] [=C] [C] [C] [/C] [Branch1] [C] [C] [=C] [\C] [O],-2.603,CC(C)=CCC/C(C)=C\CO
+129,[O] [=C] [N] [Branch1] [=C] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [Ring2] [N] [Ring1] [P] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.84,O=C1N(COC(=O)CCCCCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3
+130,[Cl] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [Cl],-4.546,Clc1ccc(Cl)c(Cl)c1Cl
+131,[C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [Branch2] [C] [O] [C] [Branch1] [C] [N] [=O] [C] [O] [C] [Branch1] [C] [N] [=O],-1.376,CCCC(C)(COC(N)=O)COC(N)=O 
+132,[C] [C] [=Branch1] [C] [=O] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [C] [Branch1] [C] [O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [C] [C] [Ring2] [Ring1] [Ring1] [Ring1] [S] [C],-4.342,CC(=O)C3CCC4C2CC=C1CC(O)CCC1(C)C2CCC34C 
+133,[O] [=C] [N] [Branch1] [=N] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [Ring2] [N] [Ring1] [S] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.496,O=C1N(COC(=O)CCCCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3
+134,[C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [Ring1] [=Branch2],-2.015,CC1=CC(=O)CC(C)(C)C1 
+135,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring1] [C] [C] [C] [Branch1] [C] [C] [C] [C],-1.958,O=C1NC(=O)NC(=O)C1(CC)C(C)CC
+136,[C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C] [C] [C] [C],-2.329,CCCCC(=O)CCCC
+137,[C] [C] [C] [Branch1] [N] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [#Branch1] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.591,CCC1(CCC(=O)NC1=O)c2ccccc2 
+138,[C] [C] [C] [Branch1] [C] [C] [C] [C],-2.6,CCC(C)CC
+139,[C] [C] [O] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [O] [C] [C] [=C] [C] [=C] [C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [Ring1] [=N],-6.896,CCOc1ccc(cc1)C(C)(C)COCc3cccc(Oc2ccccc2)c3
+140,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [C] [Branch1] [C] [C] [=N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Ring1] [O] [=O],-3.881,Cc1ccccc1n3c(C)nc2ccccc2c3=O
+141,[Cl] [C] [C] [#N],-0.4479999999999999,ClCC#N
+142,[C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [Ring1] [#Branch1] [C] [C] [C] [C] [Ring1] [O] [C] [C] [C] [Branch1] [C] [C] [C] [Ring1] [#Branch1] [C] [C] [C] [Ring1] [=Branch1] [Branch1] [C] [O] [C] [#C],-3.858,CC12CCC(=O)C=C1CCC3C2CCC4(C)C3CCC4(O)C#C 
+143,[C] [=C] [C] [=N] [N] [=C] [Ring1] [=Branch1],-0.619,c1ccnnc1
+144,[Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2],-4.621,Clc1cc(Cl)c(Cl)c(Cl)c1
+145,[C] [C] [Branch1] [C] [O] [C] [C] [C] [Branch1] [C] [C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [C] [Branch1] [C] [C] [C] [C] [O] [C] [C] [Branch1] [C] [C] [C] [C] [Ring1] [#Branch1] [O] [C] [Ring1] [N] [C] [C] [Ring1] [S] [C] [Ring2] [Ring1] [Ring2] [C] [=C] [Ring2] [Ring1] [=Branch2] [Ring2] [Ring1] [=C],-5.681,C1C(O)CCC2(C)CC3CCC4(C)C5(C)CC6OCC(C)CC6OC5CC4C3C=C21
+146,[N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O],-1.465,Nc1ccccc1O
+147,[C] [C] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C],-3.3160000000000003,CCCCCCCCC(=O)OCC
+148,[C] [O] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [O] [C] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C] [=C] [Branch1] [C] [C] [C] [=C] [C] [=C] [Ring1] [#Branch1] [C],-2.87,COCC(=O)N(C(C)C(=O)OC)c1c(C)cccc1C 
+149,[C] [N] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O] [C] [Branch1] [C] [C] [C],-2.4090000000000003,CNC(=O)Oc1ccccc1OC(C)C
+150,[C] [C] [C] [Branch1] [C] [C] [Cl],-1.94,CCC(C)Cl
+151,[O] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-3.08,Oc1ccc2ccccc2c1
+152,[C] [C] [Branch1] [C] [C] [O] [C] [=C] [C] [=Branch1] [#Branch2] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [Cl] [N] [N] [=C] [Branch1] [=Branch1] [O] [C] [Ring1] [Branch1] [=O] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-5.265,CC(C)Oc1cc(c(Cl)cc1Cl)n2nc(oc2=O)C(C)(C)C
+153,[C] [C] [C] [C] [C] [#C],-1.801,CCCCC#C
+154,[C] [C] [C] [C] [C] [C] [C] [C] [#C],-2.864,CCCCCCCC#C
+155,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl],-3.297,Cc1ccccc1Cl
+156,[C] [C] [Branch1] [C] [C] [O] [C] [Branch1] [C] [C] [C],-1.281,CC(C)OC(C)C
+157,[N] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [C] [N] [C] [=C] [Ring1] [#Branch1],-2.464,Nc1ccc(cc1)S(=O)(=O)c2ccc(N)cc2
+158,[C] [N] [N],0.5429999999999999,CNN
+159,[C] [C] [#C],-0.672,CC#C
+160,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [O] [N] [=C] [Branch1] [Ring1] [C] [#N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.557,CCOP(=S)(OCC)ON=C(C#N)c1ccccc1
+161,[C] [C] [N] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [O] [C] [=Branch1] [N] [=C] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [C] [C] [C] [C],-2.826,CCNP(=S)(OC)OC(=CC(=O)OC(C)C)C
+162,[C] [=C] [C] [=O],-0.184,C=CC=O
+163,[O] [=C] [NH1] [C] [=N] [C] [N] [=C] [NH1] [C] [Ring1] [=Branch2] [=Ring1] [Branch1],-0.6559999999999999,O=c1[nH]cnc2nc[nH]c12 
+164,[O] [C] [=C] [C] [=C] [N] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-2.725,Oc2ccc1ncccc1c2 
+165,[F] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.514,Fc1ccccc1
+166,[C] [C] [C] [Cl],-1.585,CCCCl
+167,[C] [C] [O] [C] [=Branch1] [C] [=O] [C],-0.77,CCOC(=O)C
+168,[C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-2.938,CCCC(C)(C)C
+169,[C] [C] [C] [C] [C] [Branch1] [Branch1] [C] [C] [Ring1] [=Branch1] [C] [Branch1] [C] [C] [Branch1] [C] [C] [O] [Ring1] [#Branch2],-2.579,CC12CCC(CC1)C(C)(C)O2 
+170,[C] [C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C] [C] [C],-4.726,CCCCOC(=O)CCCCCCCCC(=O)OCCCC
+171,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-5.299,Clc1ccc(cc1)c2ccc(Cl)cc2 
+172,[C] [C] [=C] [C] [=C] [N] [=C] [Ring1] [=Branch1] [C],-2.067,Cc1cccnc1C
+173,[C] [C] [=Branch1] [C] [=C] [C] [C] [C] [=C] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2],-2.042,CC(=C)C1CC=C(C)C(=O)C1
+174,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [S] [C] [S] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-5.827999999999999,CCOP(=S)(OCC)SCSc1ccc(Cl)cc1
+175,[C] [O] [C] [=C] [C] [=Branch1] [O] [=C] [C] [Branch1] [Ring1] [O] [C] [=C] [Ring1] [Branch2] [O] [C] [C] [C] [Branch1] [#Branch1] [C] [O] [C] [Ring1] [Branch1] [=O] [C] [Branch2] [Ring1] [#Branch1] [O] [C] [O] [C] [C] [O] [C] [Branch1] [C] [C] [O] [C] [Ring1] [#Branch1] [C] [Branch1] [C] [O] [C] [Ring1] [N] [O] [C] [=C] [C] [O] [C] [O] [C] [=Ring1] [Branch1] [C] [=C] [Ring2] [Ring1] [#C] [Ring1] [=Branch2],-3.292,COc1cc(cc(OC)c1O)C6C2C(COC2=O)C(OC4OC3COC(C)OC3C(O)C4O)c7cc5OCOc5cc67
+176,[C] [=C] [C] [=C] [C] [=C] [C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Branch1] [=N] [C] [=Branch1] [Branch1] [=C] [Ring2] [Ring1] [C] [C] [Ring1] [P] [=Ring1] [=N] [=C] [Ring1] [=Branch2] [Ring1] [=N],-6.007000000000001,c1cc2cccc3c4cccc5cccc(c(c1)c23)c54
+177,[C] [=C] [Branch1] [C] [Br] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [O] [Ring1] [=Branch1],-4.434,c1c(Br)ccc2ccccc12 
+178,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [O] [C] [N] [=C] [Branch1] [C] [Cl] [N] [Branch1] [Ring2] [N] [=Ring1] [=Branch1] [C] [Branch1] [C] [C] [C],-3.76,CCOP(=S)(OCC)Oc1nc(Cl)n(n1)C(C)C
+179,[C] [C] [=Branch1] [C] [=C] [C] [=C],-1.714,CC(=C)C=C
+180,[C] [C] [Branch1] [C] [C] [=C] [C] [C] [C] [Branch1] [C] [O] [Branch1] [C] [C] [C] [=C],-2.399,CC(C)=CCCC(O)(C)C=C
+181,[C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [O] [C] [=C] [C] [=C] [Branch1] [Ring1] [S] [C] [C] [Branch1] [C] [C] [=C] [Ring1] [=Branch2],-4.265,COP(=S)(OC)Oc1ccc(SC)c(C)c1
+182,[O] [C] [C] [C] [C] [C] [C] [Ring1] [=Branch1],-1.261,OC1CCCCC1
+183,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [C] [C] [C] [C] [=C],-1.013,O=C1NC(=O)NC(=O)C1(C)CC=C
+184,[C] [C] [C] [C] [C] [C] [Branch1] [P] [C] [C] [C] [C] [C] [Branch1] [C] [O] [C] [C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [S] [C] [C] [C] [Ring2] [Ring1] [Ring1] [=O],-3.882,CC34CCC1C(CCC2CC(O)CCC12C)C3CCC4=O
+185,[O] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [O],0.647,OCC(O)C(O)C(O)C(O)CO 
+186,[C] [C] [N] [C] [=N] [C] [Branch1] [C] [Cl] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [Ring1] [O],-3.069,CCNc1nc(Cl)nc(NC(C)C)n1
+187,[N] [C] [=Branch1] [C] [=S] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.7009999999999998,NC(=S)Nc1ccccc1
+188,[C] [C] [C] [C] [=Branch1] [C] [=O] [C] [C] [C],-1.62,CCCC(=O)CCC
+189,[C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-1.25,CC(=O)C(C)(C)C
+190,[O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-2.761,Oc1ccc(Cl)cc1
+191,[O] [=C] [C] [C] [C] [C] [C] [Ring1] [=Branch1],-0.996,O=C1CCCCC1
+192,[C] [C] [=C] [C] [=C] [C] [Branch1] [C] [N] [=C] [Ring1] [#Branch1],-1.954,Cc1cccc(N)c1
+193,[Cl] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [C] [#N],-2.019,ClC(Cl)(Cl)C#N
+194,[C] [N] [C] [C] [=N] [N] [Branch2] [Ring1] [Ring1] [C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [F] [Branch1] [C] [F] [F] [C] [=Branch1] [C] [=O] [C] [=Ring1] [P] [Cl],-4.029,CNc2cnn(c1cccc(c1)C(F)(F)F)c(=O)c2Cl
+195,[C] [C] [N] [Branch1] [Ring1] [C] [C] [C] [=N] [C] [Branch1] [C] [Cl] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [Ring1] [O],-3.497,CCN(CC)c1nc(Cl)nc(NC(C)C)n1
+196,[C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [C] [N] [C] [=C] [Ring1] [#Branch1],-2.383,CCOC(=O)c1ccc(N)cc1
+197,[Cl] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-4.083,Clc1ccc(Cl)c(Cl)c1
+198,[C] [C] [=N] [N] [=C] [C] [N] [=C] [Branch1] [#Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [N] [Ring2] [Ring1] [=Branch1] [Ring2] [Ring1] [Ring1],-3.948,Cc3nnc4CN=C(c1ccccc1Cl)c2cc(Cl)ccc2n34
+199,[C] [C] [N] [C] [=N] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring1] [P] [Ring1] [=Branch1],-2.794,CCN2c1ncccc1N(C)C(=O)c3cccnc23 
+200,[C] [S] [C],-0.758,CSC
+201,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Br],-3.667,Cc1ccccc1Br
+202,[C] [C] [O] [C] [=Branch1] [C] [=O] [N],-0.218,CCOC(=O)N
+203,[C] [C] [=Branch1] [C] [=O] [O] [C] [Branch2] [Ring2] [Ring2] [C] [C] [C] [C] [C] [=C] [Branch1] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [=N] [C] [C] [C] [Ring2] [Ring1] [Ring2] [Ring1] [P] [C] [C] [Branch1] [C] [C] [=O],-4.417,CC(=O)OC3(CCC4C2C=C(C)C1=CC(=O)CCC1(C)C2CCC34C)C(C)=O 
+204,[C] [C] [Branch1] [C] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [C] [C],-1.6469999999999998,CC(C)C(O)C(C)C
+205,[C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-3.468,c1ccc2ccccc2c1
+206,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [Branch1] [Ring2] [N] [Ring1] [=Branch1] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.057,O=C1NC(=O)C(N1)(c2ccccc2)c3ccccc3
+207,[C] [O] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [=N] [C] [=C] [C] [=C] [Branch1] [Ring1] [O] [C] [C] [=C] [Ring1] [Branch2] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-5.537999999999999,COc1ccc(cc1)C(c2ccc(OC)cc2)C(Cl)(Cl)Cl
+208,[O] [=C] [N] [Branch1] [O] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [Ring2] [N] [Ring1] [=C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.81,O=C1N(COC(=O)CCCC)C(=O)C(N1)(c2ccccc2)c3ccccc3
+209,[O] [C] [Branch1] [=Branch2] [C] [N] [C] [=N] [C] [=N] [Ring1] [Branch1] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [F] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [F],-3.569,OC(Cn1cncn1)(c2ccc(F)cc2)c3ccccc3F
+210,[C] [C] [=Branch1] [C] [=O] [O] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [O] [C] [Branch2] [Ring2] [#Branch1] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [Branch1] [C] [F] [C] [Branch1] [C] [O] [C] [C] [Ring2] [Ring1] [=Branch1] [Ring2] [Ring1] [C] [C] [O] [C] [Branch1] [C] [C] [=O],-3.876,CC(=O)OCC(=O)C3(O)C(CC4C2CCC1=CC(=O)C=CC1(C)C2(F)C(O)CC34C)OC(C)=O 
+211,[C] [C] [C] [C] [Br],-2.303,CCCCBr
+212,[Br] [C] [=C] [C] [Branch1] [C] [Br] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [Branch2] [Br],-6.001,Brc1cc(Br)c(Br)cc1Br
+213,[C] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [C],-1.1840000000000002,CC(C)CC(=O)C
+214,[C] [C] [S] [C] [=Branch1] [C] [=O] [N] [Branch1] [Ring1] [C] [C] [C] [C] [C] [C] [C] [C] [Ring1] [=Branch1],-3.35,CCSC(=O)N(CC)C1CCCCC1 
+215,[C] [C] [Branch1] [C] [C] [C] [Branch1] [#Branch1] [C] [=C] [Branch1] [C] [Br] [Br] [C] [Ring1] [Branch2] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [Ring1] [C] [#N] [C] [=C] [C] [=C] [C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [Ring1] [=N],-7.44,CC1(C)C(C=C(Br)Br)C1C(=O)OC(C#N)c2cccc(Oc3ccccc3)c2
+216,[C] [C] [C] [Branch1] [C] [C] [C] [Branch1] [Ring2] [C] [C] [=C] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [O] [=O],-2.06,CCC(C)C1(CC=C)C(=O)NC(=O)NC1=O
+217,[I] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-4.888999999999999,Ic1cccc2ccccc12
+218,[O] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [O],0.647,OCC(O)C(O)C(O)C(O)CO
+219,[C] [C] [S],-0.968,CCS
+220,[Cl] [C] [C] [Branch1] [C] [Cl] [Cl],-1.961,ClCC(Cl)Cl
+221,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [Branch1] [C] [C] [=N] [N] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.141,CN(C)C(=O)Oc1cc(C)nn1c2ccccc2
+222,[N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O],-1.942,NC(=O)c1ccccc1O
+223,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [=Branch1] [C] [Branch1] [C] [C] [C] [C] [Branch1] [C] [C] [C],-1.942,O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C
+224,[C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C],-3.2960000000000003,CCc1ccccc1C
+225,[C] [C] [C] [C] [C] [C] [C] [Cl],-3.003,CCCCCCCCl
+226,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring1] [C] [C] [C] [C],-1.265,O=C1NC(=O)NC(=O)C1(CC)CC
+227,[C] [Branch1] [#Branch2] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.301,C(Cc1ccccc1)c2ccccc2
+228,[C] [C] [N] [C] [=C] [C] [Branch1] [Ring1] [O] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [=Branch2] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring2] [Ring1] [Ring1] [Ring1] [=Branch1],-4.228,CCN2c1cc(OC)cc(C)c1NC(=O)c3cccnc23 
+229,[C] [C] [=C] [C] [=C] [C] [Branch1] [=N] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Ring1] [#Branch2] [=C] [Ring1] [=C],-4.87,Cc1ccc2c(ccc3ccccc32)c1
+230,[C] [C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [O] [C] [C] [C] [C],-4.378,CCCCOC(=O)c1ccccc1C(=O)OCCCC 
+231,[C] [O] [C] [=C] [Branch1] [C] [O] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch2] [Cl],-4.299,COc1c(O)c(Cl)c(Cl)c(Cl)c1Cl 
+232,[C] [C] [C] [C] [C] [C] [=Branch1] [S] [=C] [C] [C] [=C] [C] [Branch1] [C] [O] [=C] [C] [=C] [Ring1] [O] [Ring1] [#Branch1] [C] [Ring1] [#C] [C] [C] [C] [Ring2] [Ring1] [C] [=O],-3.555,CC34CCC1C(=CCc2cc(O)ccc12)C3CCC4=O
+233,[C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [N] [N] [Branch1] [Ring1] [C] [=O] [C] [=N] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [Ring1] [O] [C] [=N] [Ring1] [=Branch2],-3.719,CCOC(=O)c1ccccc1S(=O)(=O)NN(C=O)c2nc(Cl)cc(OC)n2
+234,[C] [C] [C] [C] [C] [C] [Cl],-2.648,CCCCCCCl
+235,[Cl] [C] [=C] [C] [=Branch1] [=N] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-8.468,Clc1cc(c(Cl)c(Cl)c1Cl)c2cc(Cl)c(Cl)c(Cl)c2Cl
+236,[O] [C] [C] [O] [C] [Branch1] [Ring1] [C] [O] [Branch2] [Ring2] [#Branch2] [O] [C] [O] [C] [Branch2] [Ring1] [=Branch1] [C] [O] [C] [O] [C] [Branch1] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch2] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [Branch1] [O] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [#C] [O],0.496,OCC1OC(CO)(OC2OC(COC3OC(CO)C(O)C(O)C3O)C(O)C(O)C2O)C(O)C1O
+237,[C] [C] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring1] [P] [Ring1] [=Branch1],-3.471,CCN2c1ccccc1N(C)C(=O)c3cccnc23 
+238,[C] [C] [Branch1] [C] [Cl] [Cl],-1.5759999999999998,CC(Cl)Cl
+239,[N] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [S] [Branch1] [C] [N] [=Branch1] [C] [=O] [=O],-0.954,Nc1ccc(cc1)S(N)(=O)=O
+240,[Cl] [C] [C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [Ring1] [#Branch2] [Cl],-4.009,ClC1C(Cl)C(Cl)C(Cl)C(Cl)C1Cl
+241,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [C] [C] [C],-4.538,CCOP(=S)(NC(C)C)Oc1ccccc1C(=O)OC(C)C
+242,[Cl] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [Cl],-4.008,Clc1cccc(Cl)c1Cl
+243,[Cl] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-2.607,ClC(Cl)(Cl)Cl
+244,[C] [C] [Branch1] [C] [O] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [C] [C] [Ring1] [S] [Ring2] [Ring1] [Ring2] [C],-4.073,CC1(O)CCC2C3CCC4=CC(=O)CCC4(C)C3CCC21C
+245,[C] [C] [O] [C] [=C] [C] [=C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [N] [=O] [C] [=C] [Ring1] [#Branch2],-2.167,CCOc1ccc(NC(N)=O)cc1
+246,[C] [/C] [C] [C] [C] [Branch1] [C] [\C] [C] [C] [Ring1] [#Branch1],-3.305,C/C1CCC(\C)CC1
+247,[C] [=C] [N] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=N] [C] [=C] [C] [=C] [Ring1] [O] [Ring1] [=Branch1],-2.994,c1cnc2c(c1)ccc3ncccc23
+248,[C] [O] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-0.984,COC(C)(C)C
+249,[C] [O] [C] [=C] [C] [=C] [Branch1] [Ring2] [C] [=C] [C] [C] [=C] [Ring1] [=Branch2],-3.254,COc1ccc(C=CC)cc1
+250,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [O],-4.94,CCCCCCCCCCCCCCCCO
+251,[O] [=C] [C] [=C] [NH1] [C] [=Branch1] [C] [=O] [NH1] [Ring1] [#Branch1],-0.441,O=c1cc[nH]c(=O)[nH]1 
+252,[N] [C] [=N] [C] [=N] [C] [N] [=C] [NH1] [C] [Ring1] [=Branch2] [=Ring1] [Branch1],-1.255,Nc1ncnc2nc[nH]c12 
+253,[C] [O] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [O] [Branch1] [=Branch1] [C] [C] [C] [Ring1] [Ring1] [C] [=C] [N] [=C] [N] [=C] [Ring1] [=Branch1],-2.181,COc1ccc(cc1)C(O)(C2CC2)c3cncnc3 
+254,[C] [C] [C] [Branch1] [#C] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [Branch2] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.272,CCC1(C(=O)NC(=O)NC1=O)c2ccccc2 
+255,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [Cl],-5.686,Clc1ccc(cc1)c2cccc(Cl)c2Cl 
+256,[C] [C] [Branch1] [C] [C] [C] [=C] [C] [=C] [Branch1] [O] [N] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C] [C] [=C] [Ring1] [N],-2.867,CC(C)c1ccc(NC(=O)N(C)C)cc1
+257,[C] [C] [N] [Branch1] [Ring1] [C] [C] [C] [=Branch1] [C] [=O] [C] [S] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [N] [=N] [Ring1] [#Branch1],-2.231,CCN(CC)C(=O)CSc1ccc(Cl)nn1
+258,[C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [O],-1.365,CCC(C)(C)CO
+259,[C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [C],-1.899,CCCOC(=O)CCC
+260,[C] [C] [Branch1] [C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [#Branch1] [O] [C] [Branch1] [C] [C] [C] [S] [C] [C] [N] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.99,CC(C)OP(=S)(OC(C)C)SCCNS(=O)(=O)c1ccccc1
+261,[C] [C] [C] [C] [C] [C] [C] [Ring1] [#Branch1],-2.9160000000000004,C1CCCCCC1
+262,[C] [C] [C] [O] [C] [=O],-0.757,CCCOC=O
+263,[C] [C] [Branch1] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C],-3.585,CC(C)c1ccccc1C
+264,[N] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1],-2.392,Nc1cccc(Cl)c1
+265,[C] [C] [Branch1] [C] [C] [C] [C] [Branch1] [C] [C] [C],-2.938,CC(C)CC(C)C
+266,[C] [C] [O] [C] [O] [C] [=C] [C] [=C] [Branch1] [#Branch2] [O] [S] [Branch1] [C] [C] [=Branch1] [C] [=O] [=O] [C] [=C] [Ring1] [O] [C] [Ring1] [=C] [Branch1] [C] [C] [C],-3.184,CCOC2Oc1ccc(OS(C)(=O)=O)cc1C2(C)C
+267,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [F] [Branch1] [C] [F] [F],-3.065,CN(C)C(=O)Nc1cccc(c1)C(F)(F)F
+268,[O] [C] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [=Branch2] [O],0.501,OCC1OC(O)C(O)C(O)C1O
+269,[C] [C] [=C] [C] [=C] [C] [Branch1] [C] [O] [=C] [Ring1] [#Branch1],-2.313,Cc1cccc(O)c1
+270,[C] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Ring1] [=Branch2] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=Branch1] [Ring2] [=C] [Ring1] [#Branch1] [S] [Branch1] [C] [N] [=Branch1] [C] [=O] [=O],-4.345,CC2Cc1ccccc1N2NC(=O)c3ccc(Cl)c(c3)S(N)(=O)=O 
+271,[C] [C] [C] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C] [C] [Branch1] [C] [C] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [C] [Branch1] [P] [C] [C] [C] [C] [C] [Branch1] [C] [O] [C] [C] [=Branch1] [C] [=O] [O] [Ring1] [Branch2] [C] [Ring2] [Ring1] [=Branch1] [Ring1] [P],-4.731,CCC(C)C(=O)OC2CC(C)C=C3C=CC(C)C(CCC1CC(O)CC(=O)O1)C23 
+272,[C] [C] [C] [Branch1] [#C] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [Branch2] [=O] [C] [=C] [C] [C] [C] [C] [C] [Ring1] [#Branch1] [C] [Ring1] [Branch1],-2.781,CCC1(C(=O)NC(=O)NC1=O)C2=CCC3CCC2C3
+273,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C],-3.671,CCCCCCCCCC(=O)OCC
+274,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.847,CN(C)C(=O)Nc1ccccc1
+275,[C] [C] [C] [O] [C] [C],-1.072,CCCOCC
+276,[C] [C] [Branch1] [C] [C] [O],-0.261,CC(C)O
+277,[C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-3.802,Cc1ccc2ccccc2c1
+278,[Cl] [C] [Branch1] [C] [Br] [Br],-2.54,ClC(Br)Br
+279,[C] [C] [C] [Branch1] [P] [C] [Branch1] [Ring1] [C] [C] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1],-4.854,CCC(C(CC)c1ccc(O)cc1)c2ccc(O)cc2
+280,[C] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [N] [S] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [O] [C] [C] [=Branch1] [C] [=O] [O] [C] [C],-3.391,CCOC(=O)CC(SP(=S)(OC)OC)C(=O)OCC
+281,[Cl] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.887,ClCc1ccccc1
+282,[C] [/C] [=C] [/C] [=O],-0.604,C/C=C/C=O
+283,[C] [O] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Br] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-3.938,CON(C)C(=O)Nc1ccc(Br)c(Cl)c1
+284,[C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Branch1] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#C] [Ring1] [=Branch1],-5.228,Cc1c2ccccc2c(C)c3ccccc13
+285,[C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C],-1.899,CCCCCC(=O)OC
+286,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [N] [N] [=C] [Branch1] [=Branch1] [O] [C] [Ring1] [Branch1] [=O] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-3.831,CN(C)C(=O)Nc1ccc(c(Cl)c1)n2nc(oc2=O)C(C)(C)C
+287,[C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [F] [C] [=C] [Ring1] [#Branch1],-2.181,CC(=O)Nc1ccc(F)cc1
+288,[C] [=C] [N] [=C] [N] [=C] [Ring1] [=Branch1],-0.884,c1cncnc1
+289,[C] [C] [C] [=Branch1] [C] [=O] [O] [C],-0.836,CCC(=O)OC
+290,[C] [N] [C] [C] [Branch1] [C] [O] [N] [Branch1] [Branch1] [C] [Ring1] [=Branch1] [=O] [C] [=N] [N] [=C] [Branch1] [Ring2] [S] [Ring1] [Branch1] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-2.398,CN1CC(O)N(C1=O)c2nnc(s2)C(C)(C)C
+291,[I] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.8,Ic1ccccc1
+292,[C] [C] [N] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch2] [Ring1] [Branch1] [C] [=C] [Ring1] [#Branch1] [C] [=Branch1] [C] [=O] [N] [Ring1] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [S] [Branch1] [C] [N] [=Branch1] [C] [=O] [=O],-3.777,CC2Nc1cc(Cl)c(cc1C(=O)N2c3ccccc3C)S(N)(=O)=O 
+293,[C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O] [C] [C] [Branch1] [C] [O] [C] [O] [C] [Branch1] [C] [N] [=O],-1.4280000000000002,COc1ccccc1OCC(O)COC(N)=O
+294,[C] [C] [C] [C] [O] [C] [N] [Branch1] [#Branch1] [C] [=Branch1] [C] [=O] [C] [Cl] [C] [=C] [Branch1] [Ring1] [C] [C] [C] [=C] [C] [=C] [Ring1] [Branch2] [C] [C],-4.347,CCCCOCN(C(=O)CCl)c1c(CC)cccc1CC 
+295,[O] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [Cl],-3.144,Oc1cccc(Cl)c1Cl
+296,[C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C],-1.1909999999999998,CCCC(=O)OC
+297,[C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-3.644,CCC(=O)Nc1ccc(Cl)c(Cl)c1
+298,[N] [C] [=N] [C] [Branch1] [C] [N] [=C] [N] [=C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [N] [=N] [C] [Ring1] [=N] [=N] [Ring2] [Ring1] [C],-3.051,Nc3nc(N)c2nc(c1ccccc1)c(N)nc2n3
+299,[C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C],-2.254,CCCCCC(=O)OCC
+300,[O] [C] [C] [Branch1] [C] [O] [C] [O] [C] [O] [C] [Branch1] [=Branch2] [O] [C] [Ring1] [Branch1] [C] [Ring1] [Branch2] [O] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-1.887,OCC(O)C2OC1OC(OC1C2O)C(Cl)(Cl)Cl 
+301,[C] [O] [C] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [Ring1] [=C],-3.448,COc1nc(NC(C)C)nc(NC(C)C)n1
+302,[C] [C] [C] [C] [C] [C] [C] [=C],-3.073,CCCCCCC=C
+303,[C] [C] [=C] [C] [=C] [Branch1] [C] [N] [C] [=C] [Ring1] [#Branch1],-1.954,Cc1ccc(N)cc1
+304,[N] [C] [=N] [C] [=C] [S] [Ring1] [Branch1],-1.226,Nc1nccs1 
+305,[C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O] [C] [=Branch1] [C] [=O] [N] [C],-1.947,c1ccccc1(OC(=O)NC)
+306,[C] [C] [C] [C] [Branch1] [C] [O] [C] [C],-1.324,CCCC(O)CC
+307,[C] [=C] [C] [=C] [C] [Branch1] [C] [O] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [Ring1] [O] [=C] [Ring1] [#C],-4.148,c3ccc2c(O)c1ccccc1cc2c3 
+308,[C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [Ring1] [#Branch2] [=C] [Ring1] [=C],-4.87,Cc1ccc2cc3ccccc3cc2c1
+309,[C] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1] [C],-3.312,Cc1cccc(C)c1C
+310,[C] [N] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [=C] [Branch1] [=Branch1] [N] [Branch1] [C] [C] [C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch2],-2.677,CNC(=O)Oc1ccc(N(C)C)c(C)c1
+311,[C] [C] [C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [O],-2.387,CCCCCCCC(C)O
+312,[C] [N] [Branch2] [Ring1] [Branch1] [C] [=Branch1] [C] [=O] [N] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.863,CN(C(=O)NC(C)(C)c1ccccc1)c2ccccc2
+313,[C] [C] [C] [C] [=Branch1] [C] [=O] [C] [C],-1.266,CCCC(=O)CC
+314,[O] [C] [=C] [Branch1] [C] [Br] [C] [=C] [Branch1] [Ring1] [C] [#N] [C] [=C] [Ring1] [=Branch2] [Br],-3.793,Oc1c(Br)cc(C#N)cc1Br 
+315,[Cl] [C] [=C] [C] [=C] [Branch1] [=Branch1] [C] [=C] [Ring1] [=Branch1] [Cl] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-5.223,Clc1ccc(cc1Cl)c2ccccc2 
+316,[C] [N] [Branch2] [Ring1] [Ring2] [C] [=Branch1] [C] [=O] [C] [O] [C] [=N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [S] [Ring1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.504,CN(C(=O)COc1nc2ccccc2s1)c3ccccc3
+317,[O] [C] [=C] [C] [=C] [C] [=N] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-2.725,Oc1cccc2ncccc12 
+318,[C] [C] [=C] [Branch1] [#Branch1] [S] [C] [C] [O] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.927,CC1=C(SCCO1)C(=O)Nc2ccccc2
+319,[C] [C] [O] [C] [=C] [C] [=C] [N] [=C] [Branch1] [Branch2] [S] [C] [Ring1] [Branch1] [=C] [Ring1] [=Branch2] [S] [Branch1] [C] [N] [=Branch1] [C] [=O] [=O],-3.085,CCOc2ccc1nc(sc1c2)S(N)(=O)=O 
+320,[O] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch2] [Cl],-4.835,Oc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl
+321,[Cl] [C] [Br],-1.519,ClCBr
+322,[C] [C] [=Branch1] [C] [=O] [O] [C] [C] [=Branch1] [C] [=O] [C] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [C] [C] [Ring2] [Ring1] [Ring1] [Ring1] [S] [C],-4.472,CC(=O)OCC(=O)C3CCC4C2CCC1=CC(=O)CCC1(C)C2CCC34C 
+323,[N] [C] [=Branch1] [C] [=O] [N] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.509,NC(=O)NCc1ccccc1 
+324,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-3.048,CN(C)C(=O)Nc1ccc(C)c(Cl)c1
+325,[C] [O] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-3.5810000000000004,CON(C)C(=O)Nc1ccc(Cl)c(Cl)c1
+326,[O] [C] [C] [C] [C] [C] [C] [C] [Ring1] [#Branch1],-1.7,OC1CCCCCC1
+327,[C] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [O] [C] [Branch1] [Ring1] [C] [O] [N] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [Cl] [Cl],-1.936,CS(=O)(=O)c1ccc(cc1)C(O)C(CO)NC(=O)C(Cl)Cl 
+328,[C] [C] [C] [C] [Branch1] [C] [C] [C] [Branch1] [Ring1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=S] [N] [C] [Ring1] [#Branch2] [=O],-2.96,CCCC(C)C1(CC)C(=O)NC(=S)NC1=O 
+329,[C] [C] [=Branch1] [C] [=O] [N] [C] [=N] [N] [=C] [Branch1] [Ring2] [S] [Ring1] [Branch1] [S] [Branch1] [C] [N] [=Branch1] [C] [=O] [=O],-0.7929999999999999,CC(=O)Nc1nnc(s1)S(N)(=O)=O 
+330,[Cl] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [C] [C] [C] [Branch1] [Branch1] [C] [=C] [Ring1] [Branch1] [C] [Ring1] [#Branch1] [C] [Ring1] [=N] [Branch1] [C] [Cl] [C] [Ring1] [N] [Branch1] [C] [Cl] [Cl],-5.511,ClC1=C(Cl)C2(Cl)C3C4CC(C=C4)C3C1(Cl)C2(Cl)Cl
+331,[C] [C] [C] [O] [C] [Ring1] [Branch1],-0.62,C1CCOC1
+332,[Cl] [C] [=C] [C] [=C] [C] [=Branch1] [Branch1] [=C] [Ring1] [=Branch1] [Cl] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [Cl],-6.079,Clc1cccc(c1Cl)c2cccc(Cl)c2Cl
+333,[C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [S] [N] [Branch1] [Branch1] [C] [Ring1] [=Branch1] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.0760000000000005,CCCCC1C(=O)N(N(C1=O)c2ccccc2)c3ccccc3 
+334,[C] [C] [N] [Branch1] [Ring1] [C] [C] [C] [=N] [C] [Branch1] [C] [Cl] [=N] [C] [=Branch1] [Ring2] [=N] [Ring1] [#Branch1] [N] [Branch1] [Ring1] [C] [C] [C] [C],-3.663,CCN(CC)c1nc(Cl)nc(n1)N(CC)CC
+335,[Cl] [C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [S] [N] [C] [=Branch1] [C] [=O] [C] [C] [C] [=C] [C] [C] [Ring1] [=Branch1] [C] [Ring1] [#Branch2] [=O],-4.365,ClC(Cl)C(Cl)(Cl)SN2C(=O)C1CC=CCC1C2=O 
+336,[O] [C] [N] [=C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [N] [C] [Ring2] [Ring1] [C] [=O],-3.517,OC3N=C(c1ccccc1)c2cc(Cl)ccc2NC3=O 
+337,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Branch2] [C] [Branch1] [C] [C] [C] [C] [C] [C] [C] [=C],-2.415,O=C1NC(=O)NC(=O)C1(C(C)CCC)CC=C
+338,[C] [Branch1] [C] [O] [=C] [Branch1] [C] [C] [C] [=C] [C] [Branch1] [=Branch1] [C] [Branch1] [C] [C] [C] [=C] [Ring1] [O],-3.224,c1(O)c(C)ccc(C(C)C)c1
+339,[Cl] [C] [Branch1] [P] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [Ring1] [Branch2] [Cl] [C] [Branch1] [C] [Cl] [C] [Ring1] [=Branch2] [Branch1] [C] [Cl] [C] [Ring1] [=Branch2] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [C] [Ring1] [N] [Ring1] [#Branch2] [Cl],-5.112,ClC1(C(=O)C2(Cl)C3(Cl)C14Cl)C5(Cl)C2(Cl)C3(Cl)C(Cl)(Cl)C45Cl
+340,[C] [C] [N] [Branch1] [Ring1] [C] [C] [C] [=Branch1] [C] [=S] [S] [S] [C] [=Branch1] [C] [=S] [N] [Branch1] [Ring1] [C] [C] [C] [C],-3.862,CCN(CC)C(=S)SSC(=S)N(CC)CC
+341,[C] [C] [C] [C] [C] [C] [Ring1] [=Branch1],-2.477,C1CCCCC1
+342,[Cl] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [Branch1] [=Branch2] [C] [=Branch1] [Branch1] [=C] [Ring1] [#Branch1] [Cl] [Cl] [C] [Branch1] [C] [Cl] [C] [=Branch1] [=N] [=C] [Branch1] [C] [Cl] [C] [=Branch1] [Branch1] [=C] [Ring1] [#Branch1] [Cl] [Cl] [Cl],-7.848,ClC1=C(Cl)C(Cl)(C(=C1Cl)Cl)C2(Cl)C(=C(Cl)C(=C2Cl)Cl)Cl
+343,[C] [C] [C] [C] [C] [=C] [Branch1] [S] [C] [=C] [C] [=C] [C] [Branch1] [C] [O] [=C] [C] [=C] [Ring1] [O] [Ring1] [#Branch1] [C] [Ring1] [#C] [C] [C] [C] [Ring2] [Ring1] [C] [=O],-3.927,CC34CCc1c(ccc2cc(O)ccc12)C3CCC4=O
+344,[C] [C] [C] [C] [C] [C] [C] [C] [O],-2.105,CCCCCCCCO
+345,[C] [C] [S] [C] [C],-1.598,CCSCC
+346,[Cl] [C] [C] [Cl],-1.374,ClCCCl
+347,[C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [Cl],-2.278,CCC(C)(C)Cl
+348,[Cl] [C] [C] [Br],-1.7380000000000002,ClCCBr
+349,[C] [C] [N] [C] [=N] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Branch1] [Ring1] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring2] [Ring1] [C] [Ring1] [=Branch1],-3.125,CCN2c1ncccc1N(CC)C(=O)c3cccnc23 
+350,[Cl] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.975,Clc1ccccc1
+351,[C] [C] [C] [C] [C] [C] [C] [C] [=C],-3.427,CCCCCCCC=C
+352,[Br] [C] [=C] [C] [=C] [Branch1] [C] [I] [C] [=C] [Ring1] [#Branch1],-4.754,Brc1ccc(I)cc1
+353,[C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [O] [C] [C],-1.308,CCC(C)(O)CC
+354,[C] [C] [C] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.899,CCCCCc1ccccc1
+355,[C] [C] [=Branch1] [C] [=O] [O] [C] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [C] [C] [Ring2] [Ring1] [Ring1] [Ring1] [S] [C],-4.449,CC(=O)OC3CCC4C2CCC1=CC(=O)CCC1(C)C2CCC34C 
+356,[C] [C] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [P] [Ring1] [=Branch1],-4.007,CCN2c1ccccc1N(C)C(=O)c3ccccc23 
+357,[C] [C] [C] [C] [C] [Branch1] [C] [C] [O],-1.324,CCCCC(C)O
+358,[C] [C] [C] [C] [C] [C] [C] [C] [Ring1] [Branch1],-3.16,CCCC1CCCC1
+359,[C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [N] [=C] [N] [Ring1] [Branch1] [C] [Branch1] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.359,CCOC(=O)c1cncn1C(C)c2ccccc2
+360,[C] [=C] [C] [=C] [O] [C] [=N] [C] [Ring1] [Branch1] [=C] [Ring1] [=Branch2],-2.214,c2ccc1ocnc1c2
+361,[C] [C] [C] [C] [C] [O],-1.042,CCCCCO
+362,[C] [C] [N] [Branch1] [Ring1] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.16,CCN(CC)c1ccccc1
+363,[F] [C] [=C] [C] [=C] [C] [Branch1] [C] [F] [=C] [Ring1] [#Branch1],-2.636,Fc1cccc(F)c1
+364,[Cl] [C] [C] [C] [#N],-0.522,ClCCC#N 
+365,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring1] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.272,O=C1NC(=O)NC(=O)C1(CC)c1ccccc1
+366,[C] [N] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [=C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [O] [C] [Ring1] [O] [=Ring1] [#Branch1],-3.05,CNC(=O)Oc1cccc2CC(C)(C)Oc12
+367,[C] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1] [O],-2.589,Cc1cccc(C)c1O
+368,[C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.033,c1ccccc1C(O)c2ccccc2
+369,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C],-3.3160000000000003,CCCCCCCCCC(=O)OC
+370,[C] [C] [Branch1] [C] [C] [C] [Br],-2.2880000000000003,CC(C)CBr
+371,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [=N] [C] [=Branch1] [#Branch2] [=N] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1] [C] [N] [Branch1] [C] [C] [C],-2.34,CN(C)C(=O)Oc1nc(nc(C)c1C)N(C)C
+372,[C] [C] [C] [C] [C] [C] [Br],-3.012,CCCCCCBr
+373,[C] [C] [C] [C] [Branch1] [C] [C] [C],-2.6,CCCC(C)C
+374,[C] [C] [=C] [Branch1] [C] [F] [C] [Branch1] [C] [F] [=C] [Branch2] [Ring1] [#C] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [F] [Branch1] [C] [F] [F] [C] [Ring1] [#Branch2] [Branch1] [C] [C] [C] [C] [Branch1] [C] [F] [=C] [Ring2] [Ring1] [=Branch2] [F],-6.339,Cc1c(F)c(F)c(COC(=O)C2C(C=C(Cl)C(F)(F)F)C2(C)C)c(F)c1F
+375,[C] [C] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1] [N] [Branch1] [Branch2] [C] [Branch1] [C] [C] [C] [O] [C] [C] [=Branch1] [C] [=O] [C] [Cl],-3.431,CCc1cccc(C)c1N(C(C)COC)C(=O)CCl 
+376,[N] [C] [N] [=C] [NH1] [N] [=Ring1] [Branch1],-0.674,Nc1nc[nH]n1
+377,[Br] [C] [Branch1] [C] [Br] [Br],-2.904,BrC(Br)Br
+378,[C] [O] [P] [=Branch1] [C] [=O] [Branch1] [Ring1] [O] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-1.866,COP(=O)(OC)C(O)C(Cl)(Cl)Cl
+379,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [S] [C] [N] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [O] [Ring1] [#Branch1],-5.024,CCOP(=S)(OCC)SCn1c(=O)oc2cc(Cl)ccc12
+380,[O] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.699,OCc1ccccc1
+381,[O] [=C] [C] [Branch1] [#C] [C] [C] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Ring1] [#Branch2] [=C] [Branch1] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O] [Ring2] [Ring1] [Branch1],-5.194,O=c2c(C3CCCc4ccccc43)c(O)c1ccccc1o2 
+382,[O] [C] [=C] [C] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [#Branch1],-3.132,Oc1ccc(Br)cc1
+383,[C] [C] [Branch1] [C] [C] [Br],-1.949,CC(C)Br
+384,[C] [C] [Branch1] [C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-3.276,CC(C)CC(C)(C)C
+385,[O] [C] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-6.268,OC(c1ccc(Cl)cc1)(c2ccc(Cl)cc2)C(Cl)(Cl)Cl
+386,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [S] [C] [Branch1] [Ring1] [C] [Cl] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Ring1] [#Branch2] [=O],-5.026,CCOP(=S)(OCC)SC(CCl)N2C(=O)c1ccccc1C2=O
+387,[Br] [C] [=C] [C] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [#Branch1],-4.298,Brc1ccc(Br)cc1
+388,[C] [N] [C] [=Branch1] [C] [=O] [O] [N] [Branch1] [#C] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [C] [Ring1] [=C] [=O],-3.6010000000000004,Cn2c(=O)on(c1ccc(Cl)c(Cl)c1)c2=O
+389,[O] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.701,Oc1ccc(cc1)c2ccccc2
+390,[C] [C] [=C] [Branch1] [#Branch1] [C] [C] [C] [O] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.83,CC1=C(CCCO1)C(=O)Nc2ccccc2 
+391,[C] [C] [O] [C] [=C],-0.857,CCOC=C
+392,[C] [O] [C] [=N] [C] [=N] [C] [=N] [C] [=C] [N] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-1.589,COc1ncnc2nccnc12 
+393,[C] [C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [O] [C] [C],-2.017,CCCCC(C)(O)CC
+394,[O] [=C] [N] [Branch1] [Branch2] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [Ring2] [N] [Ring1] [O] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.7230000000000003,O=C1N(COC(=O)C)C(=O)C(N1)(c2ccccc2)c3ccccc3
+395,[C] [O] [C] [=N] [C] [=C] [N] [=C] [C] [=N] [C] [Ring1] [=Branch1] [=N] [Ring1] [#Branch2],-1.589,COc2ncc1nccnc1n2
+396,[C] [N] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [O] [C] [C] [O] [Ring1] [Branch1],-1.614,CNC(=O)Oc1ccccc1C2OCCO2
+397,[C] [N] [Branch1] [N] [C] [=Branch1] [C] [=O] [N] [C] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [=N],-1.508,C1N(C(=O)NCC(C)C)C(=O)NC1
+398,[C] [C] [O] [C] [=Branch1] [C] [=O] [N] [C] [C] [O] [C] [=C] [C] [=C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [Ring1] [=N],-4.662,CCOC(=O)NCCOc2ccc(Oc1ccccc1)cc2
+399,[C] [C] [=Branch1] [C] [=O] [N] [Branch1] [S] [S] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [C] [N] [C] [=C] [Ring1] [#Branch1] [C] [O] [N] [=C] [Branch1] [C] [C] [C] [=Ring1] [=Branch1] [C],-2.024,CC(=O)N(S(=O)c1ccc(N)cc1)c2onc(C)c2C 
+400,[Cl] [C] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-2.794,ClCC(Cl)(Cl)Cl
+401,[C] [C] [C] [C] [C] [C] [C] [Ring1] [=Branch1] [N] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.779,CC1CCCCC1NC(=O)Nc2ccccc2
+402,[Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-4.159,Clc1cc(Cl)cc(Cl)c1
+403,[O] [=C] [C] [=C] [C] [=C] [O] [Ring1] [Branch1],-1.391,O=Cc1ccco1
+404,[C] [C] [Branch1] [C] [C] [C] [C] [O],-1.027,CC(C)CCO
+405,[O] [=C] [C] [=C] [C] [=C] [O] [C] [O] [C] [Ring1] [Branch1] [=C] [Ring1] [=Branch2],-2.033,O=Cc2ccc1OCOc1c2 
+406,[C] [C] [=Branch1] [C] [=C] [C],-1.5730000000000002,CC(=C)C
+407,[O] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.999,O=Cc1ccccc1
+408,[C] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [N] [Branch2] [Ring1] [=C] [S] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [=C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [O] [C] [=Ring1] [#Branch1] [Ring1] [O] [C] [Branch1] [C] [C] [C],-5.132999999999999,CCOC(=O)CCN(SN(C)C(=O)Oc1cccc2CC(C)(C)Oc21)C(C)C
+409,[O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring1] [P] [Ring1] [=Branch1],-2.7710000000000004,O2c1ccccc1N(C)C(=O)c3cccnc23 
+410,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=N] [Ring1] [=Branch1],-4.125,C1c2ccccc2c3ccccc13
+411,[C] [C] [C] [C] [C] [C] [C] [Ring1] [=Branch1],-2.891,CC1CCCCC1
+412,[N] [C] [=Branch1] [C] [=N] [N] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [C] [N] [C] [=C] [Ring1] [#Branch1],-0.706,NC(=N)NS(=O)(=O)c1ccc(N)cc1 
+413,[C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1],-2.441,COC(=O)c1ccc(O)cc1
+414,[C] [C] [C] [C] [C] [O] [Ring1] [Branch1],-1.034,CC1CCCO1
+415,[C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=Branch1] [N] [=C] [Ring1] [Branch2] [C] [Ring1] [N] [O] [C] [Ring1] [#C] [=O] [C],-2.43,CC3C2CCC1(C)C=CC(=O)C(=C1C2OC3=O)C
+416,[O] [C] [C] [O] [C] [Branch1] [N] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [P] [O],-0.975,OCC2OC(Oc1ccccc1CO)C(O)C(O)C2O
+417,[C] [C] [N] [C] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [C] [Branch1] [Ring1] [S] [C] [=N] [Ring1] [N],-3.43,CCNc1nc(NC(C)C)nc(SC)n1
+418,[C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [O] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [C] [C] [Ring1] [S] [Ring2] [Ring1] [Ring2] [C],-3.876,CC(=O)C1(O)CCC2C3CCC4=CC(=O)CCC4(C)C3CCC21C
+419,[C] [C] [C] [C] [Branch1] [C] [C] [O],-0.97,CCCC(C)O
+420,[O] [C] [Branch1] [=N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.148,OC(C(=O)c1ccccc1)c2ccccc2
+421,[C] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [Branch1] [C] [C] [=C] [Ring1] [Branch2],-2.6210000000000004,Cc1ccc(O)c(C)c1
+422,[C] [C] [=C] [Branch1] [C] [N] [C] [=Branch1] [C] [=O] [N] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Ring1] [=N] [C],-1.192,Cc2c(N)c(=O)n(c1ccccc1)n2C
+423,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [Cl],-6.23,Clc1ccc(c(Cl)c1)c2cc(Cl)ccc2Cl 
+424,[C] [C] [N] [C] [=N] [C] [Branch1] [#Branch2] [N] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [=N] [C] [Branch1] [Ring1] [S] [C] [=N] [Ring1] [=N],-3.75,CCNc1nc(NC(C)(C)C)nc(SC)n1
+425,[C] [C] [=N] [C] [=C] [N] [=C] [C] [=N] [C] [Ring1] [=Branch1] [=N] [Ring1] [#Branch2],-1.24,Cc2ncc1nccnc1n2
+426,[C] [C] [C] [C] [C] [=N] [O] [C] [=Ring1] [Branch1] [C] [=C] [Ring1] [=Branch2] [C] [C] [C] [C] [Ring1] [=N] [C] [C] [C] [Branch1] [C] [C] [C] [Ring1] [#Branch1] [C] [C] [C] [Ring1] [=Branch1] [Branch1] [C] [O] [C] [#C],-4.557,CC23Cc1cnoc1C=C2CCC4C3CCC5(C)C4CCC5(O)C#C
+427,[C] [C] [C] [C] [I],-2.841,CCCCI
+428,[Br] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-4.434,Brc1ccc2ccccc2c1
+429,[C] [C] [O] [C] [Branch1] [#Branch2] [C] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch1] [O] [O] [C] [C] [Branch1] [C] [O] [C] [C] [Branch1] [=Branch1] [O] [C] [Ring1] [#Branch1] [C] [O] [C] [C] [Branch1] [C] [O] [C] [C] [Branch2] [Branch1] [Branch1] [O] [C] [C] [C] [C] [Branch1] [C] [C] [C] [Branch2] [Ring2] [Branch1] [C] [C] [C] [C] [Ring1] [#Branch1] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [C] [C] [Branch1] [=Branch2] [C] [C] [C] [Ring1] [O] [Ring1] [=Branch1] [O] [C] [=C] [C] [=Branch1] [C] [=O] [O] [C] [Ring1] [=Branch1] [C] [Ring2] [Ring1] [O] [O] [C] [Ring2] [Ring2] [Ring1] [C],-5.312,CC1OC(CC(O)C1O)OC2C(O)CC(OC2C)OC8C(O)CC(OC7CCC3(C)C(CCC4C3CC(O)C5(C)C(CCC45O)C6=CC(=O)OC6)C7)OC8C 
+430,[F] [C] [Branch1] [C] [F] [Branch1] [C] [F] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.099,FC(F)(F)c1ccccc1
+431,[C] [C] [C] [C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [O] [C] [C] [C] [C] [C] [C],-5.757999999999999,CCCCCCOC(=O)c1ccccc1C(=O)OCCCCCC
+432,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [S] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-4.597,c1ccc2c(c1)sc3ccccc23
+433,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [Cl],-6.709,Clc1ccc(c(Cl)c1)c2ccc(Cl)c(Cl)c2Cl 
+434,[C] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [Branch1] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O] [C] [Ring1] [O] [=O],-3.913,CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O 
+435,[C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.645,c1ccccc1C(O)C(O)c2ccccc2
+436,[C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [O] [C],-2.347,COC(=O)c1ccccc1C(=O)OC
+437,[C] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C],-2.962,CCCCCCCC(=O)OCC
+438,[C] [C] [S] [S] [C] [C],-2.364,CCSSCC
+439,[C] [C] [O] [C] [C] [O] [C] [C],-0.833,CCOCCOCC
+440,[Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [Branch2] [Cl],-4.621,Clc1cc(Cl)c(Cl)cc1Cl
+441,[N] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [Branch1] [C] [N] [C] [=C] [Ring1] [#Branch1],-2.613,Nc1ccc(cc1)c2ccc(N)cc2
+442,[C] [C] [C] [C] [C] [C] [=C],-2.718,CCCCCC=C
+443,[C] [C] [C] [C] [C] [=C] [Branch1] [C] [C] [N] [=C] [Branch1] [Ring2] [N] [C] [C] [NH1] [C] [Ring1] [#Branch2] [=O],-2.732,CCCCc1c(C)nc(NCC)[nH]c1=O
+444,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring1] [C] [C] [C] [Branch1] [C] [C] [C] [C] [C],-2.312,O=C1NC(=O)NC(=O)C1(CC)C(C)CCC
+445,[N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl],-2.392,Nc1ccccc1Cl
+446,[C] [O] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1],-3.057,COc1cccc(Cl)c1
+447,[C] [C] [C] [C] [N] [Branch1] [Ring1] [C] [C] [C] [=Branch1] [C] [=O] [S] [C] [C] [C],-3.131,CCCCN(CC)C(=O)SCCC
+448,[C] [C] [C] [C] [O] [C] [=O],-1.111,CCCCOC=O
+449,[C] [C] [C] [C] [Branch1] [C] [O] [C] [C] [Branch1] [P] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [P] [C] [C] [C] [Ring2] [Ring1] [Ring2] [Branch1] [C] [O] [C] [=Branch1] [C] [=O] [C] [O],-2.974,CC12CC(O)C3C(CCC4=CC(=O)C=CC34C)C2CCC1(O)C(=O)CO
+450,[C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-5.7410000000000005,c1ccc(cc1)c2ccc(cc2)c3ccccc3
+451,[O] [C] [=C] [C] [=C] [Branch1] [Ring1] [C] [=O] [C] [=C] [Ring1] [Branch2],-2.003,Oc1ccc(C=O)cc1
+452,[C] [Br],-1.109,CBr
+453,[C] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [C] [=C] [Branch1] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O] [C] [Ring1] [O] [=O],-4.553999999999999,CC(=O)CC(c1ccc(Cl)cc1)c2c(O)c3ccccc3oc2=O
+454,[N] [C] [C] [Branch1] [C] [C] [=C] [NH1] [C] [=Branch1] [C] [=O] [N] [=Ring1] [Branch2],-0.257,Nc1c(C)c[nH]c(=O)n1 
+455,[C] [=C] [Branch1] [#C] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [I] [C] [=C] [C] [=C] [Ring1] [S],-4.245,c1c(NC(=O)c2ccccc2(I))cccc1
+456,[C] [C] [=C] [C] [N] [=C] [C] [=Branch1] [C] [=O] [NH1] [C] [=Branch1] [C] [=O] [N] [=C] [Ring1] [Branch2] [N] [Branch1] [S] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [O] [C] [=Ring2] [Ring1] [Branch1] [C] [=C] [Ring2] [Ring1] [=Branch2] [C],-1.865,Cc3cc2nc1c(=O)[nH]c(=O)nc1n(CC(O)C(O)C(O)CO)c2cc3C
+457,[F] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Br],-3.467,Fc1ccccc1Br
+458,[C] [C] [Branch1] [C] [C] [C] [Branch1] [#Branch1] [C] [=C] [Branch1] [C] [Cl] [Cl] [C] [Ring1] [Branch2] [C] [=Branch1] [C] [=O] [O] [C] [C] [=C] [C] [=C] [C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [Ring1] [=N],-7.129,CC1(C)C(C=C(Cl)Cl)C1C(=O)OCc2cccc(Oc3ccccc3)c2
+459,[O] [=C] [N] [Branch1] [=Branch2] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [Ring2] [N] [Ring1] [N] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.128,O=C1N(COC(=O)CC)C(=O)C(N1)(c2ccccc2)c3ccccc3
+460,[C] [C] [C] [C] [C] [Ring1] [Branch1],-2.0380000000000003,C1CCCC1
+461,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N],-1.922,Cc1ccccc1N
+462,[C] [Branch1] [Ring1] [O] [C] [=C] [C] [=C] [Branch1] [Ring2] [C] [C] [=C] [C] [=C] [Ring1] [O],-3.074,c1(OC)ccc(CC=C)cc1
+463,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [Branch1] [#C] [O] [C] [=Branch1] [C] [=O] [N] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [=C] [Ring1] [=C],-2.655,CN(C)C(=O)Nc1cccc(OC(=O)NC(C)(C)C)c1 
+464,[C] [C] [Branch1] [C] [C] [C] [=C],-1.994,CC(C)C=C
+465,[O] [C] [=C] [C] [=C] [C] [=N] [Ring1] [=Branch1],-1.655,Oc1ccccn1
+466,[C] [C],-1.132,CC
+467,[Cl] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl],-3.482,Clc1ccccc1Cl
+468,[S] [C] [=N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [S] [Ring1] [=Branch2],-3.411,Sc2nc1ccccc1s2 
+469,[Cl] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Branch1] [=Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch2] [Cl],-9.589,Clc1c(Cl)c(Cl)c(c(Cl)c1Cl)c2c(Cl)c(Cl)c(Cl)c(Cl)c2Cl 
+470,[C] [O] [C] [=C] [O] [C] [=C] [C] [Ring1] [Branch1] [=C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [O] [C] [Ring1] [=C] [=Ring1] [#Branch1],-3.25,COc2c1occc1cc3ccc(=O)oc23 
+471,[C] [C] [=Branch1] [C] [=O] [N],0.494,CC(=O)N
+472,[C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-3.802,Cc1cccc2ccccc12
+473,[C] [C] [N] [Branch1] [Ring1] [C] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [C] [O] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-4.088,CCN(CC)C(=O)C(C)Oc1cccc2ccccc12 
+474,[C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-1.2919999999999998,CC(O)C(C)(C)C
+475,[C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C],-1.545,CCCC(=O)OCC
+476,[C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Ring1] [O] [=O],-2.667,CC2=CC(=O)c1ccccc1C2=O 
+477,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Ring1] [O],-4.518,c1ccc2c(c1)ccc3ccccc32
+478,[C] [C] [=C] [C] [=N] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1],-2.0980000000000003,Cc1ccnc(C)c1
+479,[C] [C] [C] [C] [C] [C] [C] [C] [C] [O],-2.46,CCCCCCCCCO
+480,[Br] [C] [Br],-1.883,BrCBr
+481,[C] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [Branch1] [C] [F] [C] [Branch1] [C] [O] [C] [C] [Ring2] [Ring1] [C] [Branch1] [C] [C] [C] [Ring2] [Ring1] [=Branch1] [Branch1] [C] [O] [C] [=Branch1] [C] [=O] [C] [O],-3.4,CC1CC2C3CCC4=CC(=O)C=CC4(C)C3(F)C(O)CC2(C)C1(O)C(=O)CO
+482,[C] [C] [=C] [C] [=C] [C] [=C] [Branch1] [C] [C] [C] [=C] [C] [Ring1] [#Branch1] [=C] [Ring1] [O],-4.147,Cc1ccc2cc(C)ccc2c1
+483,[C] [C] [S] [C] [=Branch1] [C] [=O] [N] [Branch1] [#Branch1] [C] [C] [Branch1] [C] [C] [C] [C] [C] [Branch1] [C] [C] [C],-3.4530000000000003,CCSC(=O)N(CC(C)C)CC(C)C
+484,[C] [C] [C] [C] [Cl],-1.94,CCCCCl
+485,[Cl] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [C] [Branch1] [Ring2] [N] [C] [=O] [N] [C] [=C] [N] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [Ring2] [N] [C] [=O] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-3.715,ClC(Cl)(Cl)C(NC=O)N1C=CN(C=C1)C(NC=O)C(Cl)(Cl)Cl 
+486,[N] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [=C] [Ring1] [#Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring2] [Ring1] [C] [Ring1] [=Branch1],-4.849,Nc3cc2c1ccccc1ccc2c4ccccc34 
+487,[C] [C] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=S] [C] [=C] [C] [=C] [N] [=C] [Ring1] [P] [Ring1] [=Branch1],-4.227,CCN2c1ccccc1N(C)C(=S)c3cccnc23 
+488,[C] [C] [N] [C] [=Branch1] [C] [=S] [N] [C] [C],-1.028,CCNC(=S)NCC
+489,[O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [Cl],-3.78,Oc1cc(Cl)cc(Cl)c1Cl
+490,[C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C],-1.545,CCCCC(=O)OC
+491,[N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.632,Nc1ccccc1
+492,[C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [O] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-3.682,C1=Cc2cccc3cccc1c23
+493,[C] [C] [N] [C] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [C] [Branch1] [Ring1] [O] [C] [=N] [Ring1] [N],-3.185,CCNc1nc(NC(C)C)nc(OC)n1
+494,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#C] [C] [Ring1] [N] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-6.007000000000001,c1ccc2c(c1)cc3ccc4cccc5ccc2c3c45
+495,[C] [C] [Br],-1.529,CCBr
+496,[C] [C] [C] [#C] [C] [C],-1.933,CCC#CCC
+497,[C] [C] [O] [C] [Branch1] [#Branch2] [C] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch1] [O] [O] [C] [C] [Branch1] [C] [O] [C] [C] [Branch1] [=Branch1] [O] [C] [Ring1] [#Branch1] [C] [O] [C] [C] [Branch1] [C] [O] [C] [C] [Branch2] [Branch1] [C] [O] [C] [C] [C] [C] [Branch1] [C] [C] [C] [Branch2] [Ring2] [C] [C] [C] [C] [C] [Ring1] [#Branch1] [C] [C] [C] [Branch1] [C] [C] [C] [Branch1] [=Branch2] [C] [C] [C] [Ring1] [#Branch2] [Ring1] [=Branch1] [O] [C] [=C] [C] [=Branch1] [C] [=O] [O] [C] [Ring1] [=Branch1] [C] [Ring2] [Ring1] [#Branch2] [O] [C] [Ring2] [Ring2] [C] [C],-6.114,CC1OC(CC(O)C1O)OC2C(O)CC(OC2C)OC8C(O)CC(OC7CCC3(C)C(CCC4C3CCC5(C)C(CCC45O)C6=CC(=O)OC6)C7)OC8C 
+498,[C] [C] [C] [=Branch1] [C] [=C] [C],-1.994,CCC(=C)C
+499,[O] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [S] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1],-4.59,Oc1ccc(cc1)C2(OC(=O)c3ccccc23)c4ccc(O)cc4 
+500,[Br] [C] [=C] [C] [Branch1] [C] [Br] [=C] [C] [Branch1] [C] [Br] [=C] [Ring1] [Branch2],-5.27,Brc1cc(Br)cc(Br)c1
+501,[C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [Branch2] [Cl],-5.247000000000001,COP(=S)(OC)Oc1cc(Cl)c(Cl)cc1Cl
+502,[C] [C] [=C] [C] [=Branch1] [C] [=O] [NH1] [C] [=Branch1] [C] [=S] [NH1] [Ring1] [Branch2],-0.547,Cc1cc(=O)[nH]c(=S)[nH]1
+503,[C] [O] [C] [=C] [C] [Branch1] [Ring2] [C] [C] [=C] [=C] [C] [=C] [Ring1] [=Branch2] [O],-2.675,COc1cc(CC=C)ccc1O
+504,[C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=C] [C] [Ring1] [=N] [=C] [Ring1] [O] [Ring1] [#Branch1],-4.957,c1cc2ccc3cccc4ccc(c1)c2c34
+505,[C] [C] [Branch1] [C] [C] [C] [O] [N] [Branch1] [O] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl] [C] [Ring1] [=C] [=O],-3.077,CC1(C)CON(Cc2ccccc2Cl)C1=O
+506,[C] [C] [C] [C] [O] [C] [C] [O],-0.775,CCCCOCCO
+507,[C] [C] [C] [C] [C] [Branch1] [C] [O] [C] [C] [Ring1] [#Branch1] [C] [C] [C] [C] [Ring1] [O] [C] [C] [C] [Branch1] [C] [C] [C] [Ring1] [#Branch1] [C] [C] [C] [Ring1] [=Branch1] [=O],-3.882,CC12CCC(O)CC1CCC3C2CCC4(C)C3CCC4=O
+508,[F] [C] [Branch1] [C] [F] [Branch1] [C] [F] [C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [N] [C] [C] [Branch1] [Ring1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [Ring1] [Branch2] [=O],-4.749,FC(F)(F)c1cccc(c1)N2CC(CCl)C(Cl)C2=O
+509,[C] [=C] [C] [=C] [N] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-2.6630000000000003,c1ccc2ncccc2c1
+510,[C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Branch1] [C] [O] [=C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [=C] [Ring1] [=Branch2],-1.913,COC(=O)c1cc(O)c(O)c(O)c1 
+511,[O] [C] [Branch1] [=Branch2] [C] [N] [C] [=N] [C] [=N] [Ring1] [Branch1] [Branch1] [=Branch2] [C] [N] [C] [=N] [C] [=N] [Ring1] [Branch1] [C] [=C] [C] [=C] [Branch1] [C] [F] [C] [=C] [Ring1] [#Branch1] [F],-2.418,OC(Cn1cncn1)(Cn2cncn2)c3ccc(F)cc3F 
+512,[Cl] [C] [=C] [C] [=C] [O] [C] [=Branch1] [C] [=O] [NH1] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2],-2.679,Clc2ccc1oc(=O)[nH]c1c2
+513,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch2] [Cl],-7.898,Clc1ccc(c(Cl)c1)c2c(Cl)c(Cl)c(Cl)c(Cl)c2Cl
+514,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [=Branch1] [C] [=O] [C] [=Branch1] [C] [=O] [N] [Ring1] [=Branch2],0.436,O=C1NC(=O)C(=O)C(=O)N1 
+515,[Cl] [C] [C] [C] [Cl],-1.618,ClCCCCl
+516,[F] [C] [=C] [C] [=C] [C] [Branch1] [C] [Br] [=C] [Ring1] [#Branch1],-3.467,Fc1cccc(Br)c1
+517,[Cl] [C] [=C] [C] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [#Branch1],-3.928,Clc1ccc(Br)cc1
+518,[C] [C] [Branch1] [C] [C] [C] [Branch1] [C] [C] [C],-2.584,CC(C)C(C)C
+519,[C] [C] [C] [=C],-1.655,CCC=C
+520,[N] [C] [C] [=C] [NH1] [C] [=Branch1] [C] [=O] [N] [=Ring1] [#Branch1],0.051,Nc1cc[nH]c(=O)n1 
+521,[F] [C] [Branch1] [C] [F] [Branch1] [C] [Cl] [C] [Branch1] [C] [F] [Branch1] [C] [Cl] [Cl],-3.077,FC(F)(Cl)C(F)(Cl)Cl
+522,[Cl] [C] [Branch1] [C] [Cl] [C] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl],-6.007999999999999,ClC(Cl)C(c1ccc(Cl)cc1)c2ccccc2Cl 
+523,[C] [C] [C] [C] [C] [C] [Branch1] [P] [C] [C] [=C] [C] [C] [Branch1] [C] [O] [C] [C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [S] [C] [C] [C] [Ring2] [Ring1] [Ring1] [=O],-3.564,CC34CCC1C(CC=C2CC(O)CCC12C)C3CCC4=O
+524,[C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=Branch1] [Branch1] [=C] [Ring2] [Ring1] [C] [C] [Ring1] [P] [=C] [Ring1] [#C] [C] [Ring1] [=N] [=C] [Ring1] [O] [Ring1] [#Branch1],-6.446000000000001,c1cc2ccc3ccc4ccc5cccc6c(c1)c2c3c4c56
+525,[C] [C] [Branch1] [Ring1] [O] [C] [=C] [Branch1] [Ring1] [O] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C] [Ring1] [=Branch1] [C] [=Ring1] [=C],-0.825,c1c(OC)c(OC)C2C(=O)OCC2c1
+526,[O] [C] [C] [Branch1] [C] [O] [C] [O],0.688,OCC(O)CO
+527,[C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O],-1.941,COc1ccccc1O 
+528,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [O] [C] [=N] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [Branch2] [Cl],-4.972,CCOP(=S)(OCC)Oc1nc(Cl)c(Cl)cc1Cl 
+529,[C] [C] [=C] [C] [=Branch1] [C] [=O] [N] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Ring1] [N] [C],-1.733,Cc1cc(=O)n(c2ccccc2)n1C
+530,[C] [C] [N] [C] [=N] [C] [Branch1] [C] [Cl] [=N] [C] [Branch1] [Ring2] [N] [C] [C] [=N] [Ring1] [#Branch2],-2.8110000000000004,CCNc1nc(Cl)nc(NCC)n1 
+531,[C] [S] [C] [=N] [C] [=Branch1] [=N] [=N] [C] [=Branch1] [Ring2] [=N] [Ring1] [=Branch1] [N] [Branch1] [C] [C] [C] [N] [Branch1] [C] [C] [C],-2.689,CSc1nc(nc(n1)N(C)C)N(C)C
+532,[C] [=C],-0.815,C=C
+533,[C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [C] [O],-1.365,CC(C)(C)CCO
+534,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring1] [C] [C] [C] [C] [=C],-1.368,O=C1NC(=O)NC(=O)C1(CC)CC=C
+535,[O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [Cl],-3.705,Oc1ccc(Cl)c(Cl)c1Cl
+536,[C] [O] [C] [=C] [C] [=C] [Branch2] [Ring1] [=Branch1] [O] [C] [=C] [C] [=C] [Branch1] [O] [N] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C] [C] [=C] [Ring1] [N] [C] [=C] [Ring2] [Ring1] [Ring1],-3.928,COc2ccc(Oc1ccc(NC(=O)N(C)C)cc1)cc2
+537,[C] [C] [C] [=C] [C] [=C] [Branch1] [C] [C] [C] [=C] [Ring1] [#Branch1],-3.3280000000000003,CCc1ccc(C)cc1
+538,[C] [C] [Branch1] [C] [C] [S] [C] [Branch1] [C] [C] [C],-2.162,CC(C)SC(C)C
+539,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [S] [C] [S] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [O] [C] [C],-5.471,CCOP(=S)(OCC)SCSP(=S)(OCC)OCC
+540,[C] [C] [=Branch1] [C] [=O] [O] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [O] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [=Branch1] [C] [=O] [C] [C] [Ring2] [Ring1] [Branch1] [Ring1] [P] [C],-3.426,CC(=O)OCC(=O)C3(O)CCC4C2CCC1=CC(=O)CCC1(C)C2C(=O)CC34C 
+541,[N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-2.392,Nc1ccc(Cl)cc1
+542,[C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [O],-1.719,CCCC(C)(C)CO
+543,[C] [O] [C] [C] [C] [N] [C] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [C] [Branch1] [Ring1] [S] [C] [=N] [Ring1] [N],-3.259,COCCCNc1nc(NC(C)C)nc(SC)n1
+544,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [C] [C] [C] [C] [Ring1] [Branch1] [C] [C] [C] [C] [C] [Ring1] [Branch2] [Ring1] [Branch1],-2.47,CN(C)C(=O)NC1CC2CC1C3CCCC23
+545,[C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.554,CC(C)(C)c1ccccc1
+546,[C] [C] [=Branch1] [C] [=O] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [S] [N] [Branch1] [Branch1] [C] [Ring1] [=Branch1] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.645,CC(=O)CCC1C(=O)N(N(C1=O)c2ccccc2)c3ccccc3
+547,[C] [C] [=Branch1] [C] [=O] [O] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [O] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [Branch1] [C] [O] [C] [C] [Ring2] [Ring1] [Branch1] [Ring1] [P] [C],-3.507,CC(=O)OCC(=O)C3(O)CCC4C2CCC1=CC(=O)C=CC1(C)C2C(O)CC34C 
+548,[C] [C] [C] [O] [C],-0.718,CCCOC
+549,[C] [C] [Branch1] [C] [C] [O] [C] [=Branch1] [C] [=O] [C],-1.1909999999999998,CC(C)OC(=O)C
+550,[Br] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.345,Brc1ccccc1
+551,[C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [C] [C] [Ring2] [Ring1] [Ring1] [Ring1] [S] [C],-4.87,CCC(=O)OC3CCC4C2CCC1=CC(=O)CCC1(C)C2CCC34C 
+552,[O] [=C] [NH1] [C] [=N] [C] [NH1] [N] [=C] [C] [Ring1] [=Branch2] [=Ring1] [Branch1],-0.84,O=c1[nH]cnc2[nH]ncc12 
+553,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.147,CN(C)C(=O)C(c1ccccc1)c2ccccc2 
+554,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-1.882,O=C1NC(=O)c2ccccc12 
+555,[O] [C] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [Branch1] [=Branch2] [C] [=C] [N] [=C] [N] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl],-4.1080000000000005,OC(c1ccc(Cl)cc1)(c2cncnc2)c3ccccc3Cl
+556,[C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.462,COC(=O)c1ccccc1
+557,[C] [N] [C] [=C] [C] [=Branch1] [C] [=O] [NH1] [C] [Ring1] [#Branch1] [=O],-0.375,Cn1ccc(=O)[nH]c1=O
+558,[C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [Branch2] [Ring1] [Ring1] [N] [Branch1] [Branch1] [C] [Ring1] [=Branch1] [=O] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.739,CCCCC1C(=O)N(N(C1=O)c2ccc(O)cc2)c3ccccc3 
+559,[Cl] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=Branch1] [Ring2] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [Cl],-6.155,Clc1ccc(Cl)c(c1)c2cccc(Cl)c2Cl 
+560,[C] [C] [C] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=Branch1] [N] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [N] [Ring1] [N] [S] [Branch1] [C] [N] [=Branch1] [C] [=O] [=O],-2.184,CCC2NC(=O)c1cc(c(Cl)cc1N2)S(N)(=O)=O
+561,[C] [C] [C] [=C] [C] [Ring1] [Branch1],-1.72,C1CC=CC1
+562,[C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [=Branch2] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [=Branch2] [=O],0.919,C1(=O)NC(=O)NC(=O)C1(O)C2(O)C(=O)NC(=O)NC2(=O)
+563,[C] [C] [C] [C] [C] [C] [C] [C] [C],-3.678,CCCCCCCCC
+564,[C] [=C] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Ring1] [O] [=C] [C] [=C] [Ring2] [Ring1] [Ring1] [Ring1] [#C],-5.931,c1cccc2c3c(C)cc4ccccc4c3ccc12
+565,[C] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.66,CCOc1ccccc1
+566,[C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.0980000000000003,CCOC(=O)C=Cc1ccccc1
+567,[C] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [N],-1.815,Cc1ccc(cc1)S(=O)(=O)N
+568,[C] [C] [Branch2] [Ring1] [C] [O] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [C] [#C],-3.629,CC(OC(=O)Nc1cccc(Cl)c1)C#C
+569,[C] [N] [C] [=Branch1] [C] [=O] [C] [N] [=C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring2] [Ring1] [Ring1] [Ring1] [#Branch1],-4.05,CN2C(=O)CN=C(c1ccccc1)c3cc(Cl)ccc23
+570,[C] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [C] [C],-1.308,CCC(O)C(C)C
+571,[C] [C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [O] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [S] [Branch1] [C] [C] [=O],-3.283,CCOP(=S)(OCC)Oc1ccc(cc1)S(C)=O 
+572,[C] [C] [C] [C] [C] [C] [Branch1] [P] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [S] [C] [C] [C] [Ring2] [Ring1] [Ring1] [O],-3.659,CC12CCC3C(CCC4=CC(=O)CCC34C)C2CCC1O
+573,[C] [C] [C] [C] [C] [C] [C],-2.97,CCCCCCC
+574,[O] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-3.08,Oc1cccc2ccccc12
+575,[C] [/C] [C] [C] [C] [C] [C] [Ring1] [=Branch1] [\C],-3.305,C/C1CCCCC1\C
+576,[C] [C] [C] [=C] [C] [Ring1] [Branch1] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [=C] [Ring1] [#Branch2] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Ring1] [#Branch2],-5.942,C1Cc2c3c1cccc3cc4c2ccc5ccccc54
+577,[C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [C] [C] [C] [C] [C] [C] [=C] [Branch1] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [=N] [C] [C] [C] [Ring2] [Ring1] [Branch1] [Ring1] [P] [C],-4.593,CC(=O)C3(C)CCC4C2C=C(C)C1=CC(=O)CCC1(C)C2CCC34C
+578,[C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C],-1.554,CCCCCC(=O)C
+579,[C] [O] [P] [=Branch1] [C] [=O] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [=O] [S] [C],-0.416,COP(=O)(NC(C)=O)SC 
+580,[C] [C] [C] [C] [S] [P] [=Branch1] [C] [=O] [Branch1] [=Branch1] [S] [C] [C] [C] [C] [S] [C] [C] [C] [C],-4.074,CCCCSP(=O)(SCCCC)SCCCC
+581,[N] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch2] [Ring1] [C] [N] [C] [Branch1] [=Branch2] [N] [S] [Ring1] [=Branch1] [=Branch1] [C] [=O] [=O] [C] [Branch1] [C] [Cl] [Cl] [C] [=C] [Ring1] [#C] [Cl],-2.98,NS(=O)(=O)c2cc1c(NC(NS1(=O)=O)C(Cl)Cl)cc2Cl 
+582,[C] [C] [=C] [Branch1] [C] [C] [C],-1.994,CC=C(C)C
+583,[C] [C] [=C] [C] [=C] [Branch1] [C] [C] [C] [Branch1] [C] [C] [=C] [Ring1] [Branch2],-3.343,Cc1ccc(C)c(C)c1
+584,[O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [Branch2] [Cl],-3.78,Oc1cc(Cl)c(Cl)cc1Cl
+585,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [O] [Ring1] [=Branch1],-3.713,c1ccc2c(c1)cnc3ccccc23 
+586,[C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [O] [C] [C],-1.663,CCCC(C)(O)CC
+587,[C] [C] [C] [C] [C] [C] [C] [C],-3.324,CCCCCCCC
+588,[C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [Ring1] [#Branch2] [=C] [Ring1] [=C],-4.518,c1ccc2cc3ccccc3cc2c1
+589,[N] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.866,NNc1ccccc1
+590,[C] [C] [C] [C] [C] [C] [C] [C] [Ring1] [Branch2],-3.355,C1CCCCCCC1
+591,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring2] [C] [C] [=C] [C] [C] [=C],-1.471,O=C1NC(=O)NC(=O)C1(CC=C)CC=C
+592,[Cl] [C] [Branch1] [C] [Cl] [Cl],-1.812,ClC(Cl)Cl
+593,[C] [C] [=C] [C] [C] [C] [C] [Ring1] [=Branch1],-2.574,CC1=CCCCC1
+594,[C] [O] [C] [=C] [O] [C] [=C] [C] [Ring1] [Branch1] [=C] [Branch1] [Ring1] [O] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [Branch1] [C] [C] [O] [C] [Ring1] [P] [=Ring1] [Branch2],-3.603,COc2c1occc1c(OC)c3c(=O)cc(C)oc23 
+595,[C] [=C] [C] [=C] [C] [=C] [Branch1] [=Branch2] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [#C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-6.007000000000001,c1ccc2c3c(ccc2c1)c4cccc5cccc3c45
+596,[C] [C] [C] [Branch1] [Ring1] [C] [C] [C] [=O],-1.523,CCC(CC)C=O
+597,[C] [C] [C] [O] [C] [C] [C],-1.426,CCCOCCC
+598,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [O],-4.231,CCCCCCCCCCCCCCO
+599,[O] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2] [Cl],-3.572,Oc1c(Cl)ccc(Cl)c1Cl
+600,[N] [C] [=Branch1] [C] [=O] [N],0.8320000000000001,NC(=O)N
+601,[C] [C] [C] [C] [#C],-1.446,CCCC#C
+602,[Br] [C] [=C] [C] [=C] [C] [Branch1] [C] [Br] [=C] [Ring1] [#Branch1],-4.298,Brc1cccc(Br)c1
+603,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [O],-5.649,CCCCCCCCCCCCCCCCCCO
+604,[C] [=C] [C] [Branch1] [C] [O] [=C] [Branch1] [C] [O] [C] [O] [C] [C] [Branch1] [C] [O] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [O] [=C] [C] [Ring1] [Branch2] [=C] [Ring1] [N] [C] [=Ring1] [S] [Ring2] [Ring1] [=Branch1],-1.795,c1cc(O)c(O)c2OCC3(O)CC4=CC(=O)C(O)=CC4=C3c21
+605,[O] [C] [C] [=C] [C] [C] [Ring1] [Branch1] [C] [Branch1] [C] [Cl] [C] [=Branch2] [Ring1] [C] [=C] [Branch1] [C] [Cl] [C] [Ring1] [Branch2] [Branch1] [C] [Cl] [C] [Ring1] [Branch2] [Branch1] [C] [Cl] [Cl] [Cl],-4.156000000000001,OC1C=CC2C1C3(Cl)C(=C(Cl)C2(Cl)C3(Cl)Cl)Cl 
+606,[C] [C] [Branch1] [C] [C] [C] [C] [O] [C] [=O],-1.449,CC(C)CCOC=O
+607,[C] [=C] [C] [=C] [N] [=C] [Branch1] [=Branch2] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=N] [Ring1] [#Branch2],-4.9030000000000005,c2ccc1nc(ccc1c2)c4ccc3ccccc3n4 
+608,[C] [C] [O] [P] [=Branch1] [C] [=O] [Branch1] [Ring2] [O] [C] [C] [O] [C] [C],-0.953,CCOP(=O)(OCC)OCC
+609,[Cl] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [C] [=C] [C] [C] [Ring1] [Branch1] [C] [Ring1] [O] [Branch1] [C] [Cl] [C] [Ring1] [#Branch2] [Branch1] [C] [Cl] [Cl],-5.152,ClC2=C(Cl)C3(Cl)C1C=CCC1C2(Cl)C3(Cl)Cl 
+610,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [NH1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Ring1] [#Branch2],-3.836,c1ccc2c(c1)[nH]c3ccccc32
+611,[O] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [O],0.675,OCC(O)C(O)CO
+612,[C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [C] [N] [C] [=C] [Ring1] [#Branch1],-2.709,CCCOC(=O)c1ccc(N)cc1
+613,[O] [=C] [C] [C] [C] [=Branch1] [C] [=O] [N] [Ring1] [=Branch1],0.282,O=C1CCC(=O)N1
+614,[C] [C] [C] [C] [C] [=C] [Branch1] [C] [C] [N] [=C] [Branch1] [Ring2] [N] [C] [C] [N] [=C] [Ring1] [#Branch2] [O] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C],-3.4930000000000003,CCCCc1c(C)nc(NCC)nc1OS(=O)(=O)N(C)C 
+615,[C] [C] [N] [C] [=N] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=S] [C] [=C] [C] [=C] [N] [=C] [Ring1] [P] [Ring1] [=Branch1],-3.411,CCN2c1ncccc1N(C)C(=S)c3cccnc23 
+616,[C] [C] [C] [O] [C] [C] [Ring1] [=Branch1],-0.978,C1CCOCC1
+617,[C] [C] [C] [C] [C] [C] [#C],-2.155,CCCCCC#C
+618,[C] [=C] [C] [Branch1] [C] [C] [=C] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [C] [C] [C] [Branch1] [=Branch1] [C] [Ring1] [=Branch2] [=Ring1] [Branch1] [=C] [Ring1] [N] [C] [=C] [Ring1] [S],-6.311,c1cc(C)cc2c1c3cc4cccc5CCc(c45)c3cc2
+619,[C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.775,CCOC(=O)c1ccccc1
+620,[Cl] [C] [C] [Branch1] [C] [C] [C],-1.924,ClCC(C)C
+621,[C] [C] [C] [C] [C] [C] [Branch1] [S] [C] [C] [C] [=C] [C] [Branch1] [C] [O] [=C] [C] [=C] [Ring1] [O] [Ring1] [#Branch1] [C] [Ring1] [#C] [C] [C] [C] [Ring2] [Ring1] [C] [Branch1] [C] [O] [C] [#C],-4.317,CC34CCC1C(CCc2cc(O)ccc12)C3CCC4(O)C#C 
+622,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C],-4.025,CCCCCCCCCCCC(=O)OC
+623,[C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [Ring1] [#Branch2] [=C] [C] [Ring1] [=C] [=C] [Ring2] [Ring1] [C],-5.568,c1ccc2cc3cc4ccccc4cc3cc2c1
+624,[C] [C] [C] [C] [C] [Br],-2.658,CCCCCBr
+625,[C] [C] [C] [C] [/C] [=C] [/C],-2.784,CCCC/C=C/C
+626,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [Ring1] [Branch1],-3.869,CCCCCC1CCCC1
+627,[Cl] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=Branch1] [Ring2] [=C] [Ring1] [#Branch1] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-7.261,Clc1ccc(Cl)c(c1)c2c(Cl)c(Cl)cc(Cl)c2Cl 
+628,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [Ring1] [C] [C] [C] [Branch1] [C] [C] [C],-1.6030000000000002,O=C1NC(=O)NC(=O)C1(CC)C(C)C
+629,[C] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-2.232,CC(Cl)(Cl)Cl
+630,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [Branch1] [Branch2] [C] [C] [C] [C] [C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [N] [Ring1] [=N],-1.405,O=C2NC(=O)C1(CCCCC1)C(=O)N2
+631,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [Ring1] [=Branch2],-2.3040000000000003,CN(C)C(=O)OC1=CC(=O)CC(C)(C)C1 
+632,[C] [C] [=C] [C] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [#Branch1],-3.667,Cc1ccc(Br)cc1
+633,[C] [C] [Branch1] [C] [C] [N] [C] [=Branch1] [C] [=O] [N] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [Branch1] [C] [Ring1] [=Branch1] [=O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-4.004,CC(C)NC(=O)N1CC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2
+634,[C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Branch1] [C] [O] [C] [Branch1] [C] [C] [=C] [Ring1] [=Branch2],-2.9410000000000003,Cc1cc(C)c(O)c(C)c1
+635,[C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.418,c1ccccc1
+636,[Cl] [C] [=C] [C] [=C] [Branch1] [C] [I] [C] [=C] [Ring1] [#Branch1],-4.384,Clc1ccc(I)cc1
+637,[C] [O] [C] [=C] [C] [=C] [Branch1] [O] [N] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C] [C] [=C] [Ring1] [N] [Cl],-2.6830000000000003,COc1ccc(NC(=O)N(C)C)cc1Cl
+638,[C] [C] [Branch1] [C] [C] [N] [Branch1] [#Branch1] [C] [=Branch1] [C] [=O] [C] [Cl] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.018,CC(C)N(C(=O)CCl)c1ccccc1 
+639,[C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.85,C=Cc1ccccc1
+640,[C] [O] [C] [O] [C],0.092,COCOC
+641,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C],-3.004,Cc1ccccc1C
+642,[C] [C] [C] [Branch1] [C] [C] [O],-0.616,CCC(C)O
+643,[O] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1],-1.59,Oc1ccc(O)cc1
+644,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [Ring1] [P] [Ring1] [#Branch2],-5.189,C1c2ccccc2c3cc4ccccc4cc13
+645,[O] [=C] [C] [N] [C] [=Branch1] [C] [=O] [N] [Ring1] [=Branch1],0.603,O=C1CNC(=O)N1 
+646,[C] [Branch1] [C] [O] [=C] [C] [Branch1] [C] [O] [=C] [C] [=C] [Ring1] [Branch2] [C] [C] [C] [C] [C] [C],-3.4930000000000003,c1(O)cc(O)ccc1CCCCCC
+647,[C] [=C] [C] [S] [=Branch1] [C] [=O] [S] [C] [C] [=C],-2.045,C=CCS(=O)SCC=C
+648,[C] [C] [=C] [Branch1] [C] [C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [C] [=C] [Ring1] [#Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring2] [Ring1] [Ring1] [Ring1] [=Branch1],-6.265,Cc1c(C)c2c3ccccc3ccc2c4ccccc14
+649,[C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [Branch2] [Ring2] [O] [C] [Branch1] [C] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [Branch1] [C] [F] [C] [Branch1] [C] [O] [C] [C] [Ring2] [Ring1] [=Branch1] [Ring2] [Ring1] [C] [C] [C] [=Branch1] [C] [=O] [C] [O],-5.062,CCCCC(=O)OC3(C(C)CC4C2CCC1=CC(=O)C=CC1(C)C2(F)C(O)CC34C)C(=O)CO
+650,[O] [=C] [NH1] [C] [=Branch1] [C] [=O] [C] [NH1] [C] [=Branch1] [C] [=O] [NH1] [C] [=Ring1] [=Branch1] [NH1] [Ring1] [O],-0.541,O=c2[nH]c(=O)c1[nH]c(=O)[nH]c1[nH]2 
+651,[O] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-4.203,Oc1c(Cl)cc(Cl)c(Cl)c1Cl
+652,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-6.638,Clc1ccc(cc1)C(c2ccc(Cl)cc2)C(Cl)(Cl)Cl
+653,[C] [C] [Branch1] [C] [C] [C] [O] [C] [=O],-1.095,CC(C)COC=O
+654,[C] [C] [N] [C] [=N] [C] [Branch1] [C] [C] [=C] [C] [Branch1] [=Branch2] [C] [Branch1] [C] [F] [Branch1] [C] [F] [F] [=C] [Ring1] [O] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring2] [Ring1] [Branch1] [Ring1] [=Branch1],-4.45,CCN2c1nc(C)cc(C(F)(F)F)c1NC(=O)c3cccnc23 
+655,[C] [C] [C] [C] [C] [C],-2.615,CCCCCC
+656,[C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring1] [=Branch1],-1.621,COC(=O)c1cccnc1 
+657,[N] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch2] [Ring1] [=Branch1] [N] [C] [Branch1] [#Branch2] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [S] [Ring1] [=N] [=Branch1] [C] [=O] [=O] [C] [=C] [Ring2] [Ring1] [Ring1] [C] [Branch1] [C] [F] [Branch1] [C] [F] [F],-3.741,NS(=O)(=O)c3cc2c(NC(Cc1ccccc1)NS2(=O)=O)cc3C(F)(F)F
+658,[Cl] [C] [=C] [C] [=C] [Branch1] [=Branch1] [C] [=C] [Ring1] [=Branch1] [Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-7.425,Clc1ccc(cc1Cl)c2cc(Cl)c(Cl)c(Cl)c2Cl 
+659,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#C] [Ring1] [=Branch1],-5.568,c1ccc2c(c1)c3ccccc3c4ccccc24
+660,[C] [C] [=C] [Branch1] [P] [C] [=Branch1] [Ring1] [=C] [C] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1],-4.775,CC=C(C(=CC)c1ccc(O)cc1)c2ccc(O)cc2
+661,[C] [C] [C] [C] [C] [Branch1] [Ring1] [C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [O] [C] [C] [Branch1] [Ring1] [C] [C] [C] [C] [C] [C],-7.117000000000001,CCCCC(CC)COC(=O)c1ccccc1C(=O)OCC(CC)CCCC
+662,[C] [C] [C] [=C] [C] [=C] [C] [=N] [Ring1] [=Branch1],-2.051,CCc1ccccn1
+663,[C] [O] [P] [=Branch1] [C] [=O] [Branch1] [Ring1] [O] [C] [O] [C] [Branch1] [C] [Br] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Br],-3.548,COP(=O)(OC)OC(Br)C(Cl)(Cl)Br
+664,[C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.079,c1ccc(cc1)c2ccccc2
+665,[C] [N] [Branch1] [C] [C] [C] [=N] [C] [=Branch1] [=N] [=N] [C] [=Branch1] [Ring2] [=N] [Ring1] [=Branch1] [N] [Branch1] [C] [C] [C] [N] [Branch1] [C] [C] [C],-2.492,CN(C)c1nc(nc(n1)N(C)C)N(C)C
+666,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [Branch1] [=Branch2] [C] [C] [C] [C] [C] [C] [Ring1] [#Branch1] [C] [=Branch1] [C] [=O] [N] [Ring1] [=C],-1.844,O=C2NC(=O)C1(CCCCCC1)C(=O)N2 
+667,[O] [C] [C] [O] [C] [Branch1] [C] [O] [Branch1] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [=Branch2] [O],0.471,OCC1OC(O)(CO)C(O)C1O
+668,[C] [C] [=C] [C] [Branch1] [C] [C] [=C] [C] [Branch1] [C] [O] [=C] [Ring1] [Branch2],-2.652,Cc1cc(C)cc(O)c1
+669,[Cl] [C] [C] [#C] [C] [O] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1],-4.16,ClCC#CCOC(=O)Nc1cccc(Cl)c1
+670,[C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-2.642,CC(=O)Nc1ccc(Cl)cc1
+671,[Cl] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=Branch1] [Ring2] [=C] [Ring1] [#Branch1] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch2] [Cl],-7.898,Clc1ccc(Cl)c(c1)c2c(Cl)c(Cl)c(Cl)c(Cl)c2Cl
+672,[C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-2.584,CCC(C)(C)C
+673,[C] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.097,CNc1ccccc1
+674,[C] [C] [=Branch1] [C] [=O] [O] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [O] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [Branch1] [C] [O] [C] [C] [Ring1] [P] [Ring2] [Ring1] [Branch1] [C],-3.692,CC(=O)OCC(=O)C1(O)CCC2C3CCC4=CC(=O)CCC4(C)C3C(O)CC21C
+675,[O] [C] [N] [=C] [Branch1] [#Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [N] [C] [Ring2] [Ring1] [Ring1] [=O],-3.75,OC3N=C(c1ccccc1Cl)c2cc(Cl)ccc2NC3=O
+676,[O] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1],-2.761,Oc1cccc(Cl)c1
+677,[N] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [Branch1] [O] [N] [=C] [N] [S] [Ring1] [=Branch1] [=Branch1] [C] [=O] [=O] [C] [=C] [Ring1] [N] [Cl],-1.7519999999999998,NS(=O)(=O)c2cc1c(N=CNS1(=O)=O)cc2Cl 
+678,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [C] [C] [C] [C],-0.911,O=C1NC(=O)NC(=O)C1(C)CC
+679,[C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.09,C(c1ccccc1)c2ccccc2
+680,[C] [C] [Branch1] [C] [C] [N] [C] [=N] [C] [Branch1] [C] [Cl] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [Ring1] [O],-3.329,CC(C)Nc1nc(Cl)nc(NC(C)C)n1
+681,[C] [C] [C] [C] [Branch1] [C] [C] [C] [O],-1.381,CCCC(C)CO
+682,[C] [C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [O],-1.663,CCCCC(C)(C)O
+683,[C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.988,CCc1ccccc1
+684,[Cl] [C] [C] [=C] [C] [C] [Ring1] [Branch1] [C] [Branch1] [C] [Cl] [C] [=Branch2] [Ring1] [C] [=C] [Branch1] [C] [Cl] [C] [Ring1] [Branch2] [Branch1] [C] [Cl] [C] [Ring1] [Branch2] [Branch1] [C] [Cl] [Cl] [Cl],-5.26,ClC1C=CC2C1C3(Cl)C(=C(Cl)C2(Cl)C3(Cl)Cl)Cl
+685,[C] [C] [Branch1] [C] [C] [C] [Branch2] [Ring1] [=C] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [Ring1] [C] [#N] [C] [=C] [C] [=C] [C] [Branch1] [#Branch2] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [=C] [Ring1] [=N] [C] [Ring2] [Ring1] [#Branch1] [Branch1] [C] [C] [C],-6.15,CC1(C)C(C(=O)OC(C#N)c2cccc(Oc3ccccc3)c2)C1(C)C
+686,[C] [O] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [C] [C] [=C] [C] [Branch1] [C] [C] [=C] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [C] [C] [C],-4.795,COC(C)(C)CCCC(C)CC=CC(C)=CC(=O)OC(C)C
+687,[C] [S] [C] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [C] [Branch1] [#Branch1] [N] [C] [Branch1] [C] [C] [C] [=N] [Ring1] [=C],-3.693,CSc1nc(NC(C)C)nc(NC(C)C)n1
+688,[C] [C] [Branch1] [Ring1] [C] [#C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-3.199,CC(C#C)N(C)C(=O)Nc1ccc(Cl)cc1
+689,[C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [Ring1] [#Branch2] [C],-4.1160000000000005,Cc1cc2ccccc2cc1C
+690,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [Cl],-5.7620000000000005,Clc1ccc(cc1)c2cc(Cl)ccc2Cl 
+691,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-7.343,Clc1ccc(c(Cl)c1)c2cc(Cl)c(Cl)c(Cl)c2Cl 
+692,[N] [C] [Branch1] [C] [N] [=N] [C] [#N],0.361,NC(N)=NC#N 
+693,[Cl] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.984,Clc1cccc(Cl)c1c2ccccc2 
+694,[C] [O] [C] [=C] [C] [=C] [Branch1] [Ring1] [C] [=O] [C] [=C] [Ring1] [Branch2],-2.252,COc1ccc(C=O)cc1
+695,[C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C] [C],-2.608,CCCCCCC(=O)OCC
+696,[C] [=C] [C] [=C] [NH1] [N] [=C] [C] [Ring1] [Branch1] [=C] [Ring1] [=Branch2],-2.34,c2ccc1[nH]ncc1c2 
+697,[C] [C] [Branch1] [C] [C] [O] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [Branch1] [C] [F] [C] [Branch1] [C] [O] [C] [C] [Ring2] [Ring1] [C] [Branch1] [C] [C] [C] [Ring2] [Ring1] [=Branch1] [Branch1] [Branch1] [O] [Ring2] [Ring1] [#Branch2] [C] [=Branch1] [C] [=O] [C] [O],-3.928,CC5(C)OC4CC3C2CCC1=CC(=O)C=CC1(C)C2(F)C(O)CC3(C)C4(O5)C(=O)CO 
+698,[N] [C] [=N] [C] [NH1] [C] [=N] [C] [=Ring1] [Branch1] [C] [=Branch1] [C] [=O] [NH1] [Ring1] [#Branch2],-0.67,Nc2nc1[nH]cnc1c(=O)[nH]2 
+699,[C] [C] [C] [C] [C] [C] [Branch1] [P] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [S] [C] [C] [C] [Ring2] [Ring1] [Ring1] [O],-3.882,CC34CCC1C(CCC2CC(=O)CCC12C)C3CCC4O 
+700,[C] [C] [C] [C] [Branch1] [C] [O] [C] [=C],-1.199,CCCC(O)C=C
+701,[O] [C] [Branch2] [Ring2] [N] [C] [=C] [C] [C] [C] [Branch2] [Ring1] [#Branch2] [C] [Ring1] [=Branch1] [C] [Ring1] [Branch1] [=C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=N] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [N] [C] [Ring2] [Ring1] [Branch1] [=O] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=N] [Ring1] [=Branch1],-4.238,OC(C1=CC2C5C(C1C2=C(c3ccccc3)c4ccccn4)C(=O)NC5=O)(c6ccccc6)c7ccccn7 
+702,[C] [C] [C] [C] [O] [C] [C] [C] [C],-2.135,CCCCOCCCC
+703,[C] [C] [N] [C] [=N] [C] [Branch1] [Branch2] [N] [Branch1] [C] [C] [C] [C] [O] [=C] [C] [=C] [Ring1] [O] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring2] [Ring1] [Branch1] [Ring1] [=Branch1],-3.335,CCN2c1nc(N(C)(CCO))ccc1NC(=O)c3cccnc23 
+704,[N] [C] [=N] [C] [=Branch1] [C] [=O] [NH1] [C] [=C] [Ring1] [#Branch1] [F],-0.132,Nc1nc(=O)[nH]cc1F
+705,[C] [C] [C] [C] [O] [C] [=C] [C] [=C] [Branch1] [Branch2] [C] [=Branch1] [C] [=O] [O] [C] [C] [C] [=Branch1] [Ring2] [=C] [Ring1] [O] [N] [Branch1] [Ring1] [C] [C] [C] [C],-5.127999999999999,CCCCOc1ccc(C(=O)OCC)c(c1)N(CC)CC 
+706,[C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [O],-2.017,CCCCCC(C)(C)O
+707,[C] [C] [=C] [Branch1] [C] [C] [C] [Branch1] [C] [C] [=C] [Branch1] [C] [C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch2] [C],-4.361000000000001,Cc1c(C)c(C)c(C)c(C)c1C
+708,[C] [=C] [N] [=C] [N] [=C] [N] [=C] [C] [Ring1] [=Branch1] [=N] [Ring1] [#Branch2],-0.906,c2cnc1ncncc1n2
+709,[C] [=C] [C] [=C] [N] [C] [C] [C] [Ring1] [Branch1] [=C] [Ring1] [=Branch2],-2.195,c2ccc1NCCc1c2 
+710,[C] [C] [C] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C],-1.266,CCC(C)C(=O)C
+711,[O] [C] [Branch2] [Ring1] [#C] [C] [C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [Branch1] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O] [C] [Ring1] [O] [=O] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [#Branch1],-7.877000000000001,OC(CC(c1ccccc1)c3c(O)c2ccccc2oc3=O)c4ccc(cc4)c5ccc(Br)cc5 
+712,[C] [C] [Branch1] [C] [C] [N] [Branch1] [=Branch1] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [S] [C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [Cl],-4.578,CC(C)N(C(C)C)C(=O)SCC(Cl)=C(Cl)Cl
+713,[C] [=C] [C] [=C] [NH1] [C] [=C] [C] [Ring1] [Branch1] [=C] [Ring1] [=Branch2],-2.654,c2ccc1[nH]ccc1c2
+714,[C] [C] [C] [C] [C] [C] [Branch1] [P] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [S] [C] [C] [C] [Ring2] [Ring1] [Ring1] [=O],-3.393,CC34CCC1C(CCC2=CC(=O)CCC12C)C3CCC4=O
+715,[C] [C] [=C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=Branch1] [#Branch2] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [O] [S] [Branch1] [C] [N] [=Branch1] [C] [=O] [=O],-3.642,Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O
+716,[C] [C] [C] [C] [C] [C] [C] [C] [Ring1] [=Branch1],-3.245,CCC1CCCCC1
+717,[C] [O] [C] [=Branch1] [C] [=O] [N] [C] [=N] [C] [=C] [C] [=C] [Branch1] [Branch2] [C] [=C] [Ring1] [=Branch1] [NH1] [Ring1] [=Branch2] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.118,COC(=O)Nc2nc1ccc(cc1[nH]2)C(=O)c3ccccc3
+718,[C] [C] [N] [C] [=N] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring2] [Ring1] [C] [Ring1] [=Branch1],-3.446,CCN2c1nc(Cl)ccc1N(C)C(=O)c3cccnc23 
+719,[C] [N] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#Branch2] [Ring1] [=Branch1],-3.087,CNC(=O)Oc1cccc2ccccc12
+720,[C] [#C],-0.252,C#C
+721,[C] [C] [=C] [N] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [#Branch1],-2.0980000000000003,Cc1cncc(C)c1
+722,[C] [C] [=C] [C] [C] [=C] [Ring1] [=Branch1],-1.842,C1C=CCC=C1
+723,[C] [C] [O] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [S] [P] [=Branch1] [C] [=S] [Branch1] [Ring2] [O] [C] [C] [O] [C] [C],-3.738,CCOC(=O)N(C)C(=O)CSP(=S)(OCC)OCC
+724,[C] [C] [Branch1] [C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.919,CC(O)c1ccccc1
+725,[C] [C] [Branch1] [C] [Cl] [C] [Cl],-1.794,CC(Cl)CCl
+726,[C] [C] [C] [C] [=C] [Branch1] [Ring1] [C] [C] [C] [=O],-2.081,CCCC=C(CC)C=O
+727,[C] [C] [=Branch1] [C] [=O] [O] [C] [Branch1] [C] [C] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [C] [C] [C] [Ring2] [Ring1] [Ring2] [Ring1] [S] [C],-4.863,CC(=O)OC3(C)CCC4C2CCC1=CC(=O)CCC1(C)C2CCC34C 
+728,[Cl] [C] [=C] [C] [=C] [Branch1] [Branch1] [C] [=C] [Ring1] [=Branch1] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [C] [=C] [Ring1] [#Branch1] [Cl],-5.604,Clc1ccc(cc1)c2c(Cl)cccc2Cl 
+729,[O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [Cl],-5.645,Oc1cc(Cl)ccc1Oc2ccc(Cl)cc2Cl
+730,[C] [C] [C] [C] [Branch1] [C] [O] [C] [C] [Branch1] [P] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [P] [C] [C] [C] [Ring2] [Ring1] [Ring2] [C] [=Branch1] [C] [=O] [C] [O],-3.454,CC12CC(O)C3C(CCC4=CC(=O)CCC34C)C2CCC1C(=O)CO
+731,[C] [C] [=C] [C] [Branch1] [C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [Branch2],-3.375,Cc1cc(C)cc(C)c1
+732,[C] [C] [C] [C] [C] [C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [O] [C] [C] [C] [C] [C] [C] [C] [C],-7.148,CCCCCCCCOC(=O)c1ccccc1C(=O)OCCCCCCCC 
+733,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [O],-4.586,CCCCCCCCCCCCCCCO
+734,[Cl] [C] [=C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [C] [=C] [Ring1] [#Branch1] [Cl],-5.915,Clc1cccc(Cl)c1c2c(Cl)cccc2Cl 
+735,[O] [=C] [N] [C] [=Branch1] [C] [=O] [N] [C] [=Branch1] [C] [=O] [C] [Ring1] [Branch2] [Branch1] [C] [C] [C],-0.556,O=C1NC(=O)NC(=O)C1(C)C
+736,[C] [C] [Branch1] [C] [C] [I],-2.486,CC(C)I
+737,[C] [C] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C],-0.912,CC(C)C(=O)C
+738,[C] [C] [=C] [C] [=C] [C] [S] [C] [=N] [N] [=C] [N] [Ring1] [Branch1] [C] [Ring1] [N] [=Ring1] [Branch2],-2.8680000000000003,Cc2cccc3sc1nncn1c23 
+739,[C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [C],-1.909,CCCCCCC(=O)C
+740,[C] [C] [C] [C] [C] [C] [C] [C] [C] [=Branch1] [C] [=O] [O] [C],-2.962,CCCCCCCCC(=O)OC
+741,[O] [=C] [N] [Branch1] [=N] [C] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [Ring1] [#Branch1] [=O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring2] [Ring1] [C] [Ring1] [=Branch1],-1.944,O=C1N(C2CCC(=O)NC2=O)C(=O)c3ccccc13
+742,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [Branch1] [#Branch1] [C] [C] [C] [C] [Ring1] [Branch1] [C] [=Branch1] [C] [=O] [N] [Ring1] [N],-0.966,O=C2NC(=O)C1(CCCC1)C(=O)N2
+743,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [C] [C] [C] [C] [C] [C] [C] [C] [Ring1] [Branch2],-2.629,CN(C)C(=O)NC1CCCCCCC1
+744,[Cl] [C] [Branch2] [Ring1] [=C] [C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [C] [Ring1] [#Branch2] [Branch1] [C] [Cl] [C] [Ring1] [=Branch2] [Branch1] [C] [Cl] [Cl] [C] [Ring1] [Branch2] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [C] [Ring2] [Ring1] [Ring1] [Ring1] [#C] [Cl],-6.155,ClC1(C2(Cl)C3(Cl)C4(Cl)C5(Cl)C1(Cl)C3(Cl)Cl)C5(Cl)C(Cl)(Cl)C24Cl
+745,[C] [N] [Branch1] [C] [C] [C] [=C] [Branch1] [C] [C] [N] [Branch1] [C] [C] [N] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Ring1] [=N] [=O],-2.129,CN(C)c2c(C)n(C)n(c1ccccc1)c2=O 
+746,[C] [C] [C] [Branch1] [C] [O] [C] [C],-0.97,CCC(O)CC
+747,[C] [C] [Branch1] [C] [C] [O] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [O] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [#Branch1],-5.832999999999999,CC(C)OC(=O)C(O)(c1ccc(Br)cc1)c2ccc(Br)cc2 
+748,[N] [C] [C] [=N] [N] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [C] [=Ring1] [=N] [Cl],-2.603,Nc2cnn(c1ccccc1)c(=O)c2Cl
+749,[C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [O],-0.954,CCC(C)(C)O
+750,[C] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1],-2.313,Cc1ccc(O)cc1
+751,[C] [C] [O] [C] [=O],-0.402,CCOC=O
+752,[C] [C] [C] [C] [C] [C] [C] [C] [C] [Ring1] [=Branch1] [C] [Ring1] [#Branch2],-3.715,C1CCC2CCCCC2C1
+753,[C] [C] [C] [C] [S],-1.676,CCCCS
+754,[C] [=C] [C] [=C] [C] [=Branch1] [Ring2] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#C] [C] [Ring1] [=Branch1] [=C] [Ring1] [#Branch2] [Ring1] [=C],-6.007000000000001,c1ccc2c(c1)c3cccc4ccc5cccc2c5c43
+755,[Cl] [C] [=Branch1] [=Branch1] [=C] [Branch1] [C] [Cl] [Cl] [Cl],-3.063,ClC(=C(Cl)Cl)Cl
+756,[C] [C] [C] [=Branch1] [C] [=O] [C] [C],-0.912,CCC(=O)CC
+757,[C] [=C] [C] [#N],-0.354,C=CC#N
+758,[C] [C] [C] [C] [C] [C] [C] [Branch1] [C] [F] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [=N] [Branch1] [C] [F] [C] [Branch1] [C] [O] [C] [C] [Ring2] [Ring1] [Ring1] [Branch1] [C] [C] [C] [Ring2] [Ring1] [#Branch1] [Branch1] [C] [O] [C] [=Branch1] [C] [=O] [C] [O],-3.539,CC1CC2C3CC(F)C4=CC(=O)C=CC4(C)C3(F)C(O)CC2(C)C1(O)C(=O)CO
+759,[C] [C] [N] [C] [=N] [C] [Branch1] [#Branch2] [N] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [=N] [C] [Branch1] [Ring1] [O] [C] [=N] [Ring1] [=N],-3.505,CCNc1nc(NC(C)(C)C)nc(OC)n1
+760,[C] [C] [C] [C] [C] [Branch1] [C] [C] [C] [C],-3.3080000000000003,CCCCC(C)CC
+761,[Br] [C] [C] [Br],-2.102,BrCCBr
+762,[C] [N] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [C] [C],-2.734,CNC(=O)Oc1ccccc1C(C)C
+763,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [C] [Ring1] [=Branch1] [=C] [Ring1] [P] [Ring1] [#Branch2],-5.189,C1c2ccccc2c3ccc4ccccc4c13
+764,[C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Cl],-2.912,COc1ccccc1Cl
+765,[C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Br] [C] [=C] [Ring1] [Branch2] [Cl],-5.604,COP(=S)(OC)Oc1cc(Cl)c(Br)cc1Cl
+766,[Cl] [C] [Branch1] [C] [Cl] [C] [Branch1] [N] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1],-6.007999999999999,ClC(Cl)C(c1ccc(Cl)cc1)c2ccc(Cl)cc2 
+767,[N] [=Branch1] [#Branch2] [=N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.034,N(=Nc1ccccc1)c2ccccc2
+768,[C] [C] [Branch1] [C] [C] [C] [=C] [C] [=C] [Branch1] [C] [C] [C] [=C] [Ring1] [#Branch1],-3.617,CC(C)c1ccc(C)cc1
+769,[O] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [C] [=C] [Ring1] [#Branch1] [Cl],-3.012,Oc1c(Cl)cccc1Cl
+770,[O] [C] [C] [O] [C] [Branch2] [Ring1] [Branch1] [O] [C] [Branch1] [Ring1] [C] [O] [O] [C] [Branch1] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch2] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [Ring2] [O],0.31,OCC2OC(OC1(CO)OC(CO)C(O)C1O)C(O)C(O)C2O 
+771,[O] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch2] [O],-0.887,OC1C(O)C(O)C(O)C(O)C1O
+772,[C] [N] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C] [N] [=C] [N] [Branch1] [Branch2] [C] [C] [Branch1] [C] [O] [C] [O] [C] [=Ring1] [#Branch2] [C] [Ring1] [S] [=O],-0.847,Cn2c(=O)n(C)c1ncn(CC(O)CO)c1c2=O
+773,[C] [C] [C] [C] [C] [C] [Branch1] [P] [C] [C] [C] [C] [C] [S] [C] [Ring1] [Ring1] [C] [C] [Ring1] [O] [Ring1] [#Branch1] [C] [C] [Ring1] [S] [C] [C] [C] [Ring2] [Ring1] [Ring1] [O],-4.545,CC45CCC2C(CCC3CC1SC1CC23C)C4CCC5O
+774,[Br] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Br],-4.172,Brc1ccccc1Br
+775,[O] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [Branch2] [Cl],-3.648,Oc1c(Cl)cc(Cl)cc1Cl
+776,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [Branch1] [Ring2] [C] [C] [F] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring2] [Ring1] [Ring1] [Ring1] [=Branch1],-3.663,C2c1ccccc1N(CCF)C(=O)c3ccccc23 
+777,[C] [C] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [C] [C],-1.7519999999999998,CC(C)C(=O)C(C)C
+778,[C] [=C] [Branch1] [C] [O] [C] [C] [=Branch1] [C] [=O] [C] [C] [=C] [Branch1] [C] [O] [C] [=C] [C] [Ring1] [#Branch1] [O] [C] [Ring1] [N] [C] [=C] [Ring1] [P] [O] [C],-1.2919999999999998,c1c(O)C2C(=O)C3cc(O)ccC3OC2cc1(OC)
+779,[C] [N] [C] [=N] [C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [Ring1] [=N] [=Ring1] [#Branch2],-1.4980000000000002,Cn1cnc2n(C)c(=O)n(C)c(=O)c12
+780,[C] [C] [=Branch1] [C] [=O] [S] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [C] [C] [C] [Branch1] [C] [C] [C] [Branch1] [#C] [C] [C] [C] [Ring1] [=Branch1] [C] [C] [C] [=Branch1] [C] [=O] [O] [Ring1] [=Branch1] [C] [Ring2] [Ring1] [=Branch2] [Ring1] [#C],-3.842,CC(=O)SC4CC1=CC(=O)CCC1(C)C5CCC2(C)C(CCC23CCC(=O)O3)C45
+781,[O] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-4.254,O(c1ccccc1)c2ccccc2
+782,[Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [=Branch1] [C] [=C] [Ring1] [#Branch1] [Cl] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [Branch2] [Cl],-7.343,Clc1cc(Cl)c(cc1Cl)c2cc(Cl)c(Cl)cc2Cl 
+783,[N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring1] [=Branch1],-0.964,NC(=O)c1cccnc1 
+784,[S] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.758,Sc1ccccc1
+785,[C] [N] [C] [=Branch1] [C] [=O] [O] [C] [=C] [C] [Branch1] [C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [Branch2],-2.688,CNC(=O)Oc1cc(C)cc(C)c1
+786,[Cl] [C] [C] [C] [C] [Branch1] [Branch1] [C] [Ring1] [Branch1] [Cl] [C] [Branch1] [C] [Cl] [C] [=Branch2] [Ring1] [C] [=C] [Branch1] [C] [Cl] [C] [Ring1] [#Branch2] [Branch1] [C] [Cl] [C] [Ring1] [Branch2] [Branch1] [C] [Cl] [Cl] [Cl],-6.039,ClC1CC2C(C1Cl)C3(Cl)C(=C(Cl)C2(Cl)C3(Cl)Cl)Cl
+787,[C] [S] [S] [C],-1.524,CSSC
+788,[N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-1.501,NC(=O)c1ccccc1
+789,[Cl] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Br],-3.84,Clc1ccccc1Br
+790,[C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O] [C] [O] [C] [Branch2] [Ring1] [C] [C] [O] [C] [O] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [Branch2] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [Ring1] [O],-1.493,COC(=O)c1ccccc1OC2OC(COC3OCC(O)C(O)C3O)C(O)C(O)C2O
+791,[C] [C] [N] [C] [=N] [C] [Branch1] [C] [C] [=C] [C] [Branch1] [C] [C] [=C] [Ring1] [Branch2] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [N] [=C] [Ring2] [Ring1] [C] [Ring1] [=Branch1],-3.891,CCN2c1nc(C)cc(C)c1NC(=O)c3cccnc23 
+792,[O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [Branch2],-3.428,Oc1cc(Cl)cc(Cl)c1
+793,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [Ring1] [#Branch2],-4.87,Cc1cccc2c1ccc3ccccc32
+794,[C] [C] [C] [C] [C] [Branch1] [Ring1] [C] [C] [C] [O],-2.089,CCCCC(CC)CO
+795,[C] [C] [Branch1] [C] [C] [N] [Branch1] [=Branch1] [C] [Branch1] [C] [C] [C] [C] [=Branch1] [C] [=O] [S] [C] [C] [=Branch1] [Ring1] [=C] [Cl] [Cl],-3.827,CC(C)N(C(C)C)C(=O)SCC(=CCl)Cl
+796,[C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.713,Cc1ccccc1
+797,[C] [C] [C] [C] [=C] [C] [C] [Ring1] [#Branch1],-2.599,C1CCC=CCC1
+798,[C] [O] [C] [=C] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [C] [C] [C] [Ring1] [Branch2] [O] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [Ring1] [O] [C] [=C] [C] [Branch1] [Ring1] [O] [C] [=C] [Ring1] [O] [C] [Ring1] [=C] [=O],-3.3280000000000003,COC1=CC(=O)CC(C)C13Oc2c(Cl)c(OC)cc(OC)c2C3=O
+799,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [O],-2.814,CCCCCCCCCCO
+800,[C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [C],-2.938,CCC(C)(C)CC
+801,[C] [N] [C] [=Branch1] [C] [=O] [C] [Branch1] [C] [C] [S] [C] [C] [S] [P] [=Branch1] [C] [=O] [Branch1] [Ring1] [O] [C] [O] [C],-1.446,CNC(=O)C(C)SCCSP(=O)(OC)(OC)
+802,[O] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-4.335,Oc1cc(Cl)c(Cl)c(Cl)c1Cl
+803,[C] [C] [C] [C] [=O],-0.7490000000000001,CCCC=O
+804,[C] [C] [C] [C] [C] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Ring1] [#Branch1] [Branch1] [C] [C] [C] [Ring1] [N] [Branch1] [C] [F] [C] [Branch1] [C] [O] [C] [C] [Ring2] [Ring1] [C] [Branch1] [C] [C] [C] [Ring2] [Ring1] [=Branch1] [Branch1] [C] [O] [C] [=Branch1] [C] [=O] [C] [O] [C] [Branch1] [C] [C] [=O],-3.933,CC4CC3C2CCC1=CC(=O)C=CC1(C)C2(F)C(O)CC3(C)C4(O)C(=O)COC(C)=O 
+805,[C] [C] [C] [C],-1.907,CCCC
+806,[C] [O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [O],-1.941,COc1ccccc1O
+807,[C] [C] [C] [C] [C] [C] [C] [C] [Branch1] [C] [O] [Branch1] [=Branch1] [C] [=Branch1] [C] [=O] [C] [C] [Ring1] [=Branch2] [Branch1] [C] [C] [C] [C] [Branch1] [C] [O] [C] [Ring1] [#C] [Branch1] [C] [F] [C] [Branch1] [C] [C] [C] [=C] [C] [=Branch1] [C] [=O] [C] [=C] [Ring2] [Ring1] [#Branch2] [Ring1] [Branch2],-3.507,CC1CC2C3CCC(O)(C(=O)C)C3(C)CC(O)C2(F)C4(C)C=CC(=O)C=C14
+808,[Cl] [C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [Branch1] [C] [Cl] [Cl],-3.382,ClC(Cl)C(Cl)(Cl)Cl
+809,[C] [C] [O] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [O] [C] [C],-3.016,CCOC(=O)c1ccccc1C(=O)OCC
+810,[C] [C] [Branch1] [C] [C] [C] [O],-0.672,CC(C)CO
+811,[C] [C] [Branch1] [C] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.57,CC(C)Cc1ccccc1
+812,[I] [C] [I],-2.958,ICI
+813,[C] [C] [C] [C] [Branch1] [C] [O] [C] [C] [C],-1.6780000000000002,CCCC(O)CCC
+814,[C] [C] [C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C],-1.833,CCCCCOC(=O)C
+815,[O] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-4.203,Oc1c(Cl)c(Cl)cc(Cl)c1Cl
+816,[C] [C] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.281,CCCc1ccccc1
+817,[F] [C] [Branch1] [C] [F] [Branch1] [C] [Cl] [C] [Branch1] [C] [F] [Branch1] [C] [F] [Cl],-2.697,FC(F)(Cl)C(F)(F)Cl
+818,[C] [C] [=C] [C] [=O],-0.604,CC=CC=O
+819,[C] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C],-0.495,CN(C)C(=O)N(C)C 
+820,[C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [N] [C] [=Branch1] [C] [=O] [C] [=C] [Branch1] [C] [O] [C] [=C] [C] [=C] [Ring1] [#Branch1],-3.782,c1ccccc1NC(=O)c2c(O)cccc2
+821,[C] [C] [N] [Branch1] [Ring1] [C] [C] [C] [=Branch1] [C] [=S] [S] [C] [C] [Branch1] [C] [Cl] [=C],-3.254,CCN(CC)C(=S)SCC(Cl)=C
+822,[Cl] [C] [C],-1.165,ClCC
+823,[C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [Branch1] [P] [N] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [Branch1] [C] [F] [Branch1] [C] [F] [F] [=C] [Branch1] [C] [C] [C] [=C] [Ring1] [#C] [C],-3.165,CC(=O)Nc1cc(NS(=O)(=O)C(F)(F)F)c(C)cc1C
+824,[O] [=C] [Branch2] [Ring1] [C] [C] [=C] [C] [=C] [C] [=C] [C] [=C] [O] [C] [O] [C] [Ring1] [Branch1] [=C] [Ring1] [=Branch2] [N] [C] [C] [C] [C] [C] [Ring1] [=Branch1],-3.659,O=C(C=CC=Cc2ccc1OCOc1c2)N3CCCCC3
+825,[C] [C] [/C] [=C] [\C],-2.076,CC/C=C\C
+826,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [Branch1] [#Branch2] [C] [C] [C] [C] [C] [C] [C] [Ring1] [Branch2] [C] [=Branch1] [C] [=O] [N] [Ring1] [#C],-2.2840000000000003,O=C2NC(=O)C1(CCCCCCC1)C(=O)N2
+827,[C] [Branch1] [=Branch2] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [=C] [C] [Branch1] [=Branch2] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [=C] [C] [Branch1] [Branch2] [O] [C] [=Branch1] [C] [=O] [N] [C] [=C] [Ring2] [Ring1] [Ring1],-4.642,c1(C(C)(C)C)cc(C(C)(C)C)cc(OC(=O)NC)c1
+828,[O] [C] [=C] [C] [Branch1] [C] [O] [=C] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [Branch2] [O] [C] [Ring1] [#Branch1] [=C] [Ring1] [N] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [=C] [Ring1] [Branch2],-3.152,Oc2cc(O)c1C(=O)CC(Oc1c2)c3ccc(O)c(O)c3
+829,[O] [=C] [Branch1] [=Branch2] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.612,O=C(c1ccccc1)c2ccccc2
+830,[C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C] [C],-7.576,CCCCCCCCCCCCCCCCCCCC
+831,[N] [Branch1] [#Branch2] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-3.492,N(Nc1ccccc1)c2ccccc2 
+832,[C] [C] [C] [Branch1] [Ring1] [C] [C] [C] [O],-1.381,CCC(CC)CO
+833,[O] [C] [=C] [C] [=N] [C] [=C] [Ring1] [=Branch1],-1.655,Oc1ccncc1
+834,[Cl] [\C] [=C] [/Cl],-1.561,Cl\C=C/Cl
+835,[C] [C] [C] [C] [C] [C] [Ring1] [Branch1],-2.452,CC1CCCC1
+836,[C] [C] [Branch1] [C] [C] [C] [C] [Branch1] [C] [C] [O],-1.308,CC(C)CC(C)O
+837,[O] [C] [=C] [C] [=C] [Branch1] [C] [N] [C] [=C] [Ring1] [#Branch1] [N] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [=C] [C] [Branch1] [C] [C] [=C] [C] [=C] [Ring2] [Ring1] [Ring1] [Ring1] [#Branch1],-3.125,O2c1ccc(N)cc1N(C)C(=O)c3cc(C)ccc23 
+838,[C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C] [=Branch1] [C] [=O] [C] [Branch1] [=N] [O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [N] [C] [=N] [C] [=N] [Ring1] [Branch1],-4.132,CC(C)(C)C(=O)C(Oc1ccc(Cl)cc1)n2cncn2
+839,[C] [C] [=C] [C] [=Branch1] [Branch1] [=N] [O] [Ring1] [Branch1] [C] [=Branch1] [C] [=O] [N] [N] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1],-2.251,Cc1cc(no1)C(=O)NNCc2ccccc2
+840,[O] [C] [=C] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Ring1] [#Branch1] [C] [C] [=C] [C] [Branch1] [C] [Cl] [=C] [C] [=C] [Ring1] [#Branch1] [O],-4.924,Oc1ccc(Cl)cc1Cc2cc(Cl)ccc2O
+841,[O] [=C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [#C] [Ring1] [=Branch1],-3.34,O=C1c2ccccc2C(=O)c3ccccc13
+842,[C] [C] [=C] [Branch1] [=C] [C] [=Branch1] [C] [=O] [N] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [C] [O] [Ring1] [P],-2.169,CC1=C(C(=O)Nc2ccccc2)S(=O)(=O)CCO1
+843,[O] [=C] [N] [C] [=Branch1] [C] [=O] [C] [=Branch1] [C] [=O] [N] [Ring1] [#Branch1],1.091,O=C1NC(=O)C(=O)N1 
+844,[C] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [O] [C] [=C] [C] [=C] [Branch2] [Ring1] [#Branch1] [S] [C] [=C] [C] [=C] [Branch1] [N] [O] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [O] [C] [C] [=C] [Ring1] [=N] [C] [=C] [Ring2] [Ring1] [Ring2],-6.678,COP(=S)(OC)Oc1ccc(Sc2ccc(OP(=S)(OC)OC)cc2)cc1
+845,[N] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [C] [=C] [C] [=Branch1] [#Branch1] [=C] [C] [=C] [Ring1] [=Branch1] [Cl] [C] [Branch1] [C] [O] [N] [C] [=Branch1] [C] [=O] [C] [=C] [C] [=C] [C] [=C] [Ring1] [O] [Ring1] [=Branch1],-2.564,NS(=O)(=O)c1cc(ccc1Cl)C2(O)NC(=O)c3ccccc23
+846,[C] [C] [Branch1] [C] [C] [C] [O] [C] [=Branch1] [C] [=O] [C],-1.463,CC(C)COC(=O)C
+847,[C] [C] [Branch1] [C] [C] [C] [Branch1] [C] [C] [Branch1] [C] [C] [C],-2.922,CC(C)C(C)(C)C
+848,[Cl] [C] [=C] [C] [=C] [Branch1] [=Branch2] [C] [Branch1] [C] [Cl] [=C] [Ring1] [#Branch1] [Cl] [C] [=C] [Branch1] [C] [Cl] [C] [=C] [Branch1] [C] [Cl] [C] [Branch1] [C] [Cl] [=C] [Ring1] [=Branch2] [Cl],-7.746,Clc1ccc(c(Cl)c1Cl)c2c(Cl)cc(Cl)c(Cl)c2Cl 
+849,[N] [#C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [#N],-1.717,N#Cc1ccccc1C#N
+850,[F] [C] [Branch1] [C] [F] [Branch1] [C] [F] [C] [Branch1] [C] [Cl] [Br],-2.608,FC(F)(F)C(Cl)Br 
+851,[C] [N] [C] [=Branch1] [C] [=O] [O] [N] [=C] [Branch1] [Ring1] [S] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C],-0.908,CNC(=O)ON=C(SC)C(=O)N(C)C
+852,[C] [C] [S] [C] [C] [S] [P] [=Branch1] [C] [=S] [Branch1] [Ring1] [O] [C] [O] [C],-3.323,CCSCCSP(=S)(OC)OC
+853,[C] [C] [C] [Branch1] [C] [C] [C],-2.245,CCC(C)C
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+,Selected Models,Dataset,Task,Result
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+from sklearn.metrics import roc_auc_score, roc_curve
+
+import datetime
+import os
+import umap
+import numpy as np
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import pickle
+import json
+
+from xgboost import XGBClassifier, XGBRegressor
+import xgboost as xgb
+from sklearn.metrics import roc_auc_score, mean_squared_error
+import xgboost as xgb
+from sklearn.svm import SVR
+from sklearn.linear_model import LinearRegression
+from sklearn.kernel_ridge import KernelRidge
+import json
+from sklearn.compose import TransformedTargetRegressor
+from sklearn.preprocessing import MinMaxScaler
+
+
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+from .selfies_model.load import SELFIES as bart
+from .mhg_model import load as mhg
+from .smi_ted.smi_ted_light.load import load_smi_ted
+
+datasets = {}
+models = {}
+downstream_models ={}
+
+
+def avail_models_data():
+    global datasets
+    global models
+
+    datasets = [{"Dataset": "hiv", "Input": "smiles", "Output": "HIV_active", "Path": "data/hiv", "Timestamp": "2024-06-26 11:27:37"},
+  {"Dataset": "esol", "Input": "smiles", "Output": "ESOL predicted log solubility in mols per litre", "Path": "data/esol", "Timestamp": "2024-06-26 11:31:46"},
+  {"Dataset": "freesolv", "Input": "smiles", "Output": "expt", "Path": "data/freesolv", "Timestamp": "2024-06-26 11:33:47"},
+  {"Dataset": "lipo", "Input": "smiles", "Output": "y", "Path": "data/lipo", "Timestamp": "2024-06-26 11:34:37"},
+  {"Dataset": "bace", "Input": "smiles", "Output": "Class", "Path": "data/bace", "Timestamp": "2024-06-26 11:36:40"},
+  {"Dataset": "bbbp", "Input": "smiles", "Output": "p_np", "Path": "data/bbbp", "Timestamp": "2024-06-26 11:39:23"},
+  {"Dataset": "clintox", "Input": "smiles", "Output": "CT_TOX", "Path": "data/clintox", "Timestamp": "2024-06-26 11:42:43"}]
+
+
+    models = [{"Name": "bart","Model Name": "SELFIES-TED","Description": "BART model for string based SELFIES modality", "Timestamp": "2024-06-21 12:32:20"},
+  {"Name": "mol-xl","Model Name": "Molformer", "Description": "MolFormer model for string based SMILES modality", "Timestamp": "2024-06-21 12:35:56"},
+  {"Name": "mhg", "Model Name": "MHG-GED","Description": "Molecular hypergraph model", "Timestamp": "2024-07-10 00:09:42"},
+  {"Name": "smi-ted", "Model Name": "SMI-TED","Description": "SMILES based encoder decoder model", "Timestamp": "2024-07-10 00:09:42"}]
+
+
+def avail_models(raw=False):
+    global models
+
+    models = [{"Name": "smi-ted", "Model Name": "SMI-TED","Description": "SMILES based encoder decoder model"},
+              {"Name": "bart","Model Name": "SELFIES-TED","Description": "BART model for string based SELFIES modality"},
+              {"Name": "mol-xl","Model Name": "Molformer", "Description": "MolFormer model for string based SMILES modality"},
+              {"Name": "mhg", "Model Name": "MHG-GED","Description": "Molecular hypergraph model"},
+  ]
+
+
+
+    if raw: return models
+    else:
+        return pd.DataFrame(models).drop('Name', axis=1)
+
+    return models
+
+def avail_downstream_models():
+    global downstream_models
+
+    with open("downstream_models.json", "r") as outfile:
+        downstream_models = json.load(outfile)
+    return downstream_models
+
+def avail_datasets():
+    global datasets
+
+    datasets = [{"Dataset": "hiv", "Input": "smiles", "Output": "HIV_active", "Path": "data/hiv",
+                 "Timestamp": "2024-06-26 11:27:37"},
+                {"Dataset": "esol", "Input": "smiles", "Output": "ESOL predicted log solubility in mols per litre",
+                 "Path": "data/esol", "Timestamp": "2024-06-26 11:31:46"},
+                {"Dataset": "freesolv", "Input": "smiles", "Output": "expt", "Path": "data/freesolv",
+                 "Timestamp": "2024-06-26 11:33:47"},
+                {"Dataset": "lipo", "Input": "smiles", "Output": "y", "Path": "data/lipo",
+                 "Timestamp": "2024-06-26 11:34:37"},
+                {"Dataset": "bace", "Input": "smiles", "Output": "Class", "Path": "data/bace",
+                 "Timestamp": "2024-06-26 11:36:40"},
+                {"Dataset": "bbbp", "Input": "smiles", "Output": "p_np", "Path": "data/bbbp",
+                 "Timestamp": "2024-06-26 11:39:23"},
+                {"Dataset": "clintox", "Input": "smiles", "Output": "CT_TOX", "Path": "data/clintox",
+                 "Timestamp": "2024-06-26 11:42:43"}]
+
+    return datasets
+
+def reset():
+
+    """datasets = {"esol": ["smiles", "ESOL predicted log solubility in mols per litre", "data/esol", "2024-06-26 11:36:46.509324"],
+           "freesolv": ["smiles", "expt", "data/freesolv", "2024-06-26 11:37:37.393273"],
+           "lipo": ["smiles", "y", "data/lipo", "2024-06-26 11:37:37.393273"],
+           "hiv": ["smiles", "HIV_active", "data/hiv",  "2024-06-26 11:37:37.393273"],
+           "bace": ["smiles", "Class", "data/bace", "2024-06-26 11:38:40.058354"],
+           "bbbp": ["smiles", "p_np", "data/bbbp","2024-06-26 11:38:40.058354"],
+           "clintox": ["smiles", "CT_TOX", "data/clintox","2024-06-26 11:38:40.058354"],
+           "sider": ["smiles","1:", "data/sider","2024-06-26 11:38:40.058354"],
+           "tox21": ["smiles",":-2", "data/tox21","2024-06-26 11:38:40.058354"]
+           }"""
+
+    datasets = [
+      {"Dataset": "hiv", "Input": "smiles", "Output": "HIV_active", "Path": "data/hiv", "Timestamp": "2024-06-26 11:27:37"},
+      {"Dataset": "esol", "Input": "smiles", "Output": "ESOL predicted log solubility in mols per litre", "Path": "data/esol", "Timestamp": "2024-06-26 11:31:46"},
+      {"Dataset": "freesolv", "Input": "smiles", "Output": "expt", "Path": "data/freesolv", "Timestamp": "2024-06-26 11:33:47"},
+      {"Dataset": "lipo", "Input": "smiles", "Output": "y", "Path": "data/lipo", "Timestamp": "2024-06-26 11:34:37"},
+      {"Dataset": "bace", "Input": "smiles", "Output": "Class", "Path": "data/bace", "Timestamp": "2024-06-26 11:36:40"},
+      {"Dataset": "bbbp", "Input": "smiles", "Output": "p_np", "Path": "data/bbbp", "Timestamp": "2024-06-26 11:39:23"},
+      {"Dataset": "clintox", "Input": "smiles", "Output": "CT_TOX", "Path": "data/clintox", "Timestamp": "2024-06-26 11:42:43"},
+      #{"Dataset": "sider", "Input": "smiles", "Output": "1:", "path": "data/sider", "Timestamp": "2024-06-26 11:38:40.058354"},
+      #{"Dataset": "tox21", "Input": "smiles", "Output": ":-2", "path": "data/tox21", "Timestamp": "2024-06-26 11:38:40.058354"}
+    ]
+
+    models = [{"Name": "bart", "Description": "BART model for string based SELFIES modality",
+      "Timestamp": "2024-06-21 12:32:20"},
+     {"Name": "mol-xl", "Description": "MolFormer model for string based SMILES modality",
+      "Timestamp": "2024-06-21 12:35:56"},
+     {"Name": "mhg", "Description": "MHG", "Timestamp": "2024-07-10 00:09:42"},
+     {"Name": "spec-gru", "Description": "Spectrum modality with GRU", "Timestamp": "2024-07-10 00:09:42"},
+     {"Name": "spec-lstm", "Description": "Spectrum modality with LSTM", "Timestamp": "2024-07-10 00:09:54"},
+     {"Name": "3d-vae", "Description": "VAE model for 3D atom positions", "Timestamp": "2024-07-10 00:10:08"}]
+
+
+    downstream_models = [
+        {"Name": "XGBClassifier", "Description": "XG Boost Classifier",
+         "Timestamp": "2024-06-21 12:31:20"},
+        {"Name": "XGBRegressor", "Description": "XG Boost Regressor",
+         "Timestamp": "2024-06-21 12:32:56"},
+        {"Name": "2-FNN", "Description": "A two layer feedforward network",
+         "Timestamp": "2024-06-24 14:34:16"},
+        {"Name": "3-FNN", "Description": "A three layer feedforward network",
+         "Timestamp": "2024-06-24 14:38:37"},
+    ]
+
+    with open("datasets.json", "w") as outfile:
+        json.dump(datasets, outfile)
+
+    with open("models.json", "w") as outfile:
+        json.dump(models, outfile)
+
+    with open("downstream_models.json", "w") as outfile:
+        json.dump(downstream_models, outfile)
+
+def update_data_list(list_data):
+    #datasets[list_data[0]] = list_data[1:]
+
+    with open("datasets.json", "w") as outfile:
+        json.dump(datasets, outfile)
+
+    avail_models_data()
+
+def update_model_list(list_model):
+    #models[list_model[0]] = list_model[1]
+
+    with open("models.json", "w") as outfile:
+        json.dump(list_model, outfile)
+
+    avail_models_data()
+
+def update_downstream_model_list(list_model):
+    #models[list_model[0]] = list_model[1]
+
+    with open("downstream_models.json", "w") as outfile:
+        json.dump(list_model, outfile)
+
+    avail_models_data()
+
+avail_models_data()
+
+def get_representation(train_data,test_data,model_type, return_tensor=True):
+    alias = {"MHG-GED": "mhg", "SELFIES-TED": "bart", "MolFormer": "mol-xl", "Molformer": "mol-xl", "SMI-TED": "smi-ted"}
+    if model_type in alias.keys():
+        model_type = alias[model_type]
+
+    if model_type == "mhg":
+        model = mhg.load("models/mhg_model/pickles/mhggnn_pretrained_model_0724_2023.pickle")
+        with torch.no_grad():
+            train_emb = model.encode(train_data)
+            x_batch = torch.stack(train_emb)
+
+            test_emb = model.encode(test_data)
+            x_batch_test = torch.stack(test_emb)
+        if not return_tensor:
+            x_batch = pd.DataFrame(x_batch)
+            x_batch_test = pd.DataFrame(x_batch_test)
+
+
+
+    elif model_type == "bart":
+        model = bart()
+        model.load()
+        x_batch = model.encode(train_data, return_tensor=return_tensor)
+        x_batch_test = model.encode(test_data, return_tensor=return_tensor)
+
+    elif model_type == "smi-ted":
+        model = load_smi_ted(folder='./models/smi_ted/smi_ted_light', ckpt_filename='smi-ted-Light_40.pt')
+        with torch.no_grad():
+            x_batch = model.encode(train_data, return_torch=return_tensor)
+            x_batch_test = model.encode(test_data, return_torch=return_tensor)
+
+    elif model_type == "mol-xl":
+        model = AutoModel.from_pretrained("ibm/MoLFormer-XL-both-10pct", deterministic_eval=True,
+                                          trust_remote_code=True)
+        tokenizer = AutoTokenizer.from_pretrained("ibm/MoLFormer-XL-both-10pct", trust_remote_code=True)
+
+        if type(train_data) == list:
+            inputs = tokenizer(train_data, padding=True, return_tensors="pt")
+        else:
+            inputs = tokenizer(list(train_data.values), padding=True, return_tensors="pt")
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        x_batch = outputs.pooler_output
+
+        if type(test_data) == list:
+            inputs = tokenizer(test_data, padding=True, return_tensors="pt")
+        else:
+            inputs = tokenizer(list(test_data.values), padding=True, return_tensors="pt")
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        x_batch_test = outputs.pooler_output
+
+        if not return_tensor:
+            x_batch = pd.DataFrame(x_batch)
+            x_batch_test = pd.DataFrame(x_batch_test)
+
+
+    return x_batch, x_batch_test
+
+def single_modal(model,dataset, downstream_model,params):
+    print(model)
+    alias = {"MHG-GED":"mhg", "SELFIES-TED": "bart", "MolFormer":"mol-xl", "SMI-TED": "smi-ted"}
+    data = avail_models(raw=True)
+    df = pd.DataFrame(data)
+    print(list(df["Name"].values))
+    if alias[model] in list(df["Name"].values):
+        if model in alias.keys():
+            model_type = alias[model]
+        else:
+            model_type = model
+    else:
+        print("Model not available")
+        return
+
+    data = avail_datasets()
+    df = pd.DataFrame(data)
+    print(list(df["Dataset"].values))
+
+    if dataset in list(df["Dataset"].values):
+        task = dataset
+        with open(f"./representation/{task}_{model_type}.pkl", "rb") as f1:
+            x_batch, y_batch, x_batch_test, y_batch_test = pickle.load(f1)
+        print(f" Representation loaded successfully")
+    else:
+
+        print("Custom Dataset")
+        #return
+        components = dataset.split(",")
+        train_data = pd.read_csv(components[0])[components[2]]
+        test_data = pd.read_csv(components[1])[components[2]]
+
+        y_batch = pd.read_csv(components[0])[components[3]]
+        y_batch_test = pd.read_csv(components[1])[components[3]]
+
+
+        x_batch,  x_batch_test = get_representation(train_data,test_data,model_type)
+
+
+
+        print(f" Representation loaded successfully")
+
+
+
+
+
+    print(f" Calculating ROC AUC Score ...")
+
+    if downstream_model == "XGBClassifier":
+        xgb_predict_concat = XGBClassifier(**params) # n_estimators=5000, learning_rate=0.01, max_depth=10
+        xgb_predict_concat.fit(x_batch, y_batch)
+
+        y_prob = xgb_predict_concat.predict_proba(x_batch_test)[:, 1]
+
+        roc_auc = roc_auc_score(y_batch_test, y_prob)
+        fpr, tpr, _ = roc_curve(y_batch_test, y_prob)
+        print(f"ROC-AUC Score: {roc_auc:.4f}")
+
+        try:
+            with open(f"./plot_emb/{task}_{model_type}.pkl", "rb") as f1:
+                class_0,class_1 = pickle.load(f1)
+        except:
+            print("Generating latent plots")
+            reducer = umap.UMAP(metric='euclidean', n_neighbors=10, n_components=2, low_memory=True, min_dist=0.1,
+                                verbose=False)
+            n_samples = np.minimum(1000, len(x_batch))
+            features_umap = reducer.fit_transform(x_batch[:n_samples])
+            x = y_batch.values[:n_samples]
+            index_0 = [index for index in range(len(x)) if x[index] == 0]
+            index_1 = [index for index in range(len(x)) if x[index] == 1]
+
+            class_0 = features_umap[index_0]
+            class_1 = features_umap[index_1]
+            print("Generating latent plots : Done")
+
+        #vizualize(roc_auc,fpr, tpr, x_batch, y_batch )
+
+        result = f"ROC-AUC Score: {roc_auc:.4f}"
+
+        return result, roc_auc,fpr, tpr, class_0, class_1
+
+    elif downstream_model == "DefaultClassifier":
+        xgb_predict_concat = XGBClassifier() # n_estimators=5000, learning_rate=0.01, max_depth=10
+        xgb_predict_concat.fit(x_batch, y_batch)
+
+        y_prob = xgb_predict_concat.predict_proba(x_batch_test)[:, 1]
+
+        roc_auc = roc_auc_score(y_batch_test, y_prob)
+        fpr, tpr, _ = roc_curve(y_batch_test, y_prob)
+        print(f"ROC-AUC Score: {roc_auc:.4f}")
+
+        try:
+            with open(f"./plot_emb/{task}_{model_type}.pkl", "rb") as f1:
+                class_0,class_1 = pickle.load(f1)
+        except:
+            print("Generating latent plots")
+            reducer = umap.UMAP(metric='euclidean', n_neighbors=  10, n_components=2, low_memory=True, min_dist=0.1, verbose=False)
+            n_samples = np.minimum(1000,len(x_batch))
+            features_umap = reducer.fit_transform(x_batch[:n_samples])
+            x = y_batch.values[:n_samples]
+            index_0 = [index for index in range(len(x)) if x[index] == 0]
+            index_1 = [index for index in range(len(x)) if x[index] == 1]
+
+            class_0 = features_umap[index_0]
+            class_1 = features_umap[index_1]
+            print("Generating latent plots : Done")
+
+        #vizualize(roc_auc,fpr, tpr, x_batch, y_batch )
+
+        result = f"ROC-AUC Score: {roc_auc:.4f}"
+
+        return result, roc_auc,fpr, tpr, class_0, class_1
+
+    elif downstream_model == "SVR":
+        regressor = SVR(**params)
+        model = TransformedTargetRegressor(regressor= regressor,
+                                                transformer = MinMaxScaler(feature_range=(-1, 1))
+                                                ).fit(x_batch,y_batch)
+
+        y_prob = model.predict(x_batch_test)
+        RMSE_score = np.sqrt(mean_squared_error(y_batch_test, y_prob))
+
+        print(f"RMSE Score: {RMSE_score:.4f}")
+        result = f"RMSE Score: {RMSE_score:.4f}"
+
+        print("Generating latent plots")
+        reducer = umap.UMAP(metric='euclidean', n_neighbors=10, n_components=2, low_memory=True, min_dist=0.1,
+                            verbose=False)
+        n_samples = np.minimum(1000, len(x_batch))
+        features_umap = reducer.fit_transform(x_batch[:n_samples])
+        x = y_batch.values[:n_samples]
+        #index_0 = [index for index in range(len(x)) if x[index] == 0]
+        #index_1 = [index for index in range(len(x)) if x[index] == 1]
+
+        class_0 = features_umap#[index_0]
+        class_1 = features_umap#[index_1]
+        print("Generating latent plots : Done")
+
+        return result, RMSE_score,y_batch_test, y_prob, class_0, class_1
+
+    elif downstream_model == "Kernel Ridge":
+        regressor = KernelRidge(**params)
+        model = TransformedTargetRegressor(regressor=regressor,
+                                           transformer=MinMaxScaler(feature_range=(-1, 1))
+                                           ).fit(x_batch, y_batch)
+
+        y_prob = model.predict(x_batch_test)
+        RMSE_score = np.sqrt(mean_squared_error(y_batch_test, y_prob))
+
+        print(f"RMSE Score: {RMSE_score:.4f}")
+        result = f"RMSE Score: {RMSE_score:.4f}"
+
+        print("Generating latent plots")
+        reducer = umap.UMAP(metric='euclidean', n_neighbors=10, n_components=2, low_memory=True, min_dist=0.1,
+                            verbose=False)
+        n_samples = np.minimum(1000, len(x_batch))
+        features_umap = reducer.fit_transform(x_batch[:n_samples])
+        x = y_batch.values[:n_samples]
+        # index_0 = [index for index in range(len(x)) if x[index] == 0]
+        # index_1 = [index for index in range(len(x)) if x[index] == 1]
+
+        class_0 = features_umap#[index_0]
+        class_1 = features_umap#[index_1]
+        print("Generating latent plots : Done")
+
+        return result, RMSE_score, y_batch_test, y_prob, class_0, class_1
+
+
+    elif downstream_model == "Linear Regression":
+        regressor = LinearRegression(**params)
+        model = TransformedTargetRegressor(regressor=regressor,
+                                           transformer=MinMaxScaler(feature_range=(-1, 1))
+                                           ).fit(x_batch, y_batch)
+
+        y_prob = model.predict(x_batch_test)
+        RMSE_score = np.sqrt(mean_squared_error(y_batch_test, y_prob))
+
+        print(f"RMSE Score: {RMSE_score:.4f}")
+        result = f"RMSE Score: {RMSE_score:.4f}"
+
+        print("Generating latent plots")
+        reducer = umap.UMAP(metric='euclidean', n_neighbors=10, n_components=2, low_memory=True, min_dist=0.1,
+                            verbose=False)
+        n_samples = np.minimum(1000, len(x_batch))
+        features_umap = reducer.fit_transform(x_batch[:n_samples])
+        x = y_batch.values[:n_samples]
+        # index_0 = [index for index in range(len(x)) if x[index] == 0]
+        # index_1 = [index for index in range(len(x)) if x[index] == 1]
+
+        class_0 = features_umap#[index_0]
+        class_1 = features_umap#[index_1]
+        print("Generating latent plots : Done")
+
+        return result, RMSE_score, y_batch_test, y_prob, class_0, class_1
+
+
+    elif downstream_model == "DefaultRegressor":
+        regressor = SVR(kernel="rbf", degree=3, C=5, gamma="scale", epsilon=0.01)
+        model = TransformedTargetRegressor(regressor=regressor,
+                                           transformer=MinMaxScaler(feature_range=(-1, 1))
+                                           ).fit(x_batch, y_batch)
+
+        y_prob = model.predict(x_batch_test)
+        RMSE_score = np.sqrt(mean_squared_error(y_batch_test, y_prob))
+
+        print(f"RMSE Score: {RMSE_score:.4f}")
+        result = f"RMSE Score: {RMSE_score:.4f}"
+
+        print("Generating latent plots")
+        reducer = umap.UMAP(metric='euclidean', n_neighbors=10, n_components=2, low_memory=True, min_dist=0.1,
+                            verbose=False)
+        n_samples = np.minimum(1000, len(x_batch))
+        features_umap = reducer.fit_transform(x_batch[:n_samples])
+        x = y_batch.values[:n_samples]
+        # index_0 = [index for index in range(len(x)) if x[index] == 0]
+        # index_1 = [index for index in range(len(x)) if x[index] == 1]
+
+        class_0 = features_umap#[index_0]
+        class_1 = features_umap#[index_1]
+        print("Generating latent plots : Done")
+
+        return result, RMSE_score, y_batch_test, y_prob, class_0, class_1
+
+
+def multi_modal(model_list,dataset, downstream_model,params):
+    print(model_list)
+    data = avail_datasets()
+    df = pd.DataFrame(data)
+    list(df["Dataset"].values)
+
+    if dataset in list(df["Dataset"].values):
+        task = dataset
+        predefined = True
+    else:
+        predefined = False
+        components = dataset.split(",")
+        train_data = pd.read_csv(components[0])[components[2]]
+        test_data = pd.read_csv(components[1])[components[2]]
+
+        y_batch = pd.read_csv(components[0])[components[3]]
+        y_batch_test = pd.read_csv(components[1])[components[3]]
+
+        print("Custom Dataset loaded")
+
+
+    data = avail_models(raw=True)
+    df = pd.DataFrame(data)
+    list(df["Name"].values)
+
+    alias = {"MHG-GED":"mhg", "SELFIES-TED": "bart", "MolFormer":"mol-xl", "SMI-TED":"smi-ted"}
+    #if set(model_list).issubset(list(df["Name"].values)):
+    if set(model_list).issubset(list(alias.keys())):
+        for i, model in enumerate(model_list):
+            if model in alias.keys():
+                model_type = alias[model]
+            else:
+                model_type = model
+
+            if i == 0:
+                if predefined:
+                    with open(f"./representation/{task}_{model_type}.pkl", "rb") as f1:
+                        x_batch, y_batch, x_batch_test, y_batch_test = pickle.load(f1)
+                    print(f" Loaded representation/{task}_{model_type}.pkl")
+                else:
+                    x_batch, x_batch_test = get_representation(train_data, test_data, model_type)
+                    x_batch = pd.DataFrame(x_batch)
+                    x_batch_test = pd.DataFrame(x_batch_test)
+
+            else:
+                if predefined:
+                    with open(f"./representation/{task}_{model_type}.pkl", "rb") as f1:
+                        x_batch_1, y_batch_1, x_batch_test_1, y_batch_test_1 = pickle.load(f1)
+                        print(f" Loaded representation/{task}_{model_type}.pkl")
+                else:
+                    x_batch_1, x_batch_test_1 = get_representation(train_data, test_data, model_type)
+                    x_batch_1 = pd.DataFrame(x_batch_1)
+                    x_batch_test_1 = pd.DataFrame(x_batch_test_1)
+
+                x_batch = pd.concat([x_batch, x_batch_1], axis=1)
+                x_batch_test = pd.concat([x_batch_test, x_batch_test_1], axis=1)
+
+
+    else:
+        print("Model not available")
+        return
+
+    num_columns = x_batch_test.shape[1]
+    x_batch_test.columns = [f'{i + 1}' for i in range(num_columns)]
+
+    num_columns = x_batch.shape[1]
+    x_batch.columns = [f'{i + 1}' for i in range(num_columns)]
+
+
+    print(f"Representations loaded successfully")
+    try:
+        with open(f"./plot_emb/{task}_multi.pkl", "rb") as f1:
+            class_0, class_1 = pickle.load(f1)
+    except:
+        print("Generating latent plots")
+        reducer = umap.UMAP(metric='euclidean', n_neighbors=10, n_components=2, low_memory=True, min_dist=0.1,
+                            verbose=False)
+        n_samples = np.minimum(1000, len(x_batch))
+        features_umap = reducer.fit_transform(x_batch[:n_samples])
+
+        if "Classifier" in downstream_model:
+            x = y_batch.values[:n_samples]
+            index_0 = [index for index in range(len(x)) if x[index] == 0]
+            index_1 = [index for index in range(len(x)) if x[index] == 1]
+
+            class_0 = features_umap[index_0]
+            class_1 = features_umap[index_1]
+
+        else:
+            class_0 = features_umap
+            class_1 = features_umap
+
+        print("Generating latent plots : Done")
+
+    print(f" Calculating ROC AUC Score ...")
+
+
+    if downstream_model == "XGBClassifier":
+        xgb_predict_concat = XGBClassifier(**params)#n_estimators=5000, learning_rate=0.01, max_depth=10)
+        xgb_predict_concat.fit(x_batch, y_batch)
+
+        y_prob = xgb_predict_concat.predict_proba(x_batch_test)[:, 1]
+
+
+        roc_auc = roc_auc_score(y_batch_test, y_prob)
+        fpr, tpr, _ = roc_curve(y_batch_test, y_prob)
+        print(f"ROC-AUC Score: {roc_auc:.4f}")
+
+        #vizualize(roc_auc,fpr, tpr, x_batch, y_batch )
+
+        #vizualize(x_batch_test, y_batch_test)
+        print(f"ROC-AUC Score: {roc_auc:.4f}")
+        result = f"ROC-AUC Score: {roc_auc:.4f}"
+
+        return result, roc_auc,fpr, tpr, class_0, class_1
+
+    elif downstream_model == "DefaultClassifier":
+        xgb_predict_concat = XGBClassifier()#n_estimators=5000, learning_rate=0.01, max_depth=10)
+        xgb_predict_concat.fit(x_batch, y_batch)
+
+        y_prob = xgb_predict_concat.predict_proba(x_batch_test)[:, 1]
+
+
+        roc_auc = roc_auc_score(y_batch_test, y_prob)
+        fpr, tpr, _ = roc_curve(y_batch_test, y_prob)
+        print(f"ROC-AUC Score: {roc_auc:.4f}")
+
+        #vizualize(roc_auc,fpr, tpr, x_batch, y_batch )
+
+        #vizualize(x_batch_test, y_batch_test)
+        print(f"ROC-AUC Score: {roc_auc:.4f}")
+        result = f"ROC-AUC Score: {roc_auc:.4f}"
+
+        return result, roc_auc,fpr, tpr, class_0, class_1
+
+    elif downstream_model == "SVR":
+        regressor = SVR(**params)
+        model = TransformedTargetRegressor(regressor= regressor,
+                                                transformer = MinMaxScaler(feature_range=(-1, 1))
+                                                ).fit(x_batch,y_batch)
+
+        y_prob = model.predict(x_batch_test)
+        RMSE_score = np.sqrt(mean_squared_error(y_batch_test, y_prob))
+
+        print(f"RMSE Score: {RMSE_score:.4f}")
+        result = f"RMSE Score: {RMSE_score:.4f}"
+
+        return result, RMSE_score,y_batch_test, y_prob, class_0, class_1
+
+    elif downstream_model == "Linear Regression":
+        regressor = LinearRegression(**params)
+        model = TransformedTargetRegressor(regressor=regressor,
+                                           transformer=MinMaxScaler(feature_range=(-1, 1))
+                                           ).fit(x_batch, y_batch)
+
+        y_prob = model.predict(x_batch_test)
+        RMSE_score = np.sqrt(mean_squared_error(y_batch_test, y_prob))
+
+        print(f"RMSE Score: {RMSE_score:.4f}")
+        result = f"RMSE Score: {RMSE_score:.4f}"
+
+        return result, RMSE_score, y_batch_test, y_prob, class_0, class_1
+
+    elif downstream_model == "Kernel Ridge":
+        regressor = KernelRidge(**params)
+        model = TransformedTargetRegressor(regressor=regressor,
+                                           transformer=MinMaxScaler(feature_range=(-1, 1))
+                                           ).fit(x_batch, y_batch)
+
+        y_prob = model.predict(x_batch_test)
+        RMSE_score = np.sqrt(mean_squared_error(y_batch_test, y_prob))
+
+        print(f"RMSE Score: {RMSE_score:.4f}")
+        result = f"RMSE Score: {RMSE_score:.4f}"
+
+        return result, RMSE_score, y_batch_test, y_prob, class_0, class_1
+
+    elif downstream_model == "DefaultRegressor":
+        regressor = SVR(kernel="rbf", degree=3, C=5, gamma="scale", epsilon=0.01)
+        model = TransformedTargetRegressor(regressor=regressor,
+                                           transformer=MinMaxScaler(feature_range=(-1, 1))
+                                           ).fit(x_batch, y_batch)
+
+        y_prob = model.predict(x_batch_test)
+        RMSE_score = np.sqrt(mean_squared_error(y_batch_test, y_prob))
+
+        print(f"RMSE Score: {RMSE_score:.4f}")
+        result = f"RMSE Score: {RMSE_score:.4f}"
+
+        return result, RMSE_score, y_batch_test, y_prob, class_0, class_1
+
+
+
+
+
+
diff --git a/models/mhg_model/.DS_Store b/models/mhg_model/.DS_Store
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index 0000000000000000000000000000000000000000..85a8b9e57f27745d4007e144fe3c9c4d2bc9e52e
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diff --git a/models/mhg_model/README.md b/models/mhg_model/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b855ff28edd655aedc5097cae88fbb812dd06f76
--- /dev/null
+++ b/models/mhg_model/README.md
@@ -0,0 +1,75 @@
+# mhg-gnn
+
+This repository provides PyTorch source code assosiated with our publication, "MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network"
+
+**Paper:** [Arxiv Link](https://arxiv.org/pdf/2309.16374)
+
+![mhg-gnn](images/mhg_example1.png)
+
+## Introduction
+
+We present MHG-GNN, an autoencoder architecture
+that has an encoder based on GNN and a decoder based on a sequential model with MHG.
+Since the encoder is a GNN variant, MHG-GNN can accept any molecule as input, and  
+demonstrate high predictive performance on molecular graph data.
+In addition, the decoder inherits the theoretical guarantee of MHG on always generating a structurally valid molecule as output.
+
+## Table of Contents
+
+1. [Getting Started](#getting-started)
+    1. [Pretrained Models and Training Logs](#pretrained-models-and-training-logs)
+    2. [Installation](#installation)
+2. [Feature Extraction](#feature-extraction)
+
+## Getting Started
+
+**This code and environment have been tested on Intel E5-2667 CPUs at 3.30GHz and NVIDIA A100 Tensor Core GPUs.**
+
+### Pretrained Models and Training Logs
+
+We provide checkpoints of the MHG-GNN model pre-trained on a dataset of ~1.34M molecules curated from PubChem. (later) For model weights: [HuggingFace Link]()
+
+Add the MHG-GNN `pre-trained weights.pt` to the `models/` directory according to your needs. 
+
+### Installation
+
+We recommend to create a virtual environment. For example:
+
+```
+python3 -m venv .venv
+. .venv/bin/activate
+```
+
+Type the following command once the virtual environment is activated:
+
+```
+git clone git@github.ibm.com:CMD-TRL/mhg-gnn.git
+cd ./mhg-gnn
+pip install .
+```
+
+## Feature Extraction
+
+The example notebook [mhg-gnn_encoder_decoder_example.ipynb](notebooks/mhg-gnn_encoder_decoder_example.ipynb) contains code to load checkpoint files and use the pre-trained model for encoder and decoder tasks.
+
+To load mhg-gnn, you can simply use:
+
+```python
+import torch
+import load
+
+model = load.load()
+```
+
+To encode SMILES into embeddings, you can use:
+
+```python
+with torch.no_grad():
+    repr = model.encode(["CCO", "O=C=O", "OC(=O)c1ccccc1C(=O)O"])
+```
+
+For decoder, you can use the function, so you can return from embeddings to SMILES strings:
+
+```python
+orig = model.decode(repr)
+```
\ No newline at end of file
diff --git a/models/mhg_model/__init__.py b/models/mhg_model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed12f0d3c8ed176384f16d26197882c9ad47a36c
--- /dev/null
+++ b/models/mhg_model/__init__.py
@@ -0,0 +1,5 @@
+# -*- coding:utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
\ No newline at end of file
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diff --git a/models/mhg_model/graph_grammar/__init__.py b/models/mhg_model/graph_grammar/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..26f82acdc3d0383157745e5f0fe8ddd870325145
--- /dev/null
+++ b/models/mhg_model/graph_grammar/__init__.py
@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jan 1 2018"
+
diff --git a/models/mhg_model/graph_grammar/__pycache__/__init__.cpython-310.pyc b/models/mhg_model/graph_grammar/__pycache__/__init__.cpython-310.pyc
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diff --git a/models/mhg_model/graph_grammar/algo/__init__.py b/models/mhg_model/graph_grammar/algo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6f597c0a768b8dd64708ec70fe7071000953ce8
--- /dev/null
+++ b/models/mhg_model/graph_grammar/algo/__init__.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jan 1 2018"
+
diff --git a/models/mhg_model/graph_grammar/algo/__pycache__/__init__.cpython-310.pyc b/models/mhg_model/graph_grammar/algo/__pycache__/__init__.cpython-310.pyc
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diff --git a/models/mhg_model/graph_grammar/algo/tree_decomposition.py b/models/mhg_model/graph_grammar/algo/tree_decomposition.py
new file mode 100644
index 0000000000000000000000000000000000000000..81cb7748f573c99597c8f0658555b9efd1171cfa
--- /dev/null
+++ b/models/mhg_model/graph_grammar/algo/tree_decomposition.py
@@ -0,0 +1,821 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2017"
+__version__ = "0.1"
+__date__ = "Dec 11 2017"
+
+from copy import deepcopy
+from itertools import combinations
+from ..hypergraph import Hypergraph
+import networkx as nx
+import numpy as np
+
+
+class CliqueTree(nx.Graph):
+    ''' clique tree object
+
+    Attributes
+    ----------
+    hg : Hypergraph
+        This hypergraph will be decomposed.
+    root_hg : Hypergraph
+        Hypergraph on the root node.
+    ident_node_dict : dict
+        ident_node_dict[key_node] gives a list of nodes that are identical (i.e., the adjacent hyperedges are common)
+    '''
+    def __init__(self, hg=None, **kwargs):
+        self.hg = deepcopy(hg)
+        if self.hg is not None:
+            self.ident_node_dict = self.hg.get_identical_node_dict()
+        else:
+            self.ident_node_dict = {}
+        super().__init__(**kwargs)
+
+    @property
+    def root_hg(self):
+        ''' return the hypergraph on the root node
+        '''
+        return self.nodes[0]['subhg']
+
+    @root_hg.setter
+    def root_hg(self, hypergraph):
+        ''' set the hypergraph on the root node
+        '''
+        self.nodes[0]['subhg'] = hypergraph
+
+    def insert_subhg(self, subhypergraph: Hypergraph) -> None:
+        ''' insert a subhypergraph, which is extracted from a root hypergraph, into the tree.
+
+        Parameters
+        ----------
+        subhg : Hypergraph
+        '''
+        num_nodes = self.number_of_nodes()
+        self.add_node(num_nodes, subhg=subhypergraph)
+        self.add_edge(num_nodes, 0)
+        adj_nodes = deepcopy(list(self.adj[0].keys()))
+        for each_node in adj_nodes:
+            if len(self.nodes[each_node]["subhg"].nodes.intersection(
+                    self.nodes[num_nodes]["subhg"].nodes)\
+                   - self.root_hg.nodes) != 0 and each_node != num_nodes:
+                self.remove_edge(0, each_node)
+                self.add_edge(each_node, num_nodes)
+
+    def to_irredundant(self) -> None:
+        ''' convert the clique tree to be irredundant
+        '''
+        for each_node in self.hg.nodes:
+            subtree = self.subgraph([
+                each_tree_node for each_tree_node in self.nodes()\
+                if each_node in self.nodes[each_tree_node]["subhg"].nodes]).copy()
+            leaf_node_list = [x for x in subtree.nodes() if subtree.degree(x)==1]
+            redundant_leaf_node_list = []
+            for each_leaf_node in leaf_node_list:
+                if len(self.nodes[each_leaf_node]["subhg"].adj_edges(each_node)) == 0:
+                    redundant_leaf_node_list.append(each_leaf_node)
+            for each_red_leaf_node in redundant_leaf_node_list:
+                current_node = each_red_leaf_node
+                while subtree.degree(current_node) == 1 \
+                      and len(subtree.nodes[current_node]["subhg"].adj_edges(each_node)) == 0:
+                    self.nodes[current_node]["subhg"].remove_node(each_node)
+                    remove_node = current_node
+                    current_node = list(dict(subtree[remove_node]).keys())[0]
+                    subtree.remove_node(remove_node)
+
+        fixed_node_set = deepcopy(self.nodes)
+        for each_node in fixed_node_set:
+            if self.nodes[each_node]["subhg"].num_edges == 0:
+                if len(self[each_node]) == 1:
+                    self.remove_node(each_node)
+                elif len(self[each_node]) == 2:
+                    self.add_edge(*self[each_node])
+                    self.remove_node(each_node)
+                else:
+                    pass
+            else:
+                pass
+
+        redundant = True
+        while redundant:
+            redundant = False
+            fixed_edge_set = deepcopy(self.edges)
+            remove_node_set = set()
+            for node_1, node_2 in fixed_edge_set:
+                if node_1 in remove_node_set or node_2 in remove_node_set:
+                    pass
+                else:
+                    if self.nodes[node_1]['subhg'].is_subhg(self.nodes[node_2]['subhg']):
+                        redundant = True
+                        adj_node_list = set(self.adj[node_1]) - {node_2}
+                        self.remove_node(node_1)
+                        remove_node_set.add(node_1)
+                        for each_node in adj_node_list:
+                            self.add_edge(node_2, each_node)
+
+                    elif self.nodes[node_2]['subhg'].is_subhg(self.nodes[node_1]['subhg']):
+                        redundant = True
+                        adj_node_list = set(self.adj[node_2]) - {node_1}
+                        self.remove_node(node_2)
+                        remove_node_set.add(node_2)
+                        for each_node in adj_node_list:
+                            self.add_edge(node_1, each_node)
+
+    def node_update(self, key_node: str, subhg) -> None:
+        """ given a pair of a hypergraph, H, and its subhypergraph, sH, return a hypergraph H\sH.
+
+        Parameters
+        ----------
+        key_node : str
+            key node that must be removed.
+        subhg : Hypegraph
+        """
+        for each_edge in subhg.edges:
+            self.root_hg.remove_edge(each_edge)
+        self.root_hg.remove_nodes(self.ident_node_dict[key_node])
+
+        adj_node_list = list(subhg.nodes)
+        for each_node in subhg.nodes:
+            if each_node not in self.ident_node_dict[key_node]:
+                if set(self.root_hg.adj_edges(each_node)).issubset(subhg.edges):
+                    self.root_hg.remove_node(each_node)
+                    adj_node_list.remove(each_node)
+            else:
+                adj_node_list.remove(each_node)
+
+        for each_node_1, each_node_2 in combinations(adj_node_list, 2):
+            if not self.root_hg.is_adj(each_node_1, each_node_2):
+                self.root_hg.add_edge(set([each_node_1, each_node_2]), attr_dict=dict(tmp=True))
+        
+        subhg.remove_edges_with_attr({'tmp' : True})
+        self.insert_subhg(subhg)
+
+    def update(self, subhg, remove_nodes=False):
+        """ given a pair of a hypergraph, H, and its subhypergraph, sH, return a hypergraph H\sH.
+
+        Parameters
+        ----------
+        subhg : Hypegraph
+        """
+        for each_edge in subhg.edges:
+            self.root_hg.remove_edge(each_edge)
+        if remove_nodes:
+            remove_edge_list = []
+            for each_edge in self.root_hg.edges:
+                if set(self.root_hg.nodes_in_edge(each_edge)).issubset(subhg.nodes)\
+                   and self.root_hg.edge_attr(each_edge).get('tmp', False):
+                    remove_edge_list.append(each_edge)
+            self.root_hg.remove_edges(remove_edge_list)
+
+        adj_node_list = list(subhg.nodes)
+        for each_node in subhg.nodes:
+            if self.root_hg.degree(each_node) == 0:
+                self.root_hg.remove_node(each_node)
+                adj_node_list.remove(each_node)
+
+        if len(adj_node_list) != 1 and not remove_nodes:
+            self.root_hg.add_edge(set(adj_node_list), attr_dict=dict(tmp=True))
+        '''
+        else:
+            for each_node_1, each_node_2 in combinations(adj_node_list, 2):
+                if not self.root_hg.is_adj(each_node_1, each_node_2):
+                    self.root_hg.add_edge(
+                        [each_node_1, each_node_2], attr_dict=dict(tmp=True))
+        '''
+        subhg.remove_edges_with_attr({'tmp':True})
+        self.insert_subhg(subhg)
+
+
+def _get_min_deg_node(hg, ident_node_dict: dict, mode='mol'):
+    if mode == 'standard':
+        degree_dict = hg.degrees()
+        min_deg_node = min(degree_dict, key=degree_dict.get)
+        min_deg_subhg = hg.adj_subhg(min_deg_node, ident_node_dict)
+        return min_deg_node, min_deg_subhg
+    elif mode == 'mol':
+        degree_dict = hg.degrees()
+        min_deg = min(degree_dict.values())
+        min_deg_node_list = [each_node for each_node in hg.nodes if degree_dict[each_node]==min_deg]
+        min_deg_subhg_list = [hg.adj_subhg(each_min_deg_node, ident_node_dict)
+                              for each_min_deg_node in min_deg_node_list]
+        best_score = np.inf
+        best_idx = -1
+        for each_idx in range(len(min_deg_subhg_list)):
+            if min_deg_subhg_list[each_idx].num_nodes < best_score:
+                best_idx = each_idx
+        return min_deg_node_list[each_idx], min_deg_subhg_list[each_idx]
+    else:
+        raise ValueError
+
+
+def tree_decomposition(hg, irredundant=True):
+    """ compute a tree decomposition of the input hypergraph
+
+    Parameters
+    ----------
+    hg : Hypergraph
+        hypergraph to be decomposed
+    irredundant : bool
+        if True, irredundant tree decomposition will be computed.
+
+    Returns
+    -------
+    clique_tree : nx.Graph
+        each node contains a subhypergraph of `hg`
+    """
+    org_hg = hg.copy()
+    ident_node_dict = hg.get_identical_node_dict()
+    clique_tree = CliqueTree(org_hg)
+    clique_tree.add_node(0, subhg=org_hg)
+    while True:
+        degree_dict = org_hg.degrees()
+        min_deg_node = min(degree_dict, key=degree_dict.get)
+        min_deg_subhg = org_hg.adj_subhg(min_deg_node, ident_node_dict)
+        if org_hg.nodes == min_deg_subhg.nodes:
+            break
+
+        # org_hg and min_deg_subhg are divided
+        clique_tree.node_update(min_deg_node, min_deg_subhg)
+
+    clique_tree.root_hg.remove_edges_with_attr({'tmp' : True})
+
+    if irredundant:
+        clique_tree.to_irredundant()
+    return clique_tree
+
+
+def tree_decomposition_with_hrg(hg, hrg, irredundant=True, return_root=False):
+    ''' compute a tree decomposition given a hyperedge replacement grammar.
+    the resultant clique tree should induce a less compact HRG.
+    
+    Parameters
+    ----------
+    hg : Hypergraph
+        hypergraph to be decomposed
+    hrg : HyperedgeReplacementGrammar
+        current HRG
+    irredundant : bool
+        if True, irredundant tree decomposition will be computed.
+
+    Returns
+    -------
+    clique_tree : nx.Graph
+        each node contains a subhypergraph of `hg`
+    '''
+    org_hg = hg.copy()
+    ident_node_dict = hg.get_identical_node_dict()
+    clique_tree = CliqueTree(org_hg)
+    clique_tree.add_node(0, subhg=org_hg)
+    root_node = 0
+    
+    # construct a clique tree using HRG
+    success_any = True
+    while success_any:
+        success_any = False
+        for each_prod_rule in hrg.prod_rule_list:
+            org_hg, success, subhg = each_prod_rule.revert(org_hg, True)
+            if success:
+                if each_prod_rule.is_start_rule: root_node = clique_tree.number_of_nodes()
+                success_any = True
+                subhg.remove_edges_with_attr({'terminal' : False})
+                clique_tree.root_hg = org_hg
+                clique_tree.insert_subhg(subhg)
+    
+    clique_tree.root_hg = org_hg
+    
+    for each_edge in deepcopy(org_hg.edges):
+        if not org_hg.edge_attr(each_edge)['terminal']:
+            node_list = org_hg.nodes_in_edge(each_edge)
+            org_hg.remove_edge(each_edge)
+            
+            for each_node_1, each_node_2 in combinations(node_list, 2):
+                if not org_hg.is_adj(each_node_1, each_node_2):
+                    org_hg.add_edge([each_node_1, each_node_2], attr_dict=dict(tmp=True))
+
+    # construct a clique tree using the existing algorithm
+    degree_dict = org_hg.degrees()
+    if degree_dict:
+        while True:
+            min_deg_node, min_deg_subhg = _get_min_deg_node(org_hg, ident_node_dict)
+            if org_hg.nodes == min_deg_subhg.nodes: break
+
+            # org_hg and min_deg_subhg are divided
+            clique_tree.node_update(min_deg_node, min_deg_subhg)
+
+    clique_tree.root_hg.remove_edges_with_attr({'tmp' : True})
+    if irredundant:
+        clique_tree.to_irredundant()
+
+    if return_root:
+        if root_node == 0 and 0 not in clique_tree.nodes:
+            root_node = clique_tree.number_of_nodes()
+            while root_node not in clique_tree.nodes:
+                root_node -= 1
+        elif root_node not in clique_tree.nodes:
+            while root_node not in clique_tree.nodes:
+                root_node -= 1
+        else:
+            pass
+        return clique_tree, root_node
+    else:
+        return clique_tree
+
+
+def tree_decomposition_from_leaf(hg, irredundant=True):
+    """ compute a tree decomposition of the input hypergraph
+
+    Parameters
+    ----------
+    hg : Hypergraph
+        hypergraph to be decomposed
+    irredundant : bool
+        if True, irredundant tree decomposition will be computed.
+
+    Returns
+    -------
+    clique_tree : nx.Graph
+        each node contains a subhypergraph of `hg`
+    """
+    def apply_normal_decomposition(clique_tree):
+        degree_dict = clique_tree.root_hg.degrees()
+        min_deg_node = min(degree_dict, key=degree_dict.get)
+        min_deg_subhg = clique_tree.root_hg.adj_subhg(min_deg_node, clique_tree.ident_node_dict)
+        if clique_tree.root_hg.nodes == min_deg_subhg.nodes:
+            return clique_tree, False
+        clique_tree.node_update(min_deg_node, min_deg_subhg)
+        return clique_tree, True
+
+    def apply_min_edge_deg_decomposition(clique_tree):
+        edge_degree_dict = clique_tree.root_hg.edge_degrees()
+        non_tmp_edge_list = [each_edge for each_edge in clique_tree.root_hg.edges \
+                             if not clique_tree.root_hg.edge_attr(each_edge).get('tmp')]
+        if not non_tmp_edge_list:
+            return clique_tree, False
+        min_deg_edge = None
+        min_deg = np.inf
+        for each_edge in non_tmp_edge_list:
+            if min_deg > edge_degree_dict[each_edge]:
+                min_deg_edge = each_edge
+                min_deg = edge_degree_dict[each_edge]
+        node_list = clique_tree.root_hg.nodes_in_edge(min_deg_edge)
+        min_deg_subhg = clique_tree.root_hg.get_subhg(
+            node_list, [min_deg_edge], clique_tree.ident_node_dict)
+        if clique_tree.root_hg.nodes == min_deg_subhg.nodes:
+            return clique_tree, False
+        clique_tree.update(min_deg_subhg)
+        return clique_tree, True
+
+    org_hg = hg.copy()
+    clique_tree = CliqueTree(org_hg)
+    clique_tree.add_node(0, subhg=org_hg)
+
+    success = True
+    while success:
+        clique_tree, success = apply_min_edge_deg_decomposition(clique_tree)
+        if not success:
+            clique_tree, success = apply_normal_decomposition(clique_tree)
+
+    clique_tree.root_hg.remove_edges_with_attr({'tmp' : True})
+    if irredundant:
+        clique_tree.to_irredundant()
+    return clique_tree
+
+def topological_tree_decomposition(
+        hg, irredundant=True, rip_labels=True, shrink_cycle=False, contract_cycles=False):
+    ''' compute a tree decomposition of the input hypergraph
+
+    Parameters
+    ----------
+    hg : Hypergraph
+        hypergraph to be decomposed
+    irredundant : bool
+        if True, irredundant tree decomposition will be computed.
+
+    Returns
+    -------
+    clique_tree : CliqueTree
+        each node contains a subhypergraph of `hg`
+    '''
+    def _contract_tree(clique_tree):
+        ''' contract a single leaf
+
+        Parameters
+        ----------
+        clique_tree : CliqueTree
+
+        Returns
+        -------
+        CliqueTree, bool
+            bool represents whether this operation succeeds or not.
+        '''
+        edge_degree_dict = clique_tree.root_hg.edge_degrees()
+        leaf_edge_list = [each_edge for each_edge in clique_tree.root_hg.edges \
+                          if (not clique_tree.root_hg.edge_attr(each_edge).get('tmp'))\
+                          and edge_degree_dict[each_edge] == 1]
+        if not leaf_edge_list:
+            return clique_tree, False
+        min_deg_edge = leaf_edge_list[0]
+        node_list = clique_tree.root_hg.nodes_in_edge(min_deg_edge)
+        min_deg_subhg = clique_tree.root_hg.get_subhg(
+            node_list, [min_deg_edge], clique_tree.ident_node_dict)
+        if clique_tree.root_hg.nodes == min_deg_subhg.nodes:
+            return clique_tree, False
+        clique_tree.update(min_deg_subhg)
+        return clique_tree, True
+
+    def _rip_labels_from_cycles(clique_tree, org_hg):
+        ''' rip hyperedge-labels off
+
+        Parameters
+        ----------
+        clique_tree : CliqueTree
+        org_hg : Hypergraph
+
+        Returns
+        -------
+        CliqueTree, bool
+            bool represents whether this operation succeeds or not.
+        '''
+        ident_node_dict = clique_tree.ident_node_dict #hg.get_identical_node_dict()
+        for each_edge in clique_tree.root_hg.edges:
+            if each_edge in org_hg.edges:
+                if org_hg.in_cycle(each_edge):
+                    node_list = clique_tree.root_hg.nodes_in_edge(each_edge)
+                    subhg = clique_tree.root_hg.get_subhg(
+                        node_list, [each_edge], ident_node_dict)
+                    if clique_tree.root_hg.nodes == subhg.nodes:
+                        return clique_tree, False
+                    clique_tree.update(subhg)
+                    '''
+                    in_cycle_dict = {each_node: org_hg.node_attr(each_node)['is_in_ring'] for each_node in node_list}
+                    if not all(in_cycle_dict.values()):
+                        node_not_in_cycle = [each_node for each_node in in_cycle_dict.keys() if not in_cycle_dict[each_node]][0]
+                        node_list = [node_not_in_cycle]
+                        node_list.extend(clique_tree.root_hg.adj_nodes(node_not_in_cycle))
+                        edge_list = clique_tree.root_hg.adj_edges(node_not_in_cycle)
+                        import pdb; pdb.set_trace()
+                        subhg = clique_tree.root_hg.get_subhg(
+                            node_list, edge_list, ident_node_dict)
+                        
+                        clique_tree.update(subhg)
+                    '''
+                    return clique_tree, True
+        return clique_tree, False
+
+    def _shrink_cycle(clique_tree):
+        ''' shrink a cycle
+
+        Parameters
+        ----------
+        clique_tree : CliqueTree
+
+        Returns
+        -------
+        CliqueTree, bool
+            bool represents whether this operation succeeds or not.
+        '''
+        def filter_subhg(subhg, hg, key_node):
+            num_nodes_cycle = 0
+            nodes_in_cycle_list = []
+            for each_node in subhg.nodes:
+                if hg.in_cycle(each_node):
+                    num_nodes_cycle += 1
+                    if each_node != key_node:
+                        nodes_in_cycle_list.append(each_node)
+                if num_nodes_cycle > 3:
+                    break
+            if num_nodes_cycle != 3:
+                return False
+            else:
+                for each_edge in hg.edges:
+                    if set(nodes_in_cycle_list).issubset(hg.nodes_in_edge(each_edge)):
+                        return False
+                return True
+
+        #ident_node_dict = hg.get_identical_node_dict()
+        ident_node_dict = clique_tree.ident_node_dict
+        for each_node in clique_tree.root_hg.nodes:
+            if clique_tree.root_hg.in_cycle(each_node)\
+               and filter_subhg(clique_tree.root_hg.adj_subhg(each_node, ident_node_dict),
+                                clique_tree.root_hg,
+                                each_node):
+                target_node = each_node
+                target_subhg = clique_tree.root_hg.adj_subhg(target_node, ident_node_dict)
+                if clique_tree.root_hg.nodes == target_subhg.nodes:
+                    return clique_tree, False
+                clique_tree.update(target_subhg)
+                return clique_tree, True
+        return clique_tree, False
+
+    def _contract_cycles(clique_tree):
+        '''
+        remove a subhypergraph that looks like a cycle on a leaf.
+
+        Parameters
+        ----------
+        clique_tree : CliqueTree
+
+        Returns
+        -------
+        CliqueTree, bool
+            bool represents whether this operation succeeds or not.
+        '''
+        def _divide_hg(hg):
+            ''' divide a hypergraph into subhypergraphs such that
+            each subhypergraph is connected to each other in a tree-like way.
+
+            Parameters
+            ----------
+            hg : Hypergraph
+
+            Returns
+            -------
+            list of Hypergraphs
+                each element corresponds to a subhypergraph of `hg`
+            '''
+            for each_node in hg.nodes:
+                if hg.is_dividable(each_node):
+                    adj_edges_dict = {each_edge: hg.in_cycle(each_edge) for each_edge in hg.adj_edges(each_node)}
+                    '''
+                    if any(adj_edges_dict.values()):
+                        import pdb; pdb.set_trace()
+                        edge_in_cycle = [each_key for each_key, each_val in adj_edges_dict.items() if each_val][0]
+                        subhg1, subhg2, subhg3 = hg.divide(each_node, edge_in_cycle)
+                        return _divide_hg(subhg1) + _divide_hg(subhg2) + _divide_hg(subhg3)
+                    else:
+                    '''
+                    subhg1, subhg2 = hg.divide(each_node)
+                    return _divide_hg(subhg1) + _divide_hg(subhg2)
+            return [hg]
+
+        def _is_leaf(hg, divided_subhg) -> bool:
+            ''' judge whether subhg is a leaf-like in the original hypergraph
+
+            Parameters
+            ----------
+            hg : Hypergraph
+            divided_subhg : Hypergraph
+                `divided_subhg` is a subhypergraph of `hg`
+
+            Returns
+            -------
+            bool
+            '''
+            '''
+            adj_edges_set = set([])
+            for each_node in divided_subhg.nodes:
+                adj_edges_set.update(set(hg.adj_edges(each_node)))
+
+
+            _hg = deepcopy(hg)
+            _hg.remove_subhg(divided_subhg)
+            if nx.is_connected(_hg.hg) != (len(adj_edges_set - divided_subhg.edges) == 1):
+                import pdb; pdb.set_trace()
+            return len(adj_edges_set - divided_subhg.edges) == 1
+            '''
+            _hg = deepcopy(hg)
+            _hg.remove_subhg(divided_subhg)
+            return nx.is_connected(_hg.hg)
+        
+        subhg_list = _divide_hg(clique_tree.root_hg)
+        if len(subhg_list) == 1:
+            return clique_tree, False
+        else:
+            while len(subhg_list) > 1:
+                max_leaf_subhg = None
+                for each_subhg in subhg_list:
+                    if _is_leaf(clique_tree.root_hg, each_subhg):
+                        if max_leaf_subhg is None:
+                            max_leaf_subhg = each_subhg
+                        elif max_leaf_subhg.num_nodes < each_subhg.num_nodes:
+                            max_leaf_subhg = each_subhg
+                clique_tree.update(max_leaf_subhg)
+                subhg_list.remove(max_leaf_subhg)
+            return clique_tree, True
+
+    org_hg = hg.copy()
+    clique_tree = CliqueTree(org_hg)
+    clique_tree.add_node(0, subhg=org_hg)
+
+    success = True
+    while success:
+        '''
+        clique_tree, success = _rip_labels_from_cycles(clique_tree, hg)
+        if not success:
+            clique_tree, success = _contract_cycles(clique_tree)
+        '''
+        clique_tree, success = _contract_tree(clique_tree)
+        if not success:
+            if rip_labels:
+                clique_tree, success = _rip_labels_from_cycles(clique_tree, hg)
+            if not success:
+                if shrink_cycle:
+                    clique_tree, success = _shrink_cycle(clique_tree)
+                if not success:
+                    if contract_cycles:
+                        clique_tree, success = _contract_cycles(clique_tree)
+    clique_tree.root_hg.remove_edges_with_attr({'tmp' : True})
+    if irredundant:
+        clique_tree.to_irredundant()
+    return clique_tree
+
+def molecular_tree_decomposition(hg, irredundant=True):
+    """ compute a tree decomposition of the input molecular hypergraph
+
+    Parameters
+    ----------
+    hg : Hypergraph
+        molecular hypergraph to be decomposed
+    irredundant : bool
+        if True, irredundant tree decomposition will be computed.
+
+    Returns
+    -------
+    clique_tree : CliqueTree
+        each node contains a subhypergraph of `hg`
+    """
+    def _divide_hg(hg):
+        ''' divide a hypergraph into subhypergraphs such that
+        each subhypergraph is connected to each other in a tree-like way.
+
+        Parameters
+        ----------
+        hg : Hypergraph
+
+        Returns
+        -------
+        list of Hypergraphs
+            each element corresponds to a subhypergraph of `hg`
+        '''
+        is_ring = False
+        for each_node in hg.nodes:
+            if hg.node_attr(each_node)['is_in_ring']:
+                is_ring = True
+            if not hg.node_attr(each_node)['is_in_ring'] \
+               and hg.degree(each_node) == 2:
+                subhg1, subhg2 = hg.divide(each_node)
+                return _divide_hg(subhg1) + _divide_hg(subhg2)
+
+        if is_ring:
+            subhg_list = []
+            remove_edge_list = []
+            remove_node_list = []
+            for each_edge in hg.edges:
+                node_list = hg.nodes_in_edge(each_edge)
+                subhg = hg.get_subhg(node_list, [each_edge], hg.get_identical_node_dict())
+                subhg_list.append(subhg)
+                remove_edge_list.append(each_edge)
+                for each_node in node_list:
+                    if not hg.node_attr(each_node)['is_in_ring']:
+                        remove_node_list.append(each_node)
+            hg.remove_edges(remove_edge_list)
+            hg.remove_nodes(remove_node_list, False)
+            return subhg_list + [hg]
+        else:
+            return [hg]
+
+    org_hg = hg.copy()
+    clique_tree = CliqueTree(org_hg)
+    clique_tree.add_node(0, subhg=org_hg)
+
+    subhg_list = _divide_hg(deepcopy(clique_tree.root_hg))
+    #_subhg_list = deepcopy(subhg_list)
+    if len(subhg_list) == 1:
+        pass
+    else:
+        while len(subhg_list) > 1:
+            max_leaf_subhg = None
+            for each_subhg in subhg_list:
+                if _is_leaf(clique_tree.root_hg, each_subhg) and not _is_ring(each_subhg):
+                    if max_leaf_subhg is None:
+                        max_leaf_subhg = each_subhg
+                    elif max_leaf_subhg.num_nodes < each_subhg.num_nodes:
+                        max_leaf_subhg = each_subhg
+
+            if max_leaf_subhg is None:
+                for each_subhg in subhg_list:
+                    if _is_ring_label(clique_tree.root_hg, each_subhg):
+                        if max_leaf_subhg is None:
+                            max_leaf_subhg = each_subhg
+                        elif max_leaf_subhg.num_nodes < each_subhg.num_nodes:
+                            max_leaf_subhg = each_subhg
+            if max_leaf_subhg is not None:
+                clique_tree.update(max_leaf_subhg)
+                subhg_list.remove(max_leaf_subhg)
+            else:
+                for each_subhg in subhg_list:
+                    if _is_leaf(clique_tree.root_hg, each_subhg):
+                        if max_leaf_subhg is None:
+                            max_leaf_subhg = each_subhg
+                        elif max_leaf_subhg.num_nodes < each_subhg.num_nodes:
+                            max_leaf_subhg = each_subhg
+                if max_leaf_subhg is not None:
+                    clique_tree.update(max_leaf_subhg, True)
+                    subhg_list.remove(max_leaf_subhg)
+                else:
+                    break
+    if len(subhg_list) > 1:
+        '''
+        for each_idx, each_subhg in enumerate(subhg_list):
+            each_subhg.draw(f'{each_idx}', True)
+        clique_tree.root_hg.draw('root', True)
+        import pickle
+        with open('buggy_hg.pkl', 'wb') as f:
+            pickle.dump(hg, f)
+        return clique_tree, subhg_list, _subhg_list
+        '''
+        raise RuntimeError('bug in tree decomposition algorithm')
+    clique_tree.root_hg.remove_edges_with_attr({'tmp' : True})
+
+    '''
+    for each_tree_node in clique_tree.adj[0]:
+        subhg = clique_tree.nodes[each_tree_node]['subhg']
+        for each_edge in subhg.edges:
+            if set(subhg.nodes_in_edge(each_edge)).issubset(clique_tree.root_hg.nodes):
+                clique_tree.root_hg.add_edge(set(subhg.nodes_in_edge(each_edge)), attr_dict=dict(tmp=True))
+    '''
+    if irredundant:
+        clique_tree.to_irredundant()
+    return clique_tree #, _subhg_list
+
+def _is_leaf(hg, subhg) -> bool:
+    ''' judge whether subhg is a leaf-like in the original hypergraph
+
+    Parameters
+    ----------
+    hg : Hypergraph
+    subhg : Hypergraph
+        `subhg` is a subhypergraph of `hg`
+
+    Returns
+    -------
+    bool
+    '''
+    if len(subhg.edges) == 0:
+        adj_edge_set = set([])
+        subhg_edge_set = set([])
+        for each_edge in hg.edges:
+            if set(hg.nodes_in_edge(each_edge)).issubset(subhg.nodes) and hg.edge_attr(each_edge).get('tmp', False):
+                subhg_edge_set.add(each_edge)
+        for each_node in subhg.nodes:
+            adj_edge_set.update(set(hg.adj_edges(each_node)))
+        if subhg_edge_set.issubset(adj_edge_set) and len(adj_edge_set.difference(subhg_edge_set)) == 1:
+            return True
+        else:
+            return False
+    elif len(subhg.edges) == 1:
+        adj_edge_set = set([])
+        subhg_edge_set = subhg.edges
+        for each_node in subhg.nodes:
+            for each_adj_edge in hg.adj_edges(each_node):
+                adj_edge_set.add(each_adj_edge)
+        if subhg_edge_set.issubset(adj_edge_set) and len(adj_edge_set.difference(subhg_edge_set)) == 1:
+            return True
+        else:
+            return False
+    else:
+        raise ValueError('subhg should be nodes only or one-edge hypergraph.')
+
+def _is_ring_label(hg, subhg):
+    if len(subhg.edges) != 1:
+        return False
+    edge_name = list(subhg.edges)[0]
+    #assert edge_name in hg.edges, f'{edge_name}'
+    is_in_ring = False
+    for each_node in subhg.nodes:
+        if subhg.node_attr(each_node)['is_in_ring']:
+            is_in_ring = True
+        else:
+            adj_edge_list = list(hg.adj_edges(each_node))
+            adj_edge_list.remove(edge_name)
+            if len(adj_edge_list) == 1:
+                if not hg.edge_attr(adj_edge_list[0]).get('tmp', False):
+                    return False
+            elif len(adj_edge_list) == 0:
+                pass
+            else:
+                raise ValueError
+    if is_in_ring:
+        return True
+    else:
+        return False
+
+def _is_ring(hg):
+    for each_node in hg.nodes:
+        if not hg.node_attr(each_node)['is_in_ring']:
+            return False
+    return True        
+
diff --git a/models/mhg_model/graph_grammar/graph_grammar/__init__.py b/models/mhg_model/graph_grammar/graph_grammar/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e6131daba8a4f601ae72d37e6eb035d9503045
--- /dev/null
+++ b/models/mhg_model/graph_grammar/graph_grammar/__init__.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jan 1 2018"
+
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diff --git a/models/mhg_model/graph_grammar/graph_grammar/base.py b/models/mhg_model/graph_grammar/graph_grammar/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5977dff873dc004b0e1f1fbae1e65af5b52052c
--- /dev/null
+++ b/models/mhg_model/graph_grammar/graph_grammar/base.py
@@ -0,0 +1,30 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2017"
+__version__ = "0.1"
+__date__ = "Dec 11 2017"
+
+from abc import ABCMeta, abstractmethod
+
+class GraphGrammarBase(metaclass=ABCMeta):
+    @abstractmethod
+    def learn(self):
+        pass
+    
+    @abstractmethod
+    def sample(self):
+        pass
diff --git a/models/mhg_model/graph_grammar/graph_grammar/corpus.py b/models/mhg_model/graph_grammar/graph_grammar/corpus.py
new file mode 100644
index 0000000000000000000000000000000000000000..dad81a13d0b4873b2929e32a9031f3105811c808
--- /dev/null
+++ b/models/mhg_model/graph_grammar/graph_grammar/corpus.py
@@ -0,0 +1,152 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jun 4 2018"
+
+from collections import Counter
+from functools import partial
+from .utils import _easy_node_match, _edge_match, _node_match, common_node_list, _node_match_prod_rule
+from networkx.algorithms.isomorphism import GraphMatcher
+import os
+
+
+class CliqueTreeCorpus(object):
+
+    ''' clique tree corpus
+
+    Attributes
+    ----------
+    clique_tree_list : list of CliqueTree
+    subhg_list : list of Hypergraph
+    '''
+
+    def __init__(self):
+        self.clique_tree_list = []
+        self.subhg_list = []
+
+    @property
+    def size(self):
+        return len(self.subhg_list)
+
+    def add_clique_tree(self, clique_tree):
+        for each_node in clique_tree.nodes:
+            subhg = clique_tree.nodes[each_node]['subhg']
+            subhg_idx = self.add_subhg(subhg)
+            clique_tree.nodes[each_node]['subhg_idx'] = subhg_idx
+        self.clique_tree_list.append(clique_tree)
+
+    def add_to_subhg_list(self, clique_tree, root_node):
+        parent_node_dict = {}
+        current_node = None
+        parent_node_dict[root_node] = None
+        stack = [root_node]
+        while stack:
+            current_node = stack.pop()
+            current_subhg = clique_tree.nodes[current_node]['subhg']
+            for each_child in clique_tree.adj[current_node]:
+                if each_child != parent_node_dict[current_node]:
+                    stack.append(each_child)
+                    parent_node_dict[each_child] = current_node
+            if parent_node_dict[current_node] is not None:
+                parent_subhg = clique_tree.nodes[parent_node_dict[current_node]]['subhg']
+                common, _ = common_node_list(parent_subhg, current_subhg)
+                parent_subhg.add_edge(set(common), attr_dict={'tmp': True})
+
+        parent_node_dict = {}
+        current_node = None
+        parent_node_dict[root_node] = None
+        stack = [root_node]
+        while stack:
+            current_node = stack.pop()
+            current_subhg = clique_tree.nodes[current_node]['subhg']
+            for each_child in clique_tree.adj[current_node]:
+                if each_child != parent_node_dict[current_node]:
+                    stack.append(each_child)
+                    parent_node_dict[each_child] = current_node
+            if parent_node_dict[current_node] is not None:
+                parent_subhg = clique_tree.nodes[parent_node_dict[current_node]]['subhg']
+                common, _ = common_node_list(parent_subhg, current_subhg)
+                for each_idx, each_node in enumerate(common):
+                    current_subhg.set_node_attr(each_node, {'ext_id': each_idx})
+
+            subhg_idx, is_new = self.add_subhg(current_subhg)
+            clique_tree.nodes[current_node]['subhg_idx'] = subhg_idx
+        return clique_tree
+
+    def add_subhg(self, subhg):
+        if len(self.subhg_list) == 0:
+            node_dict = {}
+            for each_node in subhg.nodes:
+                node_dict[each_node] = subhg.node_attr(each_node)['symbol'].__hash__()
+            node_list = []
+            for each_key, _ in sorted(node_dict.items(), key=lambda x:x[1]):
+                node_list.append(each_key)
+            for each_idx, each_node in enumerate(node_list):
+                subhg.node_attr(each_node)['order4hrg'] = each_idx
+            self.subhg_list.append(subhg)
+            return 0, True
+        else:
+            match = False
+            subhg_bond_symbol_counter \
+                = Counter([subhg.node_attr(each_node)['symbol'] \
+                           for each_node in subhg.nodes])
+            subhg_atom_symbol_counter \
+                = Counter([subhg.edge_attr(each_edge).get('symbol', None) \
+                           for each_edge in subhg.edges])
+            for each_idx, each_subhg in enumerate(self.subhg_list):
+                each_bond_symbol_counter \
+                    = Counter([each_subhg.node_attr(each_node)['symbol'] \
+                               for each_node in each_subhg.nodes])
+                each_atom_symbol_counter \
+                    = Counter([each_subhg.edge_attr(each_edge).get('symbol', None) \
+                               for each_edge in each_subhg.edges])
+                if not match \
+                   and (subhg.num_nodes == each_subhg.num_nodes
+                        and subhg.num_edges == each_subhg.num_edges
+                        and subhg_bond_symbol_counter == each_bond_symbol_counter
+                        and subhg_atom_symbol_counter == each_atom_symbol_counter):
+                    gm = GraphMatcher(each_subhg.hg,
+                                      subhg.hg,
+                                      node_match=_easy_node_match,
+                                      edge_match=_edge_match)
+                    try:
+                        isomap = next(gm.isomorphisms_iter())
+                        match = True
+                        for each_node in each_subhg.nodes:
+                            subhg.node_attr(isomap[each_node])['order4hrg'] \
+                                = each_subhg.node_attr(each_node)['order4hrg']
+                            if 'ext_id' in each_subhg.node_attr(each_node):
+                                subhg.node_attr(isomap[each_node])['ext_id'] \
+                                    = each_subhg.node_attr(each_node)['ext_id']
+                        return each_idx, False
+                    except StopIteration:
+                        match = False    
+            if not match:
+                node_dict = {}
+                for each_node in subhg.nodes:
+                    node_dict[each_node] = subhg.node_attr(each_node)['symbol'].__hash__()
+                node_list = []
+                for each_key, _ in sorted(node_dict.items(), key=lambda x:x[1]):
+                    node_list.append(each_key)
+                for each_idx, each_node in enumerate(node_list):
+                    subhg.node_attr(each_node)['order4hrg'] = each_idx
+
+                #for each_idx, each_node in enumerate(subhg.nodes):
+                #    subhg.node_attr(each_node)['order4hrg'] = each_idx
+                self.subhg_list.append(subhg)
+                return len(self.subhg_list) - 1, True
diff --git a/models/mhg_model/graph_grammar/graph_grammar/hrg.py b/models/mhg_model/graph_grammar/graph_grammar/hrg.py
new file mode 100644
index 0000000000000000000000000000000000000000..49adf224b06b6b0fbac9040865f46b0c4f20a85a
--- /dev/null
+++ b/models/mhg_model/graph_grammar/graph_grammar/hrg.py
@@ -0,0 +1,1065 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2017"
+__version__ = "0.1"
+__date__ = "Dec 11 2017"
+
+from .corpus import CliqueTreeCorpus
+from .base import GraphGrammarBase
+from .symbols import TSymbol, NTSymbol, BondSymbol
+from .utils import _node_match, _node_match_prod_rule, _edge_match, masked_softmax, common_node_list
+from ..hypergraph import Hypergraph
+from collections import Counter
+from copy import deepcopy
+from ..algo.tree_decomposition import (
+    tree_decomposition,
+    tree_decomposition_with_hrg,
+    tree_decomposition_from_leaf,
+    topological_tree_decomposition,
+    molecular_tree_decomposition)
+from functools import partial
+from networkx.algorithms.isomorphism import GraphMatcher
+from typing import List, Dict, Tuple
+import networkx as nx
+import numpy as np
+import torch
+import os
+import random
+
+DEBUG = False
+
+
+class ProductionRule(object):
+    """ A class of a production rule
+
+    Attributes
+    ----------
+    lhs : Hypergraph or None
+        the left hand side of the production rule.
+        if None, the rule is a starting rule.
+    rhs : Hypergraph
+        the right hand side of the production rule.
+    """
+    def __init__(self, lhs, rhs):
+        self.lhs = lhs
+        self.rhs = rhs
+
+    @property
+    def is_start_rule(self) -> bool:
+        return self.lhs.num_nodes == 0
+
+    @property
+    def ext_node(self) -> Dict[int, str]:
+        """ return a dict of external nodes
+        """
+        if self.is_start_rule:
+            return {}
+        else:
+            ext_node_dict = {}
+            for each_node in self.lhs.nodes:
+                ext_node_dict[self.lhs.node_attr(each_node)["ext_id"]] = each_node
+            return ext_node_dict
+
+    @property
+    def lhs_nt_symbol(self) -> NTSymbol:
+        if self.is_start_rule:
+            return NTSymbol(degree=0, is_aromatic=False, bond_symbol_list=[])
+        else:
+            return self.lhs.edge_attr(list(self.lhs.edges)[0])['symbol']
+
+    def rhs_adj_mat(self, node_edge_list):
+        ''' return the adjacency matrix of rhs of the production rule
+        '''
+        return nx.adjacency_matrix(self.rhs.hg, node_edge_list)
+        
+    def draw(self, file_path=None):
+        return self.rhs.draw(file_path)
+
+    def is_same(self, prod_rule, ignore_order=False):
+        """ judge whether this production rule is
+        the same as the input one, `prod_rule`
+
+        Parameters
+        ----------
+        prod_rule : ProductionRule
+            production rule to be compared
+
+        Returns
+        -------
+        is_same : bool
+        isomap : dict
+            isomorphism of nodes and hyperedges.
+            ex) {'bond_42': 'bond_37', 'bond_2': 'bond_1',
+                 'e36': 'e11', 'e16': 'e12', 'e25': 'e18',
+                 'bond_40': 'bond_38', 'e26': 'e21', 'bond_41': 'bond_39'}.
+            key comes from `prod_rule`, value comes from `self`.
+        """
+        if self.is_start_rule:
+            if not prod_rule.is_start_rule:
+                return False, {}
+        else:
+            if prod_rule.is_start_rule:
+                return False, {}
+            else:
+                if prod_rule.lhs.num_nodes != self.lhs.num_nodes:
+                    return False, {}
+
+        if prod_rule.rhs.num_nodes != self.rhs.num_nodes:
+            return False, {}
+        if prod_rule.rhs.num_edges != self.rhs.num_edges:
+            return False, {}
+
+        subhg_bond_symbol_counter \
+            = Counter([prod_rule.rhs.node_attr(each_node)['symbol'] \
+                       for each_node in prod_rule.rhs.nodes])
+        each_bond_symbol_counter \
+            = Counter([self.rhs.node_attr(each_node)['symbol'] \
+                       for each_node in self.rhs.nodes])
+        if subhg_bond_symbol_counter != each_bond_symbol_counter:
+            return False, {}
+
+        subhg_atom_symbol_counter \
+            = Counter([prod_rule.rhs.edge_attr(each_edge)['symbol'] \
+                       for each_edge in prod_rule.rhs.edges])
+        each_atom_symbol_counter \
+            = Counter([self.rhs.edge_attr(each_edge)['symbol'] \
+                       for each_edge in self.rhs.edges])
+        if subhg_atom_symbol_counter != each_atom_symbol_counter:
+            return False, {}
+
+        gm = GraphMatcher(prod_rule.rhs.hg,
+                          self.rhs.hg,
+                          partial(_node_match_prod_rule,
+                                  ignore_order=ignore_order),
+                          partial(_edge_match,
+                                  ignore_order=ignore_order))
+        try:
+            return True, next(gm.isomorphisms_iter())
+        except StopIteration:
+            return False, {}
+
+    def applied_to(self,
+                   hg: Hypergraph,
+                   edge: str) -> Tuple[Hypergraph, List[str]]:
+        """ augment `hg` by replacing `edge` with `self.rhs`.
+
+        Parameters
+        ----------
+        hg : Hypergraph
+        edge : str
+            `edge` must belong to `hg`
+
+        Returns
+        -------
+        hg : Hypergraph
+            resultant hypergraph
+        nt_edge_list : list
+            list of non-terminal edges
+        """
+        nt_edge_dict = {}
+        if self.is_start_rule:
+            if (edge is not None) or (hg is not None):
+                ValueError("edge and hg must be None for this prod rule.")
+            hg = Hypergraph()
+            node_map_rhs = {} # node id in rhs -> node id in hg, where rhs is augmented.
+            for num_idx, each_node in enumerate(self.rhs.nodes):
+                hg.add_node(f"bond_{num_idx}",
+                            #attr_dict=deepcopy(self.rhs.node_attr(each_node)))
+                            attr_dict=self.rhs.node_attr(each_node))
+                node_map_rhs[each_node] = f"bond_{num_idx}"
+            for each_edge in self.rhs.edges:
+                node_list = []
+                for each_node in self.rhs.nodes_in_edge(each_edge):
+                    node_list.append(node_map_rhs[each_node])
+                if isinstance(self.rhs.nodes_in_edge(each_edge), set):
+                    node_list = set(node_list)
+                edge_id = hg.add_edge(
+                    node_list,
+                    #attr_dict=deepcopy(self.rhs.edge_attr(each_edge)))
+                    attr_dict=self.rhs.edge_attr(each_edge))
+                if "nt_idx" in hg.edge_attr(edge_id):
+                    nt_edge_dict[hg.edge_attr(edge_id)["nt_idx"]] = edge_id
+            nt_edge_list = [nt_edge_dict[key] for key in range(len(nt_edge_dict))]
+            return hg, nt_edge_list
+        else:
+            if edge not in hg.edges:
+                raise ValueError("the input hyperedge does not exist.")
+            if hg.edge_attr(edge)["terminal"]:
+                raise ValueError("the input hyperedge is terminal.")
+            if hg.edge_attr(edge)['symbol'] != self.lhs_nt_symbol:
+                print(hg.edge_attr(edge)['symbol'], self.lhs_nt_symbol)
+                raise ValueError("the input hyperedge and lhs have inconsistent number of nodes.")
+            if DEBUG:
+                for node_idx, each_node in enumerate(hg.nodes_in_edge(edge)):
+                    other_node = self.lhs.nodes_in_edge(list(self.lhs.edges)[0])[node_idx]
+                    attr = deepcopy(self.lhs.node_attr(other_node))
+                    attr.pop('ext_id')
+                    if hg.node_attr(each_node) != attr:
+                        raise ValueError('node attributes are inconsistent.')
+
+            # order of nodes that belong to the non-terminal edge in hg
+            nt_order_dict = {}  # hg_node -> order ("bond_17" : 1)
+            nt_order_dict_inv = {} # order -> hg_node
+            for each_idx, each_node in enumerate(hg.nodes_in_edge(edge)):
+                nt_order_dict[each_node] = each_idx
+                nt_order_dict_inv[each_idx] = each_node
+
+            # construct a node_map_rhs: rhs -> new hg
+            node_map_rhs = {} # node id in rhs -> node id in hg, where rhs is augmented.
+            node_idx = hg.num_nodes
+            for each_node in self.rhs.nodes:
+                if "ext_id" in self.rhs.node_attr(each_node):
+                    node_map_rhs[each_node] \
+                        = nt_order_dict_inv[
+                            self.rhs.node_attr(each_node)["ext_id"]]
+                else:
+                    node_map_rhs[each_node] = f"bond_{node_idx}"
+                    node_idx += 1
+
+            # delete non-terminal
+            hg.remove_edge(edge)
+
+            # add nodes to hg
+            for each_node in self.rhs.nodes:
+                hg.add_node(node_map_rhs[each_node],
+                            attr_dict=self.rhs.node_attr(each_node))
+
+            # add hyperedges to hg
+            for each_edge in self.rhs.edges:
+                node_list_hg = []
+                for each_node in self.rhs.nodes_in_edge(each_edge):
+                    node_list_hg.append(node_map_rhs[each_node])
+                edge_id = hg.add_edge(
+                    node_list_hg,
+                    attr_dict=self.rhs.edge_attr(each_edge))#deepcopy(self.rhs.edge_attr(each_edge)))
+                if "nt_idx" in hg.edge_attr(edge_id):
+                    nt_edge_dict[hg.edge_attr(edge_id)["nt_idx"]] = edge_id
+            nt_edge_list = [nt_edge_dict[key] for key in range(len(nt_edge_dict))]
+            return hg, nt_edge_list
+
+    def revert(self, hg: Hypergraph, return_subhg=False):
+        ''' revert applying this production rule.
+        i.e., if there exists a subhypergraph that matches the r.h.s. of this production rule,
+        this method replaces the subhypergraph with a non-terminal hyperedge.
+
+        Parameters
+        ----------
+        hg : Hypergraph
+            hypergraph to be reverted
+        return_subhg : bool
+            if True, the removed subhypergraph will be returned.
+
+        Returns
+        -------
+        hg : Hypergraph
+            the resultant hypergraph. if it cannot be reverted, the original one is returned without any replacement.
+        success : bool
+            this indicates whether reverting is successed or not.
+        '''
+        gm = GraphMatcher(hg.hg, self.rhs.hg, node_match=_node_match_prod_rule,
+                          edge_match=_edge_match)
+        try:
+            # in case when the matched subhg is connected to the other part via external nodes and more.
+            not_iso = True
+            while not_iso:
+                isomap = next(gm.subgraph_isomorphisms_iter())
+                adj_node_set = set([]) # reachable nodes from the internal nodes
+                subhg_node_set = set(isomap.keys()) # nodes in subhg
+                for each_node in subhg_node_set:
+                    adj_node_set.add(each_node)
+                    if isomap[each_node] not in self.ext_node.values():
+                        adj_node_set.update(hg.hg.adj[each_node])
+                if adj_node_set == subhg_node_set:
+                    not_iso = False
+                else:
+                    if return_subhg:
+                        return hg, False, Hypergraph()
+                    else:
+                        return hg, False
+            inv_isomap = {v: k for k, v in isomap.items()}
+            '''
+            isomap = {'e35': 'e8', 'bond_13': 'bond_18', 'bond_14': 'bond_19',
+                      'bond_15': 'bond_17', 'e29': 'e23', 'bond_12': 'bond_20'}
+            where keys come from `hg` and values come from `self.rhs`
+            '''
+        except StopIteration:
+            if return_subhg:
+                return hg, False, Hypergraph()
+            else:
+                return hg, False
+
+        if return_subhg:
+            subhg = Hypergraph()
+            for each_node in hg.nodes:
+                if each_node in isomap:
+                    subhg.add_node(each_node, attr_dict=hg.node_attr(each_node))
+            for each_edge in hg.edges:
+                if each_edge in isomap:
+                    subhg.add_edge(hg.nodes_in_edge(each_edge),
+                                   attr_dict=hg.edge_attr(each_edge),
+                                   edge_name=each_edge)
+            subhg.edge_idx = hg.edge_idx
+
+        # remove subhg except for the externael nodes
+        for each_key, each_val in isomap.items():
+            if each_key.startswith('e'):
+                hg.remove_edge(each_key)
+        for each_key, each_val in isomap.items():
+            if each_key.startswith('bond_'):
+                if each_val not in self.ext_node.values():
+                    hg.remove_node(each_key)
+
+        # add non-terminal hyperedge
+        nt_node_list = []
+        for each_ext_id in self.ext_node.keys():
+            nt_node_list.append(inv_isomap[self.ext_node[each_ext_id]])
+
+        hg.add_edge(nt_node_list,
+                    attr_dict=dict(
+                        terminal=False,
+                        symbol=self.lhs_nt_symbol))
+        if return_subhg:
+            return hg, True, subhg
+        else:
+            return hg, True
+
+
+class ProductionRuleCorpus(object):
+
+    '''
+    A corpus of production rules.
+    This class maintains 
+        (i) list of unique production rules,
+        (ii) list of unique edge symbols (both terminal and non-terminal), and
+        (iii) list of unique node symbols.
+
+    Attributes
+    ----------
+    prod_rule_list : list
+        list of unique production rules
+    edge_symbol_list : list
+        list of unique symbols (including both terminal and non-terminal)
+    node_symbol_list : list
+        list of node symbols
+    nt_symbol_list : list
+        list of unique lhs symbols
+    ext_id_list : list
+        list of ext_ids
+    lhs_in_prod_rule : array
+        a matrix of lhs vs prod_rule (= lhs_in_prod_rule)
+    '''
+
+    def __init__(self):
+        self.prod_rule_list = []
+        self.edge_symbol_list = []
+        self.edge_symbol_dict = {}
+        self.node_symbol_list = []
+        self.node_symbol_dict = {}
+        self.nt_symbol_list = []
+        self.ext_id_list = []
+        self._lhs_in_prod_rule = None
+        self.lhs_in_prod_rule_row_list = []
+        self.lhs_in_prod_rule_col_list = []
+
+    @property
+    def lhs_in_prod_rule(self):
+        if self._lhs_in_prod_rule is None:
+            self._lhs_in_prod_rule = torch.sparse.FloatTensor(
+                torch.LongTensor(list(zip(self.lhs_in_prod_rule_row_list, self.lhs_in_prod_rule_col_list))).t(),
+                torch.FloatTensor([1.0]*len(self.lhs_in_prod_rule_col_list)),
+                torch.Size([len(self.nt_symbol_list), len(self.prod_rule_list)])
+            ).to_dense()
+        return self._lhs_in_prod_rule
+        
+    @property
+    def num_prod_rule(self):
+        ''' return the number of production rules
+
+        Returns
+        -------
+        int : the number of unique production rules
+        '''
+        return len(self.prod_rule_list)
+
+    @property
+    def start_rule_list(self):
+        ''' return a list of start rules
+
+        Returns
+        -------
+        list : list of start rules
+        '''
+        start_rule_list = []
+        for each_prod_rule in self.prod_rule_list:
+            if each_prod_rule.is_start_rule:
+                start_rule_list.append(each_prod_rule)
+        return start_rule_list
+
+    @property
+    def num_edge_symbol(self):
+        return len(self.edge_symbol_list)
+
+    @property
+    def num_node_symbol(self):
+        return len(self.node_symbol_list)
+
+    @property
+    def num_ext_id(self):
+        return len(self.ext_id_list)
+
+    def construct_feature_vectors(self):
+        ''' this method constructs feature vectors for the production rules collected so far.
+        currently, NTSymbol and TSymbol are treated in the same manner.
+        '''
+        feature_id_dict = {}
+        feature_id_dict['TSymbol'] = 0
+        feature_id_dict['NTSymbol'] = 1
+        feature_id_dict['BondSymbol'] = 2
+        for each_edge_symbol in self.edge_symbol_list:
+            for each_attr in each_edge_symbol.__dict__.keys():
+                each_val = each_edge_symbol.__dict__[each_attr]
+                if isinstance(each_val, list):
+                    each_val = tuple(each_val)
+                if (each_attr, each_val) not in feature_id_dict:
+                    feature_id_dict[(each_attr, each_val)] = len(feature_id_dict)
+
+        for each_node_symbol in self.node_symbol_list:
+            for each_attr in each_node_symbol.__dict__.keys():
+                each_val = each_node_symbol.__dict__[each_attr]
+                if isinstance(each_val, list):
+                    each_val = tuple(each_val)
+                if (each_attr, each_val) not in feature_id_dict:
+                    feature_id_dict[(each_attr, each_val)] = len(feature_id_dict)
+        for each_ext_id in self.ext_id_list:
+            feature_id_dict[('ext_id', each_ext_id)] = len(feature_id_dict)
+        dim = len(feature_id_dict)
+
+        feature_dict = {}
+        for each_edge_symbol in self.edge_symbol_list:
+            idx_list = []
+            idx_list.append(feature_id_dict[each_edge_symbol.__class__.__name__])
+            for each_attr in each_edge_symbol.__dict__.keys():
+                each_val = each_edge_symbol.__dict__[each_attr]
+                if isinstance(each_val, list):
+                    each_val = tuple(each_val)
+                idx_list.append(feature_id_dict[(each_attr, each_val)])
+            feature = torch.sparse.LongTensor(
+                torch.LongTensor([idx_list]),
+                torch.ones(len(idx_list)),
+                torch.Size([len(feature_id_dict)])
+            )
+            feature_dict[each_edge_symbol] = feature
+
+        for each_node_symbol in self.node_symbol_list:
+            idx_list = []
+            idx_list.append(feature_id_dict[each_node_symbol.__class__.__name__])
+            for each_attr in each_node_symbol.__dict__.keys():
+                each_val = each_node_symbol.__dict__[each_attr]
+                if isinstance(each_val, list):
+                    each_val = tuple(each_val)
+                idx_list.append(feature_id_dict[(each_attr, each_val)])
+            feature = torch.sparse.LongTensor(
+                torch.LongTensor([idx_list]),
+                torch.ones(len(idx_list)),
+                torch.Size([len(feature_id_dict)])
+            )
+            feature_dict[each_node_symbol] = feature
+        for each_ext_id in self.ext_id_list:
+            idx_list = [feature_id_dict[('ext_id', each_ext_id)]]
+            feature_dict[('ext_id', each_ext_id)] \
+                = torch.sparse.LongTensor(
+                    torch.LongTensor([idx_list]),
+                    torch.ones(len(idx_list)),
+                    torch.Size([len(feature_id_dict)])
+                )
+        return feature_dict, dim
+
+    def edge_symbol_idx(self, symbol):
+        return self.edge_symbol_dict[symbol]
+
+    def node_symbol_idx(self, symbol):
+        return self.node_symbol_dict[symbol]
+
+    def append(self, prod_rule: ProductionRule) -> Tuple[int, ProductionRule]:
+        """ return whether the input production rule is new or not, and its production rule id.
+        Production rules are regarded as the same if 
+            i) there exists a one-to-one mapping of nodes and edges, and
+            ii) all the attributes associated with nodes and hyperedges are the same.
+
+        Parameters
+        ----------
+        prod_rule : ProductionRule
+
+        Returns
+        -------
+        prod_rule_id : int
+            production rule index. if new, a new index will be assigned.
+        prod_rule : ProductionRule
+        """
+        num_lhs = len(self.nt_symbol_list)
+        for each_idx, each_prod_rule in enumerate(self.prod_rule_list):
+            is_same, isomap = prod_rule.is_same(each_prod_rule)
+            if is_same:
+                # we do not care about edge and node names, but care about the order of non-terminal edges.
+                for key, val in isomap.items(): # key : edges & nodes in each_prod_rule.rhs , val : those in prod_rule.rhs
+                    if key.startswith("bond_"):
+                        continue
+
+                    # rewrite `nt_idx` in `prod_rule` for further processing
+                    if "nt_idx" in prod_rule.rhs.edge_attr(val).keys():
+                        if "nt_idx" not in each_prod_rule.rhs.edge_attr(key).keys():
+                            raise ValueError
+                        prod_rule.rhs.set_edge_attr(
+                            val,
+                            {'nt_idx': each_prod_rule.rhs.edge_attr(key)["nt_idx"]})
+                return each_idx, prod_rule
+        self.prod_rule_list.append(prod_rule)
+        self._update_edge_symbol_list(prod_rule)
+        self._update_node_symbol_list(prod_rule)
+        self._update_ext_id_list(prod_rule)
+
+        lhs_idx = self.nt_symbol_list.index(prod_rule.lhs_nt_symbol)
+        self.lhs_in_prod_rule_row_list.append(lhs_idx)
+        self.lhs_in_prod_rule_col_list.append(len(self.prod_rule_list)-1)
+        self._lhs_in_prod_rule = None
+        return len(self.prod_rule_list)-1, prod_rule
+
+    def get_prod_rule(self, prod_rule_idx: int) -> ProductionRule:
+        return self.prod_rule_list[prod_rule_idx]
+
+    def sample(self, unmasked_logit_array, nt_symbol, deterministic=False):
+        ''' sample a production rule whose lhs is `nt_symbol`, followihng `unmasked_logit_array`.
+
+        Parameters
+        ----------
+        unmasked_logit_array : array-like, length `num_prod_rule`
+        nt_symbol : NTSymbol
+        '''
+        if not isinstance(unmasked_logit_array, np.ndarray):
+            unmasked_logit_array = unmasked_logit_array.numpy().astype(np.float64)
+        if deterministic:
+            prob = masked_softmax(unmasked_logit_array,
+                                  self.lhs_in_prod_rule[self.nt_symbol_list.index(nt_symbol)].numpy().astype(np.float64))
+            return self.prod_rule_list[np.argmax(prob)]
+        else:
+            return np.random.choice(
+                self.prod_rule_list, 1,
+                p=masked_softmax(unmasked_logit_array,
+                                 self.lhs_in_prod_rule[self.nt_symbol_list.index(nt_symbol)].numpy().astype(np.float64)))[0]
+
+    def masked_logprob(self, unmasked_logit_array, nt_symbol):
+        if not isinstance(unmasked_logit_array, np.ndarray):
+            unmasked_logit_array = unmasked_logit_array.numpy().astype(np.float64)
+        prob = masked_softmax(unmasked_logit_array,
+                              self.lhs_in_prod_rule[self.nt_symbol_list.index(nt_symbol)].numpy().astype(np.float64))
+        return np.log(prob)
+
+    def _update_edge_symbol_list(self, prod_rule: ProductionRule):
+        ''' update edge symbol list
+
+        Parameters
+        ----------
+        prod_rule : ProductionRule
+        '''
+        if prod_rule.lhs_nt_symbol not in self.nt_symbol_list:
+            self.nt_symbol_list.append(prod_rule.lhs_nt_symbol)
+
+        for each_edge in prod_rule.rhs.edges:
+            if prod_rule.rhs.edge_attr(each_edge)['symbol'] not in self.edge_symbol_dict:
+                edge_symbol_idx = len(self.edge_symbol_list)
+                self.edge_symbol_list.append(prod_rule.rhs.edge_attr(each_edge)['symbol'])
+                self.edge_symbol_dict[prod_rule.rhs.edge_attr(each_edge)['symbol']] = edge_symbol_idx
+            else:
+                edge_symbol_idx = self.edge_symbol_dict[prod_rule.rhs.edge_attr(each_edge)['symbol']]
+            prod_rule.rhs.edge_attr(each_edge)['symbol_idx'] = edge_symbol_idx
+        pass
+
+    def _update_node_symbol_list(self, prod_rule: ProductionRule):
+        ''' update node symbol list
+
+        Parameters
+        ----------
+        prod_rule : ProductionRule
+        '''
+        for each_node in prod_rule.rhs.nodes:
+            if prod_rule.rhs.node_attr(each_node)['symbol'] not in self.node_symbol_dict:
+                node_symbol_idx = len(self.node_symbol_list)
+                self.node_symbol_list.append(prod_rule.rhs.node_attr(each_node)['symbol'])
+                self.node_symbol_dict[prod_rule.rhs.node_attr(each_node)['symbol']] = node_symbol_idx
+            else:
+                node_symbol_idx = self.node_symbol_dict[prod_rule.rhs.node_attr(each_node)['symbol']]
+            prod_rule.rhs.node_attr(each_node)['symbol_idx'] = node_symbol_idx
+
+    def _update_ext_id_list(self, prod_rule: ProductionRule):
+        for each_node in prod_rule.rhs.nodes:
+            if 'ext_id' in prod_rule.rhs.node_attr(each_node):
+                if prod_rule.rhs.node_attr(each_node)['ext_id'] not in self.ext_id_list:
+                    self.ext_id_list.append(prod_rule.rhs.node_attr(each_node)['ext_id'])
+
+
+class HyperedgeReplacementGrammar(GraphGrammarBase):
+    """
+    Learn a hyperedge replacement grammar from a set of hypergraphs.
+
+    Attributes
+    ----------
+    prod_rule_list : list of ProductionRule
+        production rules learned from the input hypergraphs
+    """
+    def __init__(self,
+                 tree_decomposition=molecular_tree_decomposition,
+                 ignore_order=False, **kwargs):
+        from functools import partial
+        self.prod_rule_corpus = ProductionRuleCorpus()
+        self.clique_tree_corpus = CliqueTreeCorpus()
+        self.ignore_order = ignore_order
+        self.tree_decomposition = partial(tree_decomposition, **kwargs)
+
+    @property
+    def num_prod_rule(self):
+        ''' return the number of production rules
+
+        Returns
+        -------
+        int : the number of unique production rules
+        '''
+        return self.prod_rule_corpus.num_prod_rule
+
+    @property
+    def start_rule_list(self):
+        ''' return a list of start rules
+
+        Returns
+        -------
+        list : list of start rules
+        '''
+        return self.prod_rule_corpus.start_rule_list
+
+    @property
+    def prod_rule_list(self):
+        return self.prod_rule_corpus.prod_rule_list
+
+    def learn(self, hg_list, logger=print, max_mol=np.inf, print_freq=500):
+        """ learn from a list of hypergraphs
+
+        Parameters
+        ----------
+        hg_list : list of Hypergraph
+
+        Returns
+        -------
+        prod_rule_seq_list : list of integers
+            each element corresponds to a sequence of production rules to generate each hypergraph.
+        """
+        prod_rule_seq_list = []
+        idx = 0
+        for each_idx, each_hg in enumerate(hg_list):
+            clique_tree = self.tree_decomposition(each_hg)
+
+            # get a pair of myself and children
+            root_node = _find_root(clique_tree)
+            clique_tree = self.clique_tree_corpus.add_to_subhg_list(clique_tree, root_node)
+            prod_rule_seq = []
+            stack = []
+
+            children = sorted(list(clique_tree[root_node].keys()))
+
+            # extract a temporary production rule
+            prod_rule = extract_prod_rule(
+                None,
+                clique_tree.nodes[root_node]["subhg"],
+                [clique_tree.nodes[each_child]["subhg"]
+                 for each_child in children],
+                clique_tree.nodes[root_node].get('subhg_idx', None))
+
+            # update the production rule list
+            prod_rule_id, prod_rule = self.update_prod_rule_list(prod_rule)
+            children = reorder_children(root_node,
+                                        children,
+                                        prod_rule,
+                                        clique_tree)
+            stack.extend([(root_node, each_child) for each_child in children[::-1]])
+            prod_rule_seq.append(prod_rule_id)
+
+            while len(stack) != 0:
+                # get a triple of parent, myself, and children
+                parent, myself = stack.pop()
+                children = sorted(list(dict(clique_tree[myself]).keys()))
+                children.remove(parent)
+
+                # extract a temp prod rule
+                prod_rule = extract_prod_rule(
+                    clique_tree.nodes[parent]["subhg"],
+                    clique_tree.nodes[myself]["subhg"],
+                    [clique_tree.nodes[each_child]["subhg"]
+                     for each_child in children],
+                    clique_tree.nodes[myself].get('subhg_idx', None))
+
+                # update the prod rule list
+                prod_rule_id, prod_rule = self.update_prod_rule_list(prod_rule)
+                children = reorder_children(myself,
+                                            children,
+                                            prod_rule,
+                                            clique_tree)
+                stack.extend([(myself, each_child)
+                              for each_child in children[::-1]])
+                prod_rule_seq.append(prod_rule_id)
+            prod_rule_seq_list.append(prod_rule_seq)
+            if (each_idx+1) % print_freq == 0:
+                msg = f'#(molecules processed)={each_idx+1}\t'\
+                        f'#(production rules)={self.prod_rule_corpus.num_prod_rule}\t#(subhg in corpus)={self.clique_tree_corpus.size}'
+                logger(msg)
+            if each_idx > max_mol:
+                break
+
+        print(f'corpus_size = {self.clique_tree_corpus.size}')
+        return prod_rule_seq_list
+
+    def sample(self, z, deterministic=False):
+        """ sample a new hypergraph from HRG.
+
+        Parameters
+        ----------
+        z : array-like, shape (len, num_prod_rule)
+            logit
+        deterministic : bool
+            if True, deterministic sampling
+
+        Returns
+        -------
+        Hypergraph
+        """
+        seq_idx = 0
+        stack = []
+        z = z[:, :-1]
+        init_prod_rule = self.prod_rule_corpus.sample(z[0], NTSymbol(degree=0,
+                                                                     is_aromatic=False,
+                                                                     bond_symbol_list=[]),
+                                                      deterministic=deterministic)
+        hg, nt_edge_list = init_prod_rule.applied_to(None, None)
+        stack = deepcopy(nt_edge_list[::-1])
+        while len(stack) != 0 and seq_idx < z.shape[0]-1:
+            seq_idx += 1
+            nt_edge = stack.pop()
+            nt_symbol = hg.edge_attr(nt_edge)['symbol']
+            prod_rule = self.prod_rule_corpus.sample(z[seq_idx], nt_symbol, deterministic=deterministic)
+            hg, nt_edge_list = prod_rule.applied_to(hg, nt_edge)
+            stack.extend(nt_edge_list[::-1])
+        if len(stack) != 0:
+            raise RuntimeError(f'{len(stack)} non-terminals are left.')
+        return hg
+
+    def construct(self, prod_rule_seq):
+        """ construct a hypergraph following `prod_rule_seq`
+
+        Parameters
+        ----------
+        prod_rule_seq : list of integers
+            a sequence of production rules.
+
+        Returns
+        -------
+        UndirectedHypergraph
+        """
+        seq_idx = 0
+        init_prod_rule = self.prod_rule_corpus.get_prod_rule(prod_rule_seq[seq_idx])
+        hg, nt_edge_list = init_prod_rule.applied_to(None, None)
+        stack = deepcopy(nt_edge_list[::-1])
+        while len(stack) != 0:
+            seq_idx += 1
+            nt_edge = stack.pop()
+            hg, nt_edge_list = self.prod_rule_corpus.get_prod_rule(prod_rule_seq[seq_idx]).applied_to(hg, nt_edge)
+            stack.extend(nt_edge_list[::-1])            
+        return hg
+
+    def update_prod_rule_list(self, prod_rule):
+        """ return whether the input production rule is new or not, and its production rule id.
+        Production rules are regarded as the same if 
+            i) there exists a one-to-one mapping of nodes and edges, and
+            ii) all the attributes associated with nodes and hyperedges are the same.
+
+        Parameters
+        ----------
+        prod_rule : ProductionRule
+
+        Returns
+        -------
+        is_new : bool
+            if True, this production rule is new
+        prod_rule_id : int
+            production rule index. if new, a new index will be assigned.
+        """
+        return self.prod_rule_corpus.append(prod_rule)
+
+
+class IncrementalHyperedgeReplacementGrammar(HyperedgeReplacementGrammar):
+    '''
+    This class learns HRG incrementally leveraging the previously obtained production rules.
+    '''
+    def __init__(self, tree_decomposition=tree_decomposition_with_hrg, ignore_order=False):
+        self.prod_rule_list = []
+        self.tree_decomposition = tree_decomposition
+        self.ignore_order = ignore_order
+
+    def learn(self, hg_list):
+        """ learn from a list of hypergraphs
+
+        Parameters
+        ----------
+        hg_list : list of UndirectedHypergraph
+
+        Returns
+        -------
+        prod_rule_seq_list : list of integers
+            each element corresponds to a sequence of production rules to generate each hypergraph.
+        """
+        prod_rule_seq_list = []
+        for each_hg in hg_list:
+            clique_tree, root_node = tree_decomposition_with_hrg(each_hg, self, return_root=True)
+
+            prod_rule_seq = []
+            stack = []
+
+            # get a pair of myself and children
+            children = sorted(list(clique_tree[root_node].keys()))
+            
+            # extract a temporary production rule
+            prod_rule = extract_prod_rule(None, clique_tree.nodes[root_node]["subhg"],
+                                          [clique_tree.nodes[each_child]["subhg"] for each_child in children])
+            
+            # update the production rule list
+            prod_rule_id, prod_rule = self.update_prod_rule_list(prod_rule)
+            children = reorder_children(root_node, children, prod_rule, clique_tree)
+            stack.extend([(root_node, each_child) for each_child in children[::-1]])
+            prod_rule_seq.append(prod_rule_id)
+            
+            while len(stack) != 0:
+                # get a triple of parent, myself, and children
+                parent, myself = stack.pop()
+                children = sorted(list(dict(clique_tree[myself]).keys()))
+                children.remove(parent)
+
+                # extract a temp prod rule
+                prod_rule = extract_prod_rule(
+                    clique_tree.nodes[parent]["subhg"], clique_tree.nodes[myself]["subhg"],
+                    [clique_tree.nodes[each_child]["subhg"] for each_child in children])
+
+                # update the prod rule list
+                prod_rule_id, prod_rule = self.update_prod_rule_list(prod_rule)
+                children = reorder_children(myself, children, prod_rule, clique_tree)
+                stack.extend([(myself, each_child) for each_child in children[::-1]])
+                prod_rule_seq.append(prod_rule_id)
+            prod_rule_seq_list.append(prod_rule_seq)
+        self._compute_stats()
+        return prod_rule_seq_list
+
+
+def reorder_children(myself, children, prod_rule, clique_tree):
+    """ reorder children so that they match the order in `prod_rule`.
+
+    Parameters
+    ----------
+    myself : int
+    children : list of int
+    prod_rule : ProductionRule
+    clique_tree : nx.Graph
+
+    Returns
+    -------
+    new_children : list of str
+        reordered children
+    """
+    perm = {} # key : `nt_idx`, val : child
+    for each_edge in prod_rule.rhs.edges:
+        if "nt_idx" in prod_rule.rhs.edge_attr(each_edge).keys():
+            for each_child in children:
+                common_node_set = set(
+                    common_node_list(clique_tree.nodes[myself]["subhg"],
+                                     clique_tree.nodes[each_child]["subhg"])[0])
+                if set(prod_rule.rhs.nodes_in_edge(each_edge)) == common_node_set:
+                    assert prod_rule.rhs.edge_attr(each_edge)["nt_idx"] not in perm
+                    perm[prod_rule.rhs.edge_attr(each_edge)["nt_idx"]] = each_child
+    new_children = []
+    assert len(perm) == len(children)
+    for i in range(len(perm)):
+        new_children.append(perm[i])
+    return new_children
+
+
+def extract_prod_rule(parent_hg, myself_hg, children_hg_list, subhg_idx=None):
+    """ extract a production rule from a triple of `parent_hg`, `myself_hg`, and `children_hg_list`.
+
+    Parameters
+    ----------
+    parent_hg : Hypergraph
+    myself_hg : Hypergraph
+    children_hg_list : list of Hypergraph
+
+    Returns
+    -------
+    ProductionRule, consisting of
+        lhs : Hypergraph or None
+        rhs : Hypergraph
+    """
+    def _add_ext_node(hg, ext_nodes):
+        """ mark nodes to be external (ordered ids are assigned)
+
+        Parameters
+        ----------
+        hg : UndirectedHypergraph
+        ext_nodes : list of str
+            list of external nodes
+
+        Returns
+        -------
+        hg : Hypergraph
+            nodes in `ext_nodes` are marked to be external
+        """
+        ext_id = 0
+        ext_id_exists = []
+        for each_node in ext_nodes:
+            ext_id_exists.append('ext_id' in hg.node_attr(each_node))
+        if ext_id_exists and any(ext_id_exists) != all(ext_id_exists):
+            raise ValueError
+        if not all(ext_id_exists):
+            for each_node in ext_nodes:
+                hg.node_attr(each_node)['ext_id'] = ext_id
+                ext_id += 1
+        return hg
+
+    def _check_aromatic(hg, node_list):
+        is_aromatic = False
+        node_aromatic_list = []
+        for each_node in node_list:
+            if hg.node_attr(each_node)['symbol'].is_aromatic:
+                is_aromatic = True
+                node_aromatic_list.append(True)
+            else:
+                node_aromatic_list.append(False)
+        return is_aromatic, node_aromatic_list
+
+    def _check_ring(hg):
+        for each_edge in hg.edges:
+            if not ('tmp' in hg.edge_attr(each_edge) or (not hg.edge_attr(each_edge)['terminal'])):
+                return False
+        return True
+
+    if parent_hg is None:
+        lhs = Hypergraph()
+        node_list = []
+    else:
+        lhs = Hypergraph()
+        node_list, edge_exists = common_node_list(parent_hg, myself_hg)
+        for each_node in node_list:
+            lhs.add_node(each_node,
+                         deepcopy(myself_hg.node_attr(each_node)))
+        is_aromatic, _ = _check_aromatic(parent_hg, node_list)
+        for_ring = _check_ring(myself_hg)
+        bond_symbol_list = []
+        for each_node in node_list:
+            bond_symbol_list.append(parent_hg.node_attr(each_node)['symbol'])
+        lhs.add_edge(
+            node_list,
+            attr_dict=dict(
+                terminal=False,
+                edge_exists=edge_exists,
+                symbol=NTSymbol(
+                    degree=len(node_list),
+                    is_aromatic=is_aromatic,
+                    bond_symbol_list=bond_symbol_list,
+                    for_ring=for_ring)))
+        try:
+            lhs = _add_ext_node(lhs, node_list)
+        except ValueError:
+            import pdb; pdb.set_trace()
+
+    rhs = remove_tmp_edge(deepcopy(myself_hg))
+    #rhs = remove_ext_node(rhs)
+    #rhs = remove_nt_edge(rhs)
+    try:
+        rhs = _add_ext_node(rhs, node_list)
+    except ValueError:
+        import pdb; pdb.set_trace()
+
+    nt_idx = 0
+    if children_hg_list is not None:
+        for each_child_hg in children_hg_list:
+            node_list, edge_exists = common_node_list(myself_hg, each_child_hg)
+            is_aromatic, _ = _check_aromatic(myself_hg, node_list)
+            for_ring = _check_ring(each_child_hg)
+            bond_symbol_list = []
+            for each_node in node_list:
+                bond_symbol_list.append(myself_hg.node_attr(each_node)['symbol'])
+            rhs.add_edge(
+                node_list,
+                attr_dict=dict(
+                    terminal=False,
+                    nt_idx=nt_idx,
+                    edge_exists=edge_exists,
+                    symbol=NTSymbol(degree=len(node_list),
+                                    is_aromatic=is_aromatic,
+                                    bond_symbol_list=bond_symbol_list,
+                                    for_ring=for_ring)))
+            nt_idx += 1
+    prod_rule = ProductionRule(lhs, rhs)
+    prod_rule.subhg_idx = subhg_idx
+    if DEBUG:
+        if sorted(list(prod_rule.ext_node.keys())) \
+           != list(np.arange(len(prod_rule.ext_node))):
+            raise RuntimeError('ext_id is not continuous')
+    return prod_rule
+
+
+def _find_root(clique_tree):
+    max_node = None
+    num_nodes_max = -np.inf
+    for each_node in clique_tree.nodes:
+        if clique_tree.nodes[each_node]['subhg'].num_nodes > num_nodes_max:
+            max_node = each_node
+            num_nodes_max = clique_tree.nodes[each_node]['subhg'].num_nodes
+        '''
+        children = sorted(list(clique_tree[each_node].keys()))
+        prod_rule = extract_prod_rule(None,
+                                      clique_tree.nodes[each_node]["subhg"],
+                                      [clique_tree.nodes[each_child]["subhg"]
+                                       for each_child in children])
+        for each_start_rule in start_rule_list:
+            if prod_rule.is_same(each_start_rule):
+                return each_node
+        '''
+    return max_node
+
+def remove_ext_node(hg):
+    for each_node in hg.nodes:
+        hg.node_attr(each_node).pop('ext_id', None)
+    return hg
+
+def remove_nt_edge(hg):
+    remove_edge_list = []
+    for each_edge in hg.edges:
+        if not hg.edge_attr(each_edge)['terminal']:
+            remove_edge_list.append(each_edge)
+    hg.remove_edges(remove_edge_list)
+    return hg
+
+def remove_tmp_edge(hg):
+    remove_edge_list = []
+    for each_edge in hg.edges:
+        if hg.edge_attr(each_edge).get('tmp', False):
+            remove_edge_list.append(each_edge)
+    hg.remove_edges(remove_edge_list)
+    return hg
diff --git a/models/mhg_model/graph_grammar/graph_grammar/symbols.py b/models/mhg_model/graph_grammar/graph_grammar/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..a024fb263c0aed40f9dc3b816e3c87913594f96c
--- /dev/null
+++ b/models/mhg_model/graph_grammar/graph_grammar/symbols.py
@@ -0,0 +1,180 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jan 1 2018"
+
+from typing import List
+
+class TSymbol(object):
+
+    ''' terminal symbol
+
+    Attributes
+    ----------
+    degree : int
+        the number of nodes in a hyperedge
+    is_aromatic : bool
+        whether or not the hyperedge is in an aromatic ring
+    symbol : str
+        atomic symbol
+    num_explicit_Hs : int
+        the number of hydrogens associated to this hyperedge
+    formal_charge : int
+        charge
+    chirality : int
+        chirality
+    '''
+
+    def __init__(self, degree, is_aromatic,
+                 symbol, num_explicit_Hs, formal_charge, chirality):
+        self.degree = degree
+        self.is_aromatic = is_aromatic
+        self.symbol = symbol
+        self.num_explicit_Hs = num_explicit_Hs
+        self.formal_charge = formal_charge
+        self.chirality = chirality
+
+    @property
+    def terminal(self):
+        return True
+
+    def __eq__(self, other):
+        if not isinstance(other, TSymbol):
+            return False
+        if self.degree != other.degree:
+            return False
+        if self.is_aromatic != other.is_aromatic:
+            return False
+        if self.symbol != other.symbol:
+            return False
+        if self.num_explicit_Hs != other.num_explicit_Hs:
+            return False
+        if self.formal_charge != other.formal_charge:
+            return False
+        if self.chirality != other.chirality:
+            return False
+        return True
+
+    def __hash__(self):
+        return self.__str__().__hash__()
+
+    def __str__(self):
+        return f'degree={self.degree}, is_aromatic={self.is_aromatic}, '\
+            f'symbol={self.symbol}, '\
+            f'num_explicit_Hs={self.num_explicit_Hs}, '\
+            f'formal_charge={self.formal_charge}, chirality={self.chirality}'
+
+
+class NTSymbol(object):
+
+    ''' non-terminal symbol
+
+    Attributes
+    ----------
+    degree : int
+        degree of the hyperedge
+    is_aromatic : bool
+        if True, at least one of the associated bonds must be aromatic.
+    node_aromatic_list : list of bool
+        indicate whether each of the nodes is aromatic or not.
+    bond_type_list : list of int
+        bond type of each node"
+    '''
+
+    def __init__(self, degree: int, is_aromatic: bool,
+                 bond_symbol_list: list,
+                 for_ring=False):
+        self.degree = degree
+        self.is_aromatic = is_aromatic
+        self.for_ring = for_ring
+        self.bond_symbol_list = bond_symbol_list
+
+    @property
+    def terminal(self) -> bool:
+        return False
+
+    @property
+    def symbol(self):
+        return f'NT{self.degree}'
+
+    def __eq__(self, other) -> bool:
+        if not isinstance(other, NTSymbol):
+            return False
+
+        if self.degree != other.degree:
+            return False
+        if self.is_aromatic != other.is_aromatic:
+            return False
+        if self.for_ring != other.for_ring:
+            return False
+        if len(self.bond_symbol_list) != len(other.bond_symbol_list):
+            return False
+        for each_idx in range(len(self.bond_symbol_list)):
+            if self.bond_symbol_list[each_idx] != other.bond_symbol_list[each_idx]:
+                return False
+        return True
+
+    def __hash__(self):
+        return self.__str__().__hash__()
+
+    def __str__(self) -> str:
+        return f'degree={self.degree}, is_aromatic={self.is_aromatic}, '\
+            f'bond_symbol_list={[str(each_symbol) for each_symbol in self.bond_symbol_list]}'\
+            f'for_ring={self.for_ring}'
+
+
+class BondSymbol(object):
+    
+
+    ''' Bond symbol
+
+    Attributes
+    ----------
+    is_aromatic : bool
+        if True, at least one of the associated bonds must be aromatic.
+    bond_type : int
+        bond type of each node"
+    '''
+
+    def __init__(self, is_aromatic: bool,
+                 bond_type: int,
+                 stereo: int):
+        self.is_aromatic = is_aromatic
+        self.bond_type = bond_type
+        self.stereo = stereo
+
+    def __eq__(self, other) -> bool:
+        if not isinstance(other, BondSymbol):
+            return False
+
+        if self.is_aromatic != other.is_aromatic:
+            return False
+        if self.bond_type != other.bond_type:
+            return False
+        if self.stereo != other.stereo:
+            return False
+        return True
+
+    def __hash__(self):
+        return self.__str__().__hash__()
+
+    def __str__(self) -> str:
+        return f'is_aromatic={self.is_aromatic}, '\
+            f'bond_type={self.bond_type}, '\
+            f'stereo={self.stereo}, '
diff --git a/models/mhg_model/graph_grammar/graph_grammar/utils.py b/models/mhg_model/graph_grammar/graph_grammar/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..18c6bcad4b8ff341f2ffb844dd2265976cca2803
--- /dev/null
+++ b/models/mhg_model/graph_grammar/graph_grammar/utils.py
@@ -0,0 +1,130 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jun 4 2018"
+
+from ..hypergraph import Hypergraph
+from copy import deepcopy
+from typing import List
+import numpy as np
+
+
+def common_node_list(hg1: Hypergraph, hg2: Hypergraph) -> List[str]:
+    """ return a list of common nodes
+
+    Parameters
+    ----------
+    hg1, hg2 : Hypergraph
+
+    Returns
+    -------
+    list of str
+        list of common nodes
+    """
+    if hg1 is None or hg2 is None:
+        return [], False
+    else:
+        node_set = hg1.nodes.intersection(hg2.nodes)
+        node_dict = {}
+        if 'order4hrg' in hg1.node_attr(list(hg1.nodes)[0]):
+            for each_node in node_set:
+                node_dict[each_node] = hg1.node_attr(each_node)['order4hrg']
+        else:
+            for each_node in node_set:
+                node_dict[each_node] = hg1.node_attr(each_node)['symbol'].__hash__()
+        node_list = []
+        for each_key, _ in sorted(node_dict.items(), key=lambda x:x[1]):
+            node_list.append(each_key)
+        edge_name = hg1.has_edge(node_list, ignore_order=True)
+        if edge_name:
+            if not hg1.edge_attr(edge_name).get('terminal', True):
+                node_list = hg1.nodes_in_edge(edge_name)
+            return node_list, True
+        else:
+            return node_list, False
+
+
+def _node_match(node1, node2):
+    # if the nodes are hyperedges, `atom_attr` determines the match
+    if node1['bipartite'] == 'edge' and node2['bipartite'] == 'edge':
+        return node1["attr_dict"]['symbol'] == node2["attr_dict"]['symbol']
+    elif node1['bipartite'] == 'node' and node2['bipartite'] == 'node':
+        # bond_symbol
+        return node1['attr_dict']['symbol'] == node2['attr_dict']['symbol']
+    else:
+        return False
+
+def _easy_node_match(node1, node2):
+    # if the nodes are hyperedges, `atom_attr` determines the match
+    if node1['bipartite'] == 'edge' and node2['bipartite'] == 'edge':
+        return node1["attr_dict"].get('symbol', None) == node2["attr_dict"].get('symbol', None)
+    elif node1['bipartite'] == 'node' and node2['bipartite'] == 'node':
+        # bond_symbol
+        return node1['attr_dict'].get('ext_id', -1) == node2['attr_dict'].get('ext_id', -1)\
+            and node1['attr_dict']['symbol'] == node2['attr_dict']['symbol']
+    else:
+        return False
+
+
+def _node_match_prod_rule(node1, node2, ignore_order=False):
+    # if the nodes are hyperedges, `atom_attr` determines the match
+    if node1['bipartite'] == 'edge' and node2['bipartite'] == 'edge':
+        return node1["attr_dict"]['symbol'] == node2["attr_dict"]['symbol']
+    elif node1['bipartite'] == 'node' and node2['bipartite'] == 'node':
+        # ext_id, order4hrg, bond_symbol
+        if ignore_order:
+            return node1['attr_dict']['symbol'] == node2['attr_dict']['symbol']
+        else:
+            return node1['attr_dict']['symbol'] == node2['attr_dict']['symbol']\
+                and node1['attr_dict'].get('ext_id', -1) == node2['attr_dict'].get('ext_id', -1)
+    else:
+        return False
+
+
+def _edge_match(edge1, edge2, ignore_order=False):
+    #return True
+    if ignore_order:
+        return True
+    else:
+        return edge1["order"] == edge2["order"]
+
+def masked_softmax(logit, mask):
+    ''' compute a probability distribution from logit
+
+    Parameters
+    ----------
+    logit : array-like, length D
+        each element indicates how each dimension is likely to be chosen
+        (the larger, the more likely)
+    mask : array-like, length D
+        each element is either 0 or 1.
+        if 0, the dimension is ignored
+        when computing the probability distribution.
+
+    Returns
+    -------
+    prob_dist : array, length D
+        probability distribution computed from logit.
+        if `mask[d] = 0`, `prob_dist[d] = 0`.
+    '''
+    if logit.shape != mask.shape:
+        raise ValueError('logit and mask must have the same shape')
+    c = np.max(logit)
+    exp_logit = np.exp(logit - c) * mask
+    sum_exp_logit = exp_logit @ mask
+    return exp_logit / sum_exp_logit
diff --git a/models/mhg_model/graph_grammar/hypergraph.py b/models/mhg_model/graph_grammar/hypergraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..15448755e3b40ac0f2b5f80c2b84511a904c2dd1
--- /dev/null
+++ b/models/mhg_model/graph_grammar/hypergraph.py
@@ -0,0 +1,544 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jan 31 2018"
+
+from copy import deepcopy
+from typing import List, Dict, Tuple
+import networkx as nx
+import numpy as np
+import os
+
+
+class Hypergraph(object):
+    '''
+    A class of a hypergraph.
+    Each hyperedge can be ordered. For the ordered case,
+    edges adjacent to the hyperedge node are labeled by their orders.
+
+    Attributes
+    ----------
+    hg : nx.Graph
+        a bipartite graph representation of a hypergraph
+    edge_idx : int
+        total number of hyperedges that exist so far
+    '''
+    def __init__(self):
+        self.hg = nx.Graph()
+        self.edge_idx = 0
+        self.nodes = set([])
+        self.num_nodes = 0
+        self.edges = set([])
+        self.num_edges = 0
+        self.nodes_in_edge_dict = {}
+
+    def add_node(self, node: str, attr_dict=None):
+        ''' add a node to hypergraph
+
+        Parameters
+        ----------
+        node : str
+            node name
+        attr_dict : dict
+            dictionary of node attributes
+        '''
+        self.hg.add_node(node, bipartite='node', attr_dict=attr_dict)
+        if node not in self.nodes:
+            self.num_nodes += 1
+        self.nodes.add(node)
+
+    def add_edge(self, node_list: List[str], attr_dict=None, edge_name=None):
+        ''' add an edge consisting of nodes `node_list`
+
+        Parameters
+        ----------
+        node_list : list 
+            ordered list of nodes that consist the edge
+        attr_dict : dict
+            dictionary of edge attributes
+        '''
+        if edge_name is None:
+            edge = 'e{}'.format(self.edge_idx)
+        else:
+            assert edge_name not in self.edges
+            edge = edge_name
+        self.hg.add_node(edge, bipartite='edge', attr_dict=attr_dict)
+        if edge not in self.edges:
+            self.num_edges += 1
+        self.edges.add(edge)
+        self.nodes_in_edge_dict[edge] = node_list
+        if type(node_list) == list:
+            for node_idx, each_node in enumerate(node_list):
+                self.hg.add_edge(edge, each_node, order=node_idx)
+                if each_node not in self.nodes:
+                    self.num_nodes += 1
+                self.nodes.add(each_node)
+
+        elif type(node_list) == set:
+            for each_node in node_list:
+                self.hg.add_edge(edge, each_node, order=-1)
+                if each_node not in self.nodes:
+                    self.num_nodes += 1
+                self.nodes.add(each_node)
+        else:
+            raise ValueError
+        self.edge_idx += 1
+        return edge
+
+    def remove_node(self, node: str, remove_connected_edges=True):
+        ''' remove a node
+
+        Parameters
+        ----------
+        node : str
+            node name
+        remove_connected_edges : bool
+            if True, remove edges that are adjacent to the node
+        '''
+        if remove_connected_edges:
+            connected_edges = deepcopy(self.adj_edges(node))
+            for each_edge in connected_edges:
+                self.remove_edge(each_edge)
+        self.hg.remove_node(node)
+        self.num_nodes -= 1
+        self.nodes.remove(node)
+
+    def remove_nodes(self, node_iter, remove_connected_edges=True):
+        ''' remove a set of nodes
+
+        Parameters
+        ----------
+        node_iter : iterator of strings
+            nodes to be removed
+        remove_connected_edges : bool
+            if True, remove edges that are adjacent to the node        
+        '''
+        for each_node in node_iter:
+            self.remove_node(each_node, remove_connected_edges)
+
+    def remove_edge(self, edge: str):
+        ''' remove an edge
+
+        Parameters
+        ----------
+        edge : str
+            edge to be removed
+        '''
+        self.hg.remove_node(edge)
+        self.edges.remove(edge)
+        self.num_edges -= 1
+        self.nodes_in_edge_dict.pop(edge)
+
+    def remove_edges(self, edge_iter):
+        ''' remove a set of edges
+
+        Parameters
+        ----------
+        edge_iter : iterator of strings
+            edges to be removed
+        '''
+        for each_edge in edge_iter:
+            self.remove_edge(each_edge)
+
+    def remove_edges_with_attr(self, edge_attr_dict):
+        remove_edge_list = []
+        for each_edge in self.edges:
+            satisfy = True
+            for each_key, each_val in edge_attr_dict.items():
+                if not satisfy:
+                    break
+                try:
+                    if self.edge_attr(each_edge)[each_key] != each_val:
+                        satisfy = False
+                except KeyError:
+                    satisfy = False
+            if satisfy:
+                remove_edge_list.append(each_edge)
+        self.remove_edges(remove_edge_list)
+
+    def remove_subhg(self, subhg):
+        ''' remove subhypergraph.
+        all of the hyperedges are removed.
+        each node of subhg is removed if its degree becomes 0 after removing hyperedges.
+
+        Parameters
+        ----------
+        subhg : Hypergraph
+        '''
+        for each_edge in subhg.edges:
+            self.remove_edge(each_edge)
+        for each_node in subhg.nodes:
+            if self.degree(each_node) == 0:
+                self.remove_node(each_node)
+
+    def nodes_in_edge(self, edge):
+        ''' return an ordered list of nodes in a given edge.
+
+        Parameters
+        ----------
+        edge : str
+            edge whose nodes are returned
+
+        Returns
+        -------
+        list or set
+            ordered list or set of nodes that belong to the edge
+        '''
+        if edge.startswith('e'):
+            return self.nodes_in_edge_dict[edge]
+        else:
+            adj_node_list = self.hg.adj[edge]
+            adj_node_order_list = []
+            adj_node_name_list = []
+            for each_node in adj_node_list:
+                adj_node_order_list.append(adj_node_list[each_node]['order'])
+                adj_node_name_list.append(each_node)
+            if adj_node_order_list == [-1] * len(adj_node_order_list):
+                return set(adj_node_name_list)
+            else:
+                return [adj_node_name_list[each_idx] for each_idx
+                        in np.argsort(adj_node_order_list)]
+
+    def adj_edges(self, node):
+        ''' return a dict of adjacent hyperedges
+
+        Parameters
+        ----------
+        node : str
+
+        Returns
+        -------
+        set
+            set of edges that are adjacent to `node`
+        '''
+        return self.hg.adj[node]
+
+    def adj_nodes(self, node):
+        ''' return a set of adjacent nodes
+
+        Parameters
+        ----------
+        node : str
+
+        Returns
+        -------
+        set
+            set of nodes that are adjacent to `node`
+        '''
+        node_set = set([])
+        for each_adj_edge in self.adj_edges(node):
+            node_set.update(set(self.nodes_in_edge(each_adj_edge)))
+        node_set.discard(node)
+        return node_set
+
+    def has_edge(self, node_list, ignore_order=False):
+        for each_edge in self.edges:
+            if ignore_order:
+                if set(self.nodes_in_edge(each_edge)) == set(node_list):
+                    return each_edge
+            else:
+                if self.nodes_in_edge(each_edge) == node_list:
+                    return each_edge
+        return False
+
+    def degree(self, node):
+        return len(self.hg.adj[node])
+
+    def degrees(self):
+        return {each_node: self.degree(each_node) for each_node in self.nodes}
+
+    def edge_degree(self, edge):
+        return len(self.nodes_in_edge(edge))
+
+    def edge_degrees(self):
+        return {each_edge: self.edge_degree(each_edge) for each_edge in self.edges}
+
+    def is_adj(self, node1, node2):
+        return node1 in self.adj_nodes(node2)
+
+    def adj_subhg(self, node, ident_node_dict=None):
+        """ return a subhypergraph consisting of a set of nodes and hyperedges adjacent to `node`.
+        if an adjacent node has a self-loop hyperedge, it will be also added to the subhypergraph.
+
+        Parameters
+        ----------
+        node : str
+        ident_node_dict : dict
+            dict containing identical nodes. see `get_identical_node_dict` for more details
+
+        Returns
+        -------
+        subhg : Hypergraph
+        """
+        if ident_node_dict is None:
+            ident_node_dict = self.get_identical_node_dict()
+        adj_node_set = set(ident_node_dict[node])
+        adj_edge_set = set([])
+        for each_node in ident_node_dict[node]:
+            adj_edge_set.update(set(self.adj_edges(each_node)))
+        fixed_adj_edge_set = deepcopy(adj_edge_set)
+        for each_edge in fixed_adj_edge_set:
+            other_nodes = self.nodes_in_edge(each_edge)
+            adj_node_set.update(other_nodes)
+
+            # if the adjacent node has self-loop edge, it will be appended to adj_edge_list.
+            for each_node in other_nodes:
+                for other_edge in set(self.adj_edges(each_node)) - set([each_edge]):
+                    if len(set(self.nodes_in_edge(other_edge)) \
+                           - set(self.nodes_in_edge(each_edge))) == 0:
+                        adj_edge_set.update(set([other_edge]))
+        subhg = Hypergraph()
+        for each_node in adj_node_set:
+            subhg.add_node(each_node, attr_dict=self.node_attr(each_node))
+        for each_edge in adj_edge_set:
+            subhg.add_edge(self.nodes_in_edge(each_edge),
+                           attr_dict=self.edge_attr(each_edge),
+                           edge_name=each_edge)
+        subhg.edge_idx = self.edge_idx
+        return subhg
+
+    def get_subhg(self, node_list, edge_list, ident_node_dict=None):
+        """ return a subhypergraph consisting of a set of nodes and hyperedges adjacent to `node`.
+        if an adjacent node has a self-loop hyperedge, it will be also added to the subhypergraph.
+
+        Parameters
+        ----------
+        node : str
+        ident_node_dict : dict
+            dict containing identical nodes. see `get_identical_node_dict` for more details
+
+        Returns
+        -------
+        subhg : Hypergraph
+        """
+        if ident_node_dict is None:
+            ident_node_dict = self.get_identical_node_dict()
+        adj_node_set = set([])
+        for each_node in node_list:
+            adj_node_set.update(set(ident_node_dict[each_node]))
+        adj_edge_set = set(edge_list)
+
+        subhg = Hypergraph()
+        for each_node in adj_node_set:
+            subhg.add_node(each_node,
+                           attr_dict=deepcopy(self.node_attr(each_node)))
+        for each_edge in adj_edge_set:
+            subhg.add_edge(self.nodes_in_edge(each_edge),
+                           attr_dict=deepcopy(self.edge_attr(each_edge)),
+                           edge_name=each_edge)
+        subhg.edge_idx = self.edge_idx
+        return subhg
+
+    def copy(self):
+        ''' return a copy of the object
+        
+        Returns
+        -------
+        Hypergraph
+        '''
+        return deepcopy(self)
+
+    def node_attr(self, node):
+        return self.hg.nodes[node]['attr_dict']
+
+    def edge_attr(self, edge):
+        return self.hg.nodes[edge]['attr_dict']
+
+    def set_node_attr(self, node, attr_dict):
+        for each_key, each_val in attr_dict.items():
+            self.hg.nodes[node]['attr_dict'][each_key] = each_val
+
+    def set_edge_attr(self, edge, attr_dict):
+        for each_key, each_val in attr_dict.items():
+            self.hg.nodes[edge]['attr_dict'][each_key] = each_val
+
+    def get_identical_node_dict(self):
+        ''' get identical nodes
+        nodes are identical if they share the same set of adjacent edges.
+        
+        Returns
+        -------
+        ident_node_dict : dict
+            ident_node_dict[node] returns a list of nodes that are identical to `node`.
+        '''
+        ident_node_dict = {}
+        for each_node in self.nodes:
+            ident_node_list = []
+            for each_other_node in self.nodes:
+                if each_other_node == each_node:
+                    ident_node_list.append(each_other_node)
+                elif self.adj_edges(each_node) == self.adj_edges(each_other_node) \
+                   and len(self.adj_edges(each_node)) != 0:
+                    ident_node_list.append(each_other_node)
+            ident_node_dict[each_node] = ident_node_list
+        return ident_node_dict
+    '''
+        ident_node_dict = {}
+        for each_node in self.nodes:
+            ident_node_dict[each_node] = [each_node]
+        return ident_node_dict
+    '''
+
+    def get_leaf_edge(self):
+        ''' get an edge that is incident only to one edge
+
+        Returns
+        -------
+        if exists, return a leaf edge. otherwise, return None.
+        '''
+        for each_edge in self.edges:
+            if len(self.adj_nodes(each_edge)) == 1:
+                if 'tmp' not in self.edge_attr(each_edge):
+                    return each_edge
+        return None
+
+    def get_nontmp_edge(self):
+        for each_edge in self.edges:
+            if 'tmp' not in self.edge_attr(each_edge):
+                return each_edge
+        return None
+
+    def is_subhg(self, hg):
+        ''' return whether this hypergraph is a subhypergraph of `hg`
+
+        Returns
+        -------
+        True if self \in hg,
+        False otherwise.
+        '''
+        for each_node in self.nodes:
+            if each_node not in hg.nodes:
+                return False
+        for each_edge in self.edges:
+            if each_edge not in hg.edges:
+                return False
+        return True
+
+    def in_cycle(self, node, visited=None, parent='', root_node='') -> bool:
+        ''' if `node` is in a cycle, then return True. otherwise, False.
+
+        Parameters
+        ----------
+        node : str
+            node in a hypergraph
+        visited : list
+            list of visited nodes, used for recursion
+        parent : str
+            parent node, used to eliminate a cycle consisting of two nodes and one edge.
+
+        Returns
+        -------
+        bool
+        '''
+        if visited is None:
+            visited = []
+        if parent == '':
+            visited = []
+        if root_node == '':
+            root_node = node
+        visited.append(node)
+        for each_adj_node in self.adj_nodes(node):
+            if each_adj_node not in visited:
+                if self.in_cycle(each_adj_node, visited, node, root_node):
+                    return True
+            elif each_adj_node != parent and each_adj_node == root_node:
+                return True
+        return False
+
+    
+    def draw(self, file_path=None, with_node=False, with_edge_name=False):
+        ''' draw hypergraph
+        '''
+        import graphviz
+        G = graphviz.Graph(format='png')
+        for each_node in self.nodes:
+            if 'ext_id' in self.node_attr(each_node):
+                G.node(each_node, label='',
+                       shape='circle', width='0.1', height='0.1', style='filled',
+                       fillcolor='black')
+            else:
+                if with_node:
+                    G.node(each_node, label='',
+                           shape='circle', width='0.1', height='0.1', style='filled',
+                           fillcolor='gray')
+        edge_list = []
+        for each_edge in self.edges:
+            if self.edge_attr(each_edge).get('terminal', False):
+                G.node(each_edge,
+                       label=self.edge_attr(each_edge)['symbol'].symbol if not with_edge_name \
+                       else self.edge_attr(each_edge)['symbol'].symbol + ', ' + each_edge,
+                       fontcolor='black', shape='square')
+            elif self.edge_attr(each_edge).get('tmp', False):
+                G.node(each_edge, label='tmp' if not with_edge_name else 'tmp, ' + each_edge,
+                       fontcolor='black', shape='square')
+            else:
+                G.node(each_edge,
+                       label=self.edge_attr(each_edge)['symbol'].symbol if not with_edge_name \
+                       else self.edge_attr(each_edge)['symbol'].symbol + ', ' + each_edge,
+                       fontcolor='black', shape='square', style='filled')
+            if with_node:
+                for each_node in self.nodes_in_edge(each_edge):
+                    G.edge(each_edge, each_node)
+            else:
+                for each_node in self.nodes_in_edge(each_edge):
+                    if 'ext_id' in self.node_attr(each_node)\
+                       and set([each_node, each_edge]) not in edge_list:
+                        G.edge(each_edge, each_node)
+                        edge_list.append(set([each_node, each_edge]))
+                for each_other_edge in self.adj_nodes(each_edge):
+                    if set([each_edge, each_other_edge]) not in edge_list:
+                        num_bond = 0
+                        common_node_set = set(self.nodes_in_edge(each_edge))\
+                                          .intersection(set(self.nodes_in_edge(each_other_edge)))
+                        for each_node in common_node_set:
+                            if self.node_attr(each_node)['symbol'].bond_type in [1, 2, 3]:
+                                num_bond += self.node_attr(each_node)['symbol'].bond_type
+                            elif self.node_attr(each_node)['symbol'].bond_type in [12]:
+                                num_bond += 1
+                            else:
+                                raise NotImplementedError('unsupported bond type')
+                        for _ in range(num_bond):
+                            G.edge(each_edge, each_other_edge)
+                        edge_list.append(set([each_edge, each_other_edge]))
+        if file_path is not None:
+            G.render(file_path, cleanup=True)
+            #os.remove(file_path)
+        return G
+
+    def is_dividable(self, node):
+        _hg = deepcopy(self.hg)
+        _hg.remove_node(node)
+        return (not nx.is_connected(_hg))
+
+    def divide(self, node):
+        subhg_list = []
+
+        hg_wo_node = deepcopy(self)
+        hg_wo_node.remove_node(node, remove_connected_edges=False)
+        connected_components = nx.connected_components(hg_wo_node.hg)
+        for each_component in connected_components:
+            node_list = [node]
+            edge_list = []
+            node_list.extend([each_node for each_node in each_component
+                              if each_node.startswith('bond_')])
+            edge_list.extend([each_edge for each_edge in each_component
+                              if each_edge.startswith('e')])
+            subhg_list.append(self.get_subhg(node_list, edge_list))
+            #subhg_list[-1].set_node_attr(node, {'divided': True})
+        return subhg_list
+
diff --git a/models/mhg_model/graph_grammar/io/__init__.py b/models/mhg_model/graph_grammar/io/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e6131daba8a4f601ae72d37e6eb035d9503045
--- /dev/null
+++ b/models/mhg_model/graph_grammar/io/__init__.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jan 1 2018"
+
diff --git a/models/mhg_model/graph_grammar/io/__pycache__/__init__.cpython-310.pyc b/models/mhg_model/graph_grammar/io/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d1eaa9f0f6cc12a34e90d8a24278729e3df8ef4c
Binary files /dev/null and b/models/mhg_model/graph_grammar/io/__pycache__/__init__.cpython-310.pyc differ
diff --git a/models/mhg_model/graph_grammar/io/__pycache__/smi.cpython-310.pyc b/models/mhg_model/graph_grammar/io/__pycache__/smi.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..668564306df44bae188271ebc8956bfdedacab80
Binary files /dev/null and b/models/mhg_model/graph_grammar/io/__pycache__/smi.cpython-310.pyc differ
diff --git a/models/mhg_model/graph_grammar/io/smi.py b/models/mhg_model/graph_grammar/io/smi.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd17428fdcb7888365cffc59867976f415841d79
--- /dev/null
+++ b/models/mhg_model/graph_grammar/io/smi.py
@@ -0,0 +1,559 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jan 12 2018"
+
+from copy import deepcopy
+from rdkit import Chem
+from rdkit import RDLogger
+import networkx as nx
+import numpy as np
+from ..hypergraph import Hypergraph
+from ..graph_grammar.symbols import TSymbol, BondSymbol
+
+# supress warnings
+lg = RDLogger.logger()
+lg.setLevel(RDLogger.CRITICAL)
+
+
+class HGGen(object):
+    """
+    load .smi file and yield a hypergraph.
+
+    Attributes
+    ----------
+    path_to_file : str
+        path to .smi file
+    kekulize : bool
+        kekulize or not
+    add_Hs : bool
+        add implicit hydrogens to the molecule or not.
+    all_single : bool
+        if True, all multiple bonds are summarized into a single bond with some attributes
+
+    Yields
+    ------
+    Hypergraph
+    """
+    def __init__(self, path_to_file, kekulize=True, add_Hs=False, all_single=True):
+        self.num_line = 1
+        self.mol_gen = Chem.SmilesMolSupplier(path_to_file, titleLine=False)
+        self.kekulize = kekulize
+        self.add_Hs = add_Hs
+        self.all_single = all_single
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        '''
+        each_mol = None
+        while each_mol is None:
+            each_mol = next(self.mol_gen)
+        '''
+        # not ignoring parse errors
+        each_mol = next(self.mol_gen)
+        if each_mol is None:
+            raise ValueError(f'incorrect smiles in line {self.num_line}')
+        else:
+            self.num_line += 1
+        return mol_to_hg(each_mol, self.kekulize, self.add_Hs)
+
+
+def mol_to_bipartite(mol, kekulize):
+    """
+    get a bipartite representation of a molecule.
+
+    Parameters
+    ----------
+    mol : rdkit.Chem.rdchem.Mol
+        molecule object
+
+    Returns
+    -------
+    nx.Graph
+        a bipartite graph representing which bond is connected to which atoms.
+    """
+    try:
+        mol = standardize_stereo(mol)
+    except KeyError:
+        print(Chem.MolToSmiles(mol))
+        raise KeyError
+        
+    if kekulize:
+        Chem.Kekulize(mol)
+
+    bipartite_g = nx.Graph()
+    for each_atom in mol.GetAtoms():
+        bipartite_g.add_node(f"atom_{each_atom.GetIdx()}",
+                             atom_attr=atom_attr(each_atom, kekulize))
+
+    for each_bond in mol.GetBonds():
+        bond_idx = each_bond.GetIdx()
+        bipartite_g.add_node(
+            f"bond_{bond_idx}",
+            bond_attr=bond_attr(each_bond, kekulize))
+        bipartite_g.add_edge(
+            f"atom_{each_bond.GetBeginAtomIdx()}",
+            f"bond_{bond_idx}")
+        bipartite_g.add_edge(
+            f"atom_{each_bond.GetEndAtomIdx()}",
+            f"bond_{bond_idx}")
+    return bipartite_g
+
+
+def mol_to_hg(mol, kekulize, add_Hs):
+    """
+    get a bipartite representation of a molecule.
+
+    Parameters
+    ----------
+    mol : rdkit.Chem.rdchem.Mol
+        molecule object
+    kekulize : bool
+        kekulize or not
+    add_Hs : bool
+        add implicit hydrogens to the molecule or not.
+
+    Returns
+    -------
+    Hypergraph
+    """
+    if add_Hs:
+        mol = Chem.AddHs(mol)
+
+    if kekulize:
+        Chem.Kekulize(mol)
+
+    bipartite_g = mol_to_bipartite(mol, kekulize)
+    hg = Hypergraph()
+    for each_atom in [each_node for each_node in bipartite_g.nodes()
+                      if each_node.startswith('atom_')]:
+        node_set = set([])
+        for each_bond in bipartite_g.adj[each_atom]:
+            hg.add_node(each_bond,
+                        attr_dict=bipartite_g.nodes[each_bond]['bond_attr'])
+            node_set.add(each_bond)
+        hg.add_edge(node_set,
+                    attr_dict=bipartite_g.nodes[each_atom]['atom_attr'])
+    return hg
+
+
+def hg_to_mol(hg, verbose=False):
+    """ convert a hypergraph into Mol object
+
+    Parameters
+    ----------
+    hg : Hypergraph
+
+    Returns
+    -------
+    mol : Chem.RWMol
+    """
+    mol = Chem.RWMol()
+    atom_dict = {}
+    bond_set = set([])
+    for each_edge in hg.edges:
+        atom = Chem.Atom(hg.edge_attr(each_edge)['symbol'].symbol)
+        atom.SetNumExplicitHs(hg.edge_attr(each_edge)['symbol'].num_explicit_Hs)
+        atom.SetFormalCharge(hg.edge_attr(each_edge)['symbol'].formal_charge)
+        atom.SetChiralTag(
+            Chem.rdchem.ChiralType.values[
+                hg.edge_attr(each_edge)['symbol'].chirality])
+        atom_idx = mol.AddAtom(atom)
+        atom_dict[each_edge] = atom_idx
+
+    for each_node in hg.nodes:
+        edge_1, edge_2 = hg.adj_edges(each_node)
+        if edge_1+edge_2 not in bond_set:
+            if hg.node_attr(each_node)['symbol'].bond_type <= 3:
+                num_bond = hg.node_attr(each_node)['symbol'].bond_type
+            elif hg.node_attr(each_node)['symbol'].bond_type == 12:
+                num_bond = 1
+            else:
+                raise ValueError(f'too many bonds; {hg.node_attr(each_node)["bond_symbol"].bond_type}')
+            _ = mol.AddBond(atom_dict[edge_1],
+                            atom_dict[edge_2],
+                            order=Chem.rdchem.BondType.values[num_bond])
+            bond_idx = mol.GetBondBetweenAtoms(atom_dict[edge_1], atom_dict[edge_2]).GetIdx()
+
+            # stereo
+            mol.GetBondWithIdx(bond_idx).SetStereo(
+                Chem.rdchem.BondStereo.values[hg.node_attr(each_node)['symbol'].stereo])
+            bond_set.update([edge_1+edge_2])
+            bond_set.update([edge_2+edge_1])
+    mol.UpdatePropertyCache()
+    mol = mol.GetMol()
+    not_stereo_mol = deepcopy(mol)
+    if Chem.MolFromSmiles(Chem.MolToSmiles(not_stereo_mol)) is None:
+        raise RuntimeError('no valid molecule was obtained.')
+    try:
+        mol = set_stereo(mol)
+        is_stereo = True
+    except:
+        import traceback
+        traceback.print_exc()
+        is_stereo = False
+    mol_tmp = deepcopy(mol)
+    Chem.SetAromaticity(mol_tmp)
+    if Chem.MolFromSmiles(Chem.MolToSmiles(mol_tmp)) is not None:
+        mol = mol_tmp
+    else:
+        if Chem.MolFromSmiles(Chem.MolToSmiles(mol)) is None:
+            mol = not_stereo_mol
+    mol.UpdatePropertyCache()
+    Chem.GetSymmSSSR(mol)
+    mol = Chem.MolFromSmiles(Chem.MolToSmiles(mol))
+    if verbose:
+        return mol, is_stereo
+    else:
+        return mol
+
+def hgs_to_mols(hg_list, ignore_error=False):
+    if ignore_error:
+        mol_list = []
+        for each_hg in hg_list:
+            try:
+                mol = hg_to_mol(each_hg)
+            except:
+                mol = None
+            mol_list.append(mol)
+    else:
+        mol_list = [hg_to_mol(each_hg) for each_hg in hg_list]
+    return mol_list
+
+def hgs_to_smiles(hg_list, ignore_error=False):
+    mol_list = hgs_to_mols(hg_list, ignore_error)
+    smiles_list = []
+    for each_mol in mol_list:
+        try:
+            smiles_list.append(
+                Chem.MolToSmiles(
+                    Chem.MolFromSmiles(
+                        Chem.MolToSmiles(
+                            each_mol))))
+        except:
+            smiles_list.append(None)
+    return smiles_list
+
+def atom_attr(atom, kekulize):
+    """
+    get atom's attributes
+
+    Parameters
+    ----------
+    atom : rdkit.Chem.rdchem.Atom
+    kekulize : bool
+        kekulize or not
+
+    Returns
+    -------
+    atom_attr : dict
+        "is_aromatic" : bool
+            the atom is aromatic or not.
+        "smarts" : str
+            SMARTS representation of the atom.
+    """
+    if kekulize:
+        return {'terminal': True,
+                'is_in_ring': atom.IsInRing(),
+                'symbol': TSymbol(degree=0,
+                                  #degree=atom.GetTotalDegree(),
+                                  is_aromatic=False,
+                                  symbol=atom.GetSymbol(),
+                                  num_explicit_Hs=atom.GetNumExplicitHs(),
+                                  formal_charge=atom.GetFormalCharge(),
+                                  chirality=atom.GetChiralTag().real
+                )}
+    else:
+        return {'terminal': True,
+                'is_in_ring': atom.IsInRing(),
+                'symbol': TSymbol(degree=0,
+                                  #degree=atom.GetTotalDegree(),
+                                  is_aromatic=atom.GetIsAromatic(),
+                                  symbol=atom.GetSymbol(),
+                                  num_explicit_Hs=atom.GetNumExplicitHs(),
+                                  formal_charge=atom.GetFormalCharge(),
+                                  chirality=atom.GetChiralTag().real
+                )}
+
+def bond_attr(bond, kekulize):
+    """
+    get atom's attributes
+
+    Parameters
+    ----------
+    bond : rdkit.Chem.rdchem.Bond
+    kekulize : bool
+        kekulize or not
+
+    Returns
+    -------
+    bond_attr : dict
+        "bond_type" : int
+        {0: rdkit.Chem.rdchem.BondType.UNSPECIFIED,
+         1: rdkit.Chem.rdchem.BondType.SINGLE,
+         2: rdkit.Chem.rdchem.BondType.DOUBLE,
+         3: rdkit.Chem.rdchem.BondType.TRIPLE,
+         4: rdkit.Chem.rdchem.BondType.QUADRUPLE,
+         5: rdkit.Chem.rdchem.BondType.QUINTUPLE,
+         6: rdkit.Chem.rdchem.BondType.HEXTUPLE,
+         7: rdkit.Chem.rdchem.BondType.ONEANDAHALF,
+         8: rdkit.Chem.rdchem.BondType.TWOANDAHALF,
+         9: rdkit.Chem.rdchem.BondType.THREEANDAHALF,
+         10: rdkit.Chem.rdchem.BondType.FOURANDAHALF,
+         11: rdkit.Chem.rdchem.BondType.FIVEANDAHALF,
+         12: rdkit.Chem.rdchem.BondType.AROMATIC,
+         13: rdkit.Chem.rdchem.BondType.IONIC,
+         14: rdkit.Chem.rdchem.BondType.HYDROGEN,
+         15: rdkit.Chem.rdchem.BondType.THREECENTER,
+         16: rdkit.Chem.rdchem.BondType.DATIVEONE,
+         17: rdkit.Chem.rdchem.BondType.DATIVE,
+         18: rdkit.Chem.rdchem.BondType.DATIVEL,
+         19: rdkit.Chem.rdchem.BondType.DATIVER,
+         20: rdkit.Chem.rdchem.BondType.OTHER,
+         21: rdkit.Chem.rdchem.BondType.ZERO}
+    """
+    if kekulize:
+        is_aromatic = False
+        if bond.GetBondType().real == 12:
+            bond_type = 1
+        else:
+            bond_type = bond.GetBondType().real
+    else:
+        is_aromatic = bond.GetIsAromatic()
+        bond_type = bond.GetBondType().real
+    return {'symbol': BondSymbol(is_aromatic=is_aromatic,
+                                 bond_type=bond_type,
+                                 stereo=int(bond.GetStereo())),
+            'is_in_ring': bond.IsInRing()}
+
+
+def standardize_stereo(mol):
+    '''
+ 0: rdkit.Chem.rdchem.BondDir.NONE,
+ 1: rdkit.Chem.rdchem.BondDir.BEGINWEDGE,
+ 2: rdkit.Chem.rdchem.BondDir.BEGINDASH,
+ 3: rdkit.Chem.rdchem.BondDir.ENDDOWNRIGHT,
+ 4: rdkit.Chem.rdchem.BondDir.ENDUPRIGHT,
+
+    '''
+    # mol = Chem.AddHs(mol) # this removes CIPRank !!!
+    for each_bond in mol.GetBonds():
+        if int(each_bond.GetStereo()) in [2, 3]: #2=Z (same side), 3=E
+            begin_stereo_atom_idx = each_bond.GetBeginAtomIdx()
+            end_stereo_atom_idx = each_bond.GetEndAtomIdx()
+            atom_idx_1 = each_bond.GetStereoAtoms()[0]
+            atom_idx_2 = each_bond.GetStereoAtoms()[1]
+            if mol.GetBondBetweenAtoms(atom_idx_1, begin_stereo_atom_idx):
+                begin_atom_idx = atom_idx_1
+                end_atom_idx = atom_idx_2
+            else:
+                begin_atom_idx = atom_idx_2
+                end_atom_idx = atom_idx_1
+
+            begin_another_atom_idx = None
+            assert len(mol.GetAtomWithIdx(begin_stereo_atom_idx).GetNeighbors()) <= 3
+            for each_neighbor in mol.GetAtomWithIdx(begin_stereo_atom_idx).GetNeighbors():
+                each_neighbor_idx = each_neighbor.GetIdx()
+                if each_neighbor_idx not in [end_stereo_atom_idx, begin_atom_idx]:
+                    begin_another_atom_idx = each_neighbor_idx
+
+            end_another_atom_idx = None
+            assert len(mol.GetAtomWithIdx(end_stereo_atom_idx).GetNeighbors()) <= 3
+            for each_neighbor in mol.GetAtomWithIdx(end_stereo_atom_idx).GetNeighbors():
+                each_neighbor_idx = each_neighbor.GetIdx()
+                if each_neighbor_idx not in [begin_stereo_atom_idx, end_atom_idx]:
+                    end_another_atom_idx = each_neighbor_idx
+
+            ''' 
+            relationship between begin_atom_idx and end_atom_idx is encoded in GetStereo
+            '''
+            begin_atom_rank = int(mol.GetAtomWithIdx(begin_atom_idx).GetProp('_CIPRank'))
+            end_atom_rank = int(mol.GetAtomWithIdx(end_atom_idx).GetProp('_CIPRank'))
+            try:
+                begin_another_atom_rank = int(mol.GetAtomWithIdx(begin_another_atom_idx).GetProp('_CIPRank'))
+            except:
+                begin_another_atom_rank = np.inf
+            try:
+                end_another_atom_rank = int(mol.GetAtomWithIdx(end_another_atom_idx).GetProp('_CIPRank'))
+            except:
+                end_another_atom_rank = np.inf
+            if begin_atom_rank < begin_another_atom_rank\
+               and end_atom_rank < end_another_atom_rank:
+                pass
+            elif begin_atom_rank < begin_another_atom_rank\
+                 and end_atom_rank > end_another_atom_rank:
+                # (begin_atom_idx +) end_another_atom_idx should be in StereoAtoms
+                if each_bond.GetStereo() == 2:
+                    # set stereo
+                    each_bond.SetStereo(Chem.rdchem.BondStereo.values[3])
+                    # set bond dir
+                    mol = safe_set_bond_dir(mol, begin_atom_idx, begin_stereo_atom_idx, 3)
+                    mol = safe_set_bond_dir(mol, begin_another_atom_idx, begin_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, end_atom_idx, end_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, end_another_atom_idx, end_stereo_atom_idx, 3)
+                elif each_bond.GetStereo() == 3:
+                    # set stereo
+                    each_bond.SetStereo(Chem.rdchem.BondStereo.values[2])
+                    # set bond dir
+                    mol = safe_set_bond_dir(mol, begin_atom_idx, begin_stereo_atom_idx, 3)
+                    mol = safe_set_bond_dir(mol, begin_another_atom_idx, begin_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, end_atom_idx, end_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, end_another_atom_idx, end_stereo_atom_idx, 4)
+                else:
+                    raise ValueError
+                each_bond.SetStereoAtoms(begin_atom_idx, end_another_atom_idx)
+            elif begin_atom_rank > begin_another_atom_rank\
+                 and end_atom_rank < end_another_atom_rank:
+                # (end_atom_idx +) begin_another_atom_idx should be in StereoAtoms
+                if each_bond.GetStereo() == 2:
+                    # set stereo
+                    each_bond.SetStereo(Chem.rdchem.BondStereo.values[3])
+                    # set bond dir
+                    mol = safe_set_bond_dir(mol, begin_atom_idx, begin_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, begin_another_atom_idx, begin_stereo_atom_idx, 4)
+                    mol = safe_set_bond_dir(mol, end_atom_idx, end_stereo_atom_idx, 4)
+                    mol = safe_set_bond_dir(mol, end_another_atom_idx, end_stereo_atom_idx, 0)
+                elif each_bond.GetStereo() == 3:
+                    # set stereo
+                    each_bond.SetStereo(Chem.rdchem.BondStereo.values[2])
+                    # set bond dir
+                    mol = safe_set_bond_dir(mol, begin_atom_idx, begin_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, begin_another_atom_idx, begin_stereo_atom_idx, 4)
+                    mol = safe_set_bond_dir(mol, end_atom_idx, end_stereo_atom_idx, 3)
+                    mol = safe_set_bond_dir(mol, end_another_atom_idx, end_stereo_atom_idx, 0)
+                else:
+                    raise ValueError
+                each_bond.SetStereoAtoms(begin_another_atom_idx, end_atom_idx)
+            elif begin_atom_rank > begin_another_atom_rank\
+                 and end_atom_rank > end_another_atom_rank:
+                # begin_another_atom_idx + end_another_atom_idx should be in StereoAtoms
+                if each_bond.GetStereo() == 2:
+                    # set bond dir
+                    mol = safe_set_bond_dir(mol, begin_atom_idx, begin_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, begin_another_atom_idx, begin_stereo_atom_idx, 4)
+                    mol = safe_set_bond_dir(mol, end_atom_idx, end_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, end_another_atom_idx, end_stereo_atom_idx, 3)
+                elif each_bond.GetStereo() == 3:
+                    # set bond dir
+                    mol = safe_set_bond_dir(mol, begin_atom_idx, begin_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, begin_another_atom_idx, begin_stereo_atom_idx, 4)
+                    mol = safe_set_bond_dir(mol, end_atom_idx, end_stereo_atom_idx, 0)
+                    mol = safe_set_bond_dir(mol, end_another_atom_idx, end_stereo_atom_idx, 4)
+                else:
+                    raise ValueError
+                each_bond.SetStereoAtoms(begin_another_atom_idx, end_another_atom_idx)
+            else:
+                raise RuntimeError
+    return mol
+
+
+def set_stereo(mol):
+    '''
+ 0: rdkit.Chem.rdchem.BondDir.NONE,
+ 1: rdkit.Chem.rdchem.BondDir.BEGINWEDGE,
+ 2: rdkit.Chem.rdchem.BondDir.BEGINDASH,
+ 3: rdkit.Chem.rdchem.BondDir.ENDDOWNRIGHT,
+ 4: rdkit.Chem.rdchem.BondDir.ENDUPRIGHT,
+    '''
+    _mol = Chem.MolFromSmiles(Chem.MolToSmiles(mol))
+    Chem.Kekulize(_mol, True)
+    substruct_match = mol.GetSubstructMatch(_mol)
+    if not substruct_match:
+        ''' mol and _mol are kekulized.
+        sometimes, the order of '=' and '-' changes, which causes mol and _mol not matched.
+        '''
+        Chem.SetAromaticity(mol)
+        Chem.SetAromaticity(_mol)
+        substruct_match = mol.GetSubstructMatch(_mol)
+    try:
+        atom_match = {substruct_match[_mol_atom_idx]: _mol_atom_idx for _mol_atom_idx in range(_mol.GetNumAtoms())} # mol to _mol
+    except:
+        raise ValueError('two molecules obtained from the same data do not match.')
+        
+    for each_bond in mol.GetBonds():
+        begin_atom_idx = each_bond.GetBeginAtomIdx()
+        end_atom_idx = each_bond.GetEndAtomIdx()
+        _bond = _mol.GetBondBetweenAtoms(atom_match[begin_atom_idx], atom_match[end_atom_idx])
+        _bond.SetStereo(each_bond.GetStereo())
+
+    mol = _mol
+    for each_bond in mol.GetBonds():
+        if int(each_bond.GetStereo()) in [2, 3]: #2=Z (same side), 3=E
+            begin_stereo_atom_idx = each_bond.GetBeginAtomIdx()
+            end_stereo_atom_idx = each_bond.GetEndAtomIdx()
+            begin_atom_idx_set = set([each_neighbor.GetIdx()
+                                      for each_neighbor
+                                      in mol.GetAtomWithIdx(begin_stereo_atom_idx).GetNeighbors()
+                                      if each_neighbor.GetIdx() != end_stereo_atom_idx])
+            end_atom_idx_set = set([each_neighbor.GetIdx()
+                                    for each_neighbor
+                                    in mol.GetAtomWithIdx(end_stereo_atom_idx).GetNeighbors()
+                                    if each_neighbor.GetIdx() != begin_stereo_atom_idx])
+            if not begin_atom_idx_set:
+                each_bond.SetStereo(Chem.rdchem.BondStereo(0))
+                continue
+            if not end_atom_idx_set:
+                each_bond.SetStereo(Chem.rdchem.BondStereo(0))
+                continue
+            if len(begin_atom_idx_set) == 1:
+                begin_atom_idx = begin_atom_idx_set.pop()
+                begin_another_atom_idx = None
+            if len(end_atom_idx_set) == 1:
+                end_atom_idx = end_atom_idx_set.pop()
+                end_another_atom_idx = None
+            if len(begin_atom_idx_set) == 2:
+                atom_idx_1 = begin_atom_idx_set.pop()
+                atom_idx_2 = begin_atom_idx_set.pop()
+                if int(mol.GetAtomWithIdx(atom_idx_1).GetProp('_CIPRank')) < int(mol.GetAtomWithIdx(atom_idx_2).GetProp('_CIPRank')):
+                    begin_atom_idx = atom_idx_1
+                    begin_another_atom_idx = atom_idx_2
+                else:
+                    begin_atom_idx = atom_idx_2
+                    begin_another_atom_idx = atom_idx_1
+            if len(end_atom_idx_set) == 2:
+                atom_idx_1 = end_atom_idx_set.pop()
+                atom_idx_2 = end_atom_idx_set.pop()
+                if int(mol.GetAtomWithIdx(atom_idx_1).GetProp('_CIPRank')) < int(mol.GetAtomWithIdx(atom_idx_2).GetProp('_CIPRank')):
+                    end_atom_idx = atom_idx_1
+                    end_another_atom_idx = atom_idx_2
+                else:
+                    end_atom_idx = atom_idx_2
+                    end_another_atom_idx = atom_idx_1
+
+            if each_bond.GetStereo() == 2: # same side
+                mol = safe_set_bond_dir(mol, begin_atom_idx, begin_stereo_atom_idx, 3)
+                mol = safe_set_bond_dir(mol, end_atom_idx, end_stereo_atom_idx, 4)
+                each_bond.SetStereoAtoms(begin_atom_idx, end_atom_idx)
+            elif each_bond.GetStereo() == 3: # opposite side
+                mol = safe_set_bond_dir(mol, begin_atom_idx, begin_stereo_atom_idx, 3)
+                mol = safe_set_bond_dir(mol, end_atom_idx, end_stereo_atom_idx, 3)
+                each_bond.SetStereoAtoms(begin_atom_idx, end_atom_idx)
+            else:
+                raise ValueError
+    return mol
+
+
+def safe_set_bond_dir(mol, atom_idx_1, atom_idx_2, bond_dir_val):
+    if atom_idx_1 is None or atom_idx_2 is None:
+        return mol
+    else:
+        mol.GetBondBetweenAtoms(atom_idx_1, atom_idx_2).SetBondDir(Chem.rdchem.BondDir.values[bond_dir_val])
+        return mol
+        
diff --git a/models/mhg_model/graph_grammar/nn/__init__.py b/models/mhg_model/graph_grammar/nn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2912d326a2ed386143a635e5dc674b3ea7ce01f
--- /dev/null
+++ b/models/mhg_model/graph_grammar/nn/__init__.py
@@ -0,0 +1,11 @@
+# -*- coding:utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
diff --git a/models/mhg_model/graph_grammar/nn/__pycache__/__init__.cpython-310.pyc b/models/mhg_model/graph_grammar/nn/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..618622c9a7febe8e703163fd725ead466ebbf71e
Binary files /dev/null and b/models/mhg_model/graph_grammar/nn/__pycache__/__init__.cpython-310.pyc differ
diff --git a/models/mhg_model/graph_grammar/nn/__pycache__/decoder.cpython-310.pyc b/models/mhg_model/graph_grammar/nn/__pycache__/decoder.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bec0cee5b282fd3f69c8ceb1487d82882431374d
Binary files /dev/null and b/models/mhg_model/graph_grammar/nn/__pycache__/decoder.cpython-310.pyc differ
diff --git a/models/mhg_model/graph_grammar/nn/__pycache__/encoder.cpython-310.pyc b/models/mhg_model/graph_grammar/nn/__pycache__/encoder.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0a0770e581b2b96bbca34242c839baa164490280
Binary files /dev/null and b/models/mhg_model/graph_grammar/nn/__pycache__/encoder.cpython-310.pyc differ
diff --git a/models/mhg_model/graph_grammar/nn/dataset.py b/models/mhg_model/graph_grammar/nn/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..70894b289d6413dd29c81dfd3934574c6edb1383
--- /dev/null
+++ b/models/mhg_model/graph_grammar/nn/dataset.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Apr 18 2018"
+
+from torch.utils.data import Dataset, DataLoader
+import torch
+import numpy as np
+
+
+def left_padding(sentence_list, max_len, pad_idx=-1, inverse=False):
+    ''' pad left
+
+    Parameters
+    ----------
+    sentence_list : list of sequences of integers
+    max_len : int
+        maximum length of sentences.
+        if a sentence is shorter than `max_len`, its left part is padded.
+    pad_idx : int
+        integer for padding
+    inverse : bool
+        if True, the sequence is inversed.
+
+    Returns
+    -------
+    List of torch.LongTensor
+        each sentence is left-padded.
+    '''
+    max_in_list = max([len(each_sen) for each_sen in sentence_list])
+        
+    if max_in_list > max_len:
+        raise ValueError('`max_len` should be larger than the maximum length of input sequences, {}.'.format(max_in_list))
+
+    if inverse:
+        return [torch.LongTensor([pad_idx] * (max_len - len(each_sen)) + each_sen[::-1]) for each_sen in sentence_list]
+    else:
+        return [torch.LongTensor([pad_idx] * (max_len - len(each_sen)) + each_sen) for each_sen in sentence_list]
+
+
+def right_padding(sentence_list, max_len, pad_idx=-1):
+    ''' pad right
+
+    Parameters
+    ----------
+    sentence_list : list of sequences of integers
+    max_len : int
+        maximum length of sentences.
+        if a sentence is shorter than `max_len`, its right part is padded.
+    pad_idx : int
+        integer for padding
+
+    Returns
+    -------
+    List of torch.LongTensor
+        each sentence is right-padded.
+    '''
+    max_in_list = max([len(each_sen) for each_sen in sentence_list])
+    if max_in_list > max_len:
+        raise ValueError('`max_len` should be larger than the maximum length of input sequences, {}.'.format(max_in_list))
+
+    return [torch.LongTensor(each_sen + [pad_idx] * (max_len - len(each_sen))) for each_sen in sentence_list]
+
+
+class HRGDataset(Dataset):
+
+    '''
+    A class of HRG data
+    '''
+
+    def __init__(self, hrg, prod_rule_seq_list, max_len, target_val_list=None, inversed_input=False):
+        self.hrg = hrg
+        self.left_prod_rule_seq_list = left_padding(prod_rule_seq_list,
+                                                    max_len,
+                                                    inverse=inversed_input)
+            
+        self.right_prod_rule_seq_list = right_padding(prod_rule_seq_list, max_len)
+        self.inserved_input = inversed_input
+        self.target_val_list = target_val_list
+        if target_val_list is not None:
+            if len(prod_rule_seq_list) != len(target_val_list):
+                raise ValueError(f'prod_rule_seq_list and target_val_list have inconsistent lengths: {len(prod_rule_seq_list)}, {len(target_val_list)}')
+
+    def __len__(self):
+        return len(self.left_prod_rule_seq_list)
+
+    def __getitem__(self, idx):
+        if self.target_val_list is not None:
+            return self.left_prod_rule_seq_list[idx], self.right_prod_rule_seq_list[idx], np.float32(self.target_val_list[idx])
+        else:
+            return self.left_prod_rule_seq_list[idx], self.right_prod_rule_seq_list[idx]
+
+    @property
+    def vocab_size(self):
+        return self.hrg.num_prod_rule
+
+def batch_padding(each_batch, batch_size, padding_idx):
+    num_pad = batch_size - len(each_batch[0])
+    if num_pad:
+        each_batch[0] = torch.cat([each_batch[0],
+                                   padding_idx * torch.ones((batch_size - len(each_batch[0]),
+                                                             len(each_batch[0][0])), dtype=torch.int64)], dim=0)
+        each_batch[1] = torch.cat([each_batch[1],
+                                   padding_idx * torch.ones((batch_size - len(each_batch[1]),
+                                                             len(each_batch[1][0])), dtype=torch.int64)], dim=0)
+    return each_batch, num_pad
diff --git a/models/mhg_model/graph_grammar/nn/decoder.py b/models/mhg_model/graph_grammar/nn/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3b9c9d69e7077650355e1df201b18420de55471
--- /dev/null
+++ b/models/mhg_model/graph_grammar/nn/decoder.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Aug 9 2018"
+
+
+import abc
+import numpy as np
+import torch
+from torch import nn
+
+
+class DecoderBase(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.hidden_dict = {}
+
+    @abc.abstractmethod
+    def forward_one_step(self, tgt_emb_in):
+        ''' one-step forward model
+
+        Parameters
+        ----------
+        tgt_emb_in : Tensor, shape (batch_size, input_dim)
+
+        Returns
+        -------
+        Tensor, shape (batch_size, hidden_dim)
+        '''
+        tgt_emb_out = None
+        return tgt_emb_out
+
+    @abc.abstractmethod
+    def init_hidden(self):
+        ''' initialize the hidden states
+        '''
+        pass
+
+    @abc.abstractmethod
+    def feed_hidden(self, hidden_dict_0):
+        for each_hidden in self.hidden_dict.keys():
+            self.hidden_dict[each_hidden][0] = hidden_dict_0[each_hidden]
+
+
+class GRUDecoder(DecoderBase):
+
+    def __init__(self, input_dim: int, hidden_dim: int, num_layers: int,
+                 dropout: float, batch_size: int, use_gpu: bool,
+                 no_dropout=False):
+        super().__init__()
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.batch_size = batch_size
+        self.use_gpu = use_gpu
+        self.model = nn.GRU(input_size=self.input_dim,
+                            hidden_size=self.hidden_dim,
+                            num_layers=self.num_layers,
+                            batch_first=True,
+                            bidirectional=False,
+                            dropout=self.dropout if not no_dropout else 0
+        )
+        if self.use_gpu:
+            self.model.cuda()
+        self.init_hidden()
+
+    def init_hidden(self):
+        self.hidden_dict['h'] = torch.zeros((self.num_layers,
+                                             self.batch_size,
+                                             self.hidden_dim),
+                                            requires_grad=False)
+        if self.use_gpu:
+            self.hidden_dict['h'] = self.hidden_dict['h'].cuda()
+
+    def forward_one_step(self, tgt_emb_in):
+        ''' one-step forward model
+
+        Parameters
+        ----------
+        tgt_emb_in : Tensor, shape (batch_size, input_dim)
+
+        Returns
+        -------
+        Tensor, shape (batch_size, hidden_dim)
+        '''
+        tgt_emb_out, self.hidden_dict['h'] \
+            = self.model(tgt_emb_in.view(self.batch_size, 1, -1),
+                         self.hidden_dict['h'])
+        return tgt_emb_out
+
+
+class LSTMDecoder(DecoderBase):
+
+    def __init__(self, input_dim: int, hidden_dim: int, num_layers: int,
+                 dropout: float, batch_size: int, use_gpu: bool,
+                 no_dropout=False):
+        super().__init__()
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.batch_size = batch_size
+        self.use_gpu = use_gpu
+        self.model = nn.LSTM(input_size=self.input_dim,
+                             hidden_size=self.hidden_dim,
+                             num_layers=self.num_layers,
+                             batch_first=True,
+                             bidirectional=False,
+                             dropout=self.dropout if not no_dropout else 0)
+        if self.use_gpu:
+            self.model.cuda()
+        self.init_hidden()
+
+    def init_hidden(self):
+        self.hidden_dict['h'] = torch.zeros((self.num_layers,
+                                             self.batch_size,
+                                             self.hidden_dim),
+                                            requires_grad=False)
+        self.hidden_dict['c'] = torch.zeros((self.num_layers,
+                                             self.batch_size,
+                                             self.hidden_dim),
+                                            requires_grad=False)
+        if self.use_gpu:
+            for each_hidden in self.hidden_dict.keys():
+                self.hidden_dict[each_hidden] = self.hidden_dict[each_hidden].cuda()
+
+    def forward_one_step(self, tgt_emb_in):
+        ''' one-step forward model
+
+        Parameters
+        ----------
+        tgt_emb_in : Tensor, shape (batch_size, input_dim)
+
+        Returns
+        -------
+        Tensor, shape (batch_size, hidden_dim)
+        '''
+        tgt_hidden_out, self.hidden_dict['h'], self.hidden_dict['c'] \
+            = self.model(tgt_emb_in.view(self.batch_size, 1, -1),
+                         self.hidden_dict['h'], self.hidden_dict['c'])
+        return tgt_hidden_out
diff --git a/models/mhg_model/graph_grammar/nn/encoder.py b/models/mhg_model/graph_grammar/nn/encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a59226d9980479503ed482be26976dd4917b9953
--- /dev/null
+++ b/models/mhg_model/graph_grammar/nn/encoder.py
@@ -0,0 +1,199 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Aug 9 2018"
+
+
+import abc
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from typing import List
+
+
+class EncoderBase(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @abc.abstractmethod
+    def forward(self, in_seq):
+        ''' forward model
+
+        Parameters
+        ----------
+        in_seq_emb : Variable, shape (batch_size, max_len, input_dim)
+
+        Returns
+        -------
+        hidden_seq_emb : Tensor, shape (batch_size, max_len, 1 + bidirectional, hidden_dim)
+        '''
+        pass
+
+    @abc.abstractmethod
+    def init_hidden(self):
+        ''' initialize the hidden states
+        '''
+        pass
+
+
+class GRUEncoder(EncoderBase):
+
+    def __init__(self, input_dim: int, hidden_dim: int, num_layers: int,
+                 bidirectional: bool, dropout: float, batch_size: int, use_gpu: bool,
+                 no_dropout=False):
+        super().__init__()
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.num_layers = num_layers
+        self.bidirectional = bidirectional
+        self.dropout = dropout
+        self.batch_size = batch_size
+        self.use_gpu = use_gpu
+        self.model = nn.GRU(input_size=self.input_dim,
+                            hidden_size=self.hidden_dim,
+                            num_layers=self.num_layers,
+                            batch_first=True,
+                            bidirectional=self.bidirectional,
+                            dropout=self.dropout if not no_dropout else 0)
+        if self.use_gpu:
+            self.model.cuda()
+        self.init_hidden()
+
+
+    def init_hidden(self):
+        self.h0 = torch.zeros(((self.bidirectional + 1) * self.num_layers,
+                               self.batch_size,
+                               self.hidden_dim),
+                              requires_grad=False)
+        if self.use_gpu:        
+            self.h0 = self.h0.cuda()
+
+    def forward(self, in_seq_emb):
+        ''' forward model
+
+        Parameters
+        ----------
+        in_seq_emb : Tensor, shape (batch_size, max_len, input_dim)
+
+        Returns
+        -------
+        hidden_seq_emb : Tensor, shape (batch_size, max_len, 1 + bidirectional, hidden_dim)
+        '''
+        max_len = in_seq_emb.size(1)
+        hidden_seq_emb, self.h0 = self.model(
+            in_seq_emb, self.h0)
+        hidden_seq_emb = hidden_seq_emb.view(self.batch_size,
+                                             max_len,
+                                             1 + self.bidirectional,
+                                             self.hidden_dim)
+        return hidden_seq_emb
+
+
+class LSTMEncoder(EncoderBase):
+
+    def __init__(self, input_dim: int, hidden_dim: int, num_layers: int,
+                 bidirectional: bool, dropout: float, batch_size: int, use_gpu: bool,
+                 no_dropout=False):
+        super().__init__()
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.num_layers = num_layers
+        self.bidirectional = bidirectional
+        self.dropout = dropout
+        self.batch_size = batch_size
+        self.use_gpu = use_gpu
+        self.model = nn.LSTM(input_size=self.input_dim,
+                             hidden_size=self.hidden_dim,
+                             num_layers=self.num_layers,
+                             batch_first=True,
+                             bidirectional=self.bidirectional,
+                             dropout=self.dropout if not no_dropout else 0)
+        if self.use_gpu:
+            self.model.cuda()
+        self.init_hidden()
+
+    def init_hidden(self):
+        self.h0 = torch.zeros(((self.bidirectional + 1) * self.num_layers,
+                               self.batch_size,
+                               self.hidden_dim),
+                              requires_grad=False)
+        self.c0 = torch.zeros(((self.bidirectional + 1) * self.num_layers,
+                               self.batch_size,
+                               self.hidden_dim),
+                              requires_grad=False)
+        if self.use_gpu:        
+            self.h0 = self.h0.cuda()
+            self.c0 = self.c0.cuda()
+
+    def forward(self, in_seq_emb):
+        ''' forward model
+
+        Parameters
+        ----------
+        in_seq_emb : Tensor, shape (batch_size, max_len, input_dim)
+
+        Returns
+        -------
+        hidden_seq_emb : Tensor, shape (batch_size, max_len, 1 + bidirectional, hidden_dim)
+        '''
+        max_len = in_seq_emb.size(1)
+        hidden_seq_emb, (self.h0, self.c0) = self.model(
+            in_seq_emb, (self.h0, self.c0))
+        hidden_seq_emb = hidden_seq_emb.view(self.batch_size,
+                                             max_len,
+                                             1 + self.bidirectional,
+                                             self.hidden_dim)
+        return hidden_seq_emb
+
+
+class FullConnectedEncoder(EncoderBase):
+
+    def __init__(self, input_dim: int, hidden_dim: int, max_len: int, hidden_dim_list: List[int],
+                 batch_size: int, use_gpu: bool):
+        super().__init__()
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.max_len = max_len
+        self.hidden_dim_list = hidden_dim_list
+        self.use_gpu = use_gpu
+        in_out_dim_list = [input_dim * max_len] + list(hidden_dim_list) + [hidden_dim]
+        self.linear_list = nn.ModuleList(
+            [nn.Linear(in_out_dim_list[each_idx], in_out_dim_list[each_idx + 1])\
+             for each_idx in range(len(in_out_dim_list) - 1)])
+
+    def forward(self, in_seq_emb):
+        ''' forward model
+
+        Parameters
+        ----------
+        in_seq_emb : Tensor, shape (batch_size, max_len, input_dim)
+
+        Returns
+        -------
+        hidden_seq_emb : Tensor, shape (batch_size, max_len, 1 + bidirectional, hidden_dim)
+        '''
+        batch_size = in_seq_emb.size(0)
+        x = in_seq_emb.view(batch_size, -1)
+        for each_linear in self.linear_list:
+            x = F.relu(each_linear(x))
+        return x.view(batch_size, 1, -1)
+
+    def init_hidden(self):
+        pass
diff --git a/models/mhg_model/graph_grammar/nn/graph.py b/models/mhg_model/graph_grammar/nn/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2da699b2000ddf81c856c2c636f8ccdb864c81c
--- /dev/null
+++ b/models/mhg_model/graph_grammar/nn/graph.py
@@ -0,0 +1,313 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. 
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+""" Title """
+
+__author__ = "Hiroshi Kajino <KAJINO@jp.ibm.com>"
+__copyright__ = "(c) Copyright IBM Corp. 2018"
+__version__ = "0.1"
+__date__ = "Jan 1 2018"
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from graph_grammar.graph_grammar.hrg import ProductionRuleCorpus
+from torch import nn
+from torch.autograd import Variable
+
+class MolecularProdRuleEmbedding(nn.Module):
+    
+    ''' molecular fingerprint layer
+    '''
+
+    def __init__(self, prod_rule_corpus, layer2layer_activation, layer2out_activation,
+                 out_dim=32, element_embed_dim=32,
+                 num_layers=3, padding_idx=None, use_gpu=False):
+        super().__init__()
+        if padding_idx is not None:
+            assert padding_idx == -1, 'padding_idx must be -1.'
+        self.prod_rule_corpus = prod_rule_corpus
+        self.layer2layer_activation = layer2layer_activation
+        self.layer2out_activation = layer2out_activation
+        self.out_dim = out_dim
+        self.element_embed_dim = element_embed_dim
+        self.num_layers = num_layers
+        self.padding_idx = padding_idx
+        self.use_gpu = use_gpu
+
+        self.layer2layer_list = []
+        self.layer2out_list = []
+
+        if self.use_gpu:
+            self.atom_embed = torch.randn(self.prod_rule_corpus.num_edge_symbol,
+                                          self.element_embed_dim, requires_grad=True).cuda()
+            self.bond_embed = torch.randn(self.prod_rule_corpus.num_node_symbol,
+                                          self.element_embed_dim, requires_grad=True).cuda()
+            self.ext_id_embed = torch.randn(self.prod_rule_corpus.num_ext_id,
+                                            self.element_embed_dim, requires_grad=True).cuda()
+            for _ in range(num_layers):
+                self.layer2layer_list.append(nn.Linear(self.element_embed_dim, self.element_embed_dim).cuda())
+                self.layer2out_list.append(nn.Linear(self.element_embed_dim, self.out_dim).cuda())
+        else:
+            self.atom_embed = torch.randn(self.prod_rule_corpus.num_edge_symbol,
+                                          self.element_embed_dim, requires_grad=True)
+            self.bond_embed = torch.randn(self.prod_rule_corpus.num_node_symbol,
+                                          self.element_embed_dim, requires_grad=True)
+            self.ext_id_embed = torch.randn(self.prod_rule_corpus.num_ext_id,
+                                            self.element_embed_dim, requires_grad=True)
+            for _ in range(num_layers):
+                self.layer2layer_list.append(nn.Linear(self.element_embed_dim, self.element_embed_dim))
+                self.layer2out_list.append(nn.Linear(self.element_embed_dim, self.out_dim))
+
+
+    def forward(self, prod_rule_idx_seq):
+        ''' forward model for mini-batch
+
+        Parameters
+        ----------
+        prod_rule_idx_seq : (batch_size, length)
+
+        Returns
+        -------
+        Variable, shape (batch_size, length, out_dim)
+        '''
+        batch_size, length = prod_rule_idx_seq.shape
+        if self.use_gpu:
+            out = Variable(torch.zeros((batch_size, length, self.out_dim))).cuda()
+        else:
+            out = Variable(torch.zeros((batch_size, length, self.out_dim)))
+        for each_batch_idx in range(batch_size):
+            for each_idx in range(length):
+                if int(prod_rule_idx_seq[each_batch_idx, each_idx]) == len(self.prod_rule_corpus.prod_rule_list):
+                    continue
+                else:
+                    each_prod_rule = self.prod_rule_corpus.prod_rule_list[int(prod_rule_idx_seq[each_batch_idx, each_idx])]
+                    layer_wise_embed_dict = {each_edge: self.atom_embed[
+                        each_prod_rule.rhs.edge_attr(each_edge)['symbol_idx']]
+                                             for each_edge in each_prod_rule.rhs.edges}
+                    layer_wise_embed_dict.update({each_node: self.bond_embed[
+                        each_prod_rule.rhs.node_attr(each_node)['symbol_idx']]
+                                                  for each_node in each_prod_rule.rhs.nodes})
+                    for each_node in each_prod_rule.rhs.nodes:
+                        if 'ext_id' in each_prod_rule.rhs.node_attr(each_node):
+                            layer_wise_embed_dict[each_node] \
+                                = layer_wise_embed_dict[each_node] \
+                                + self.ext_id_embed[each_prod_rule.rhs.node_attr(each_node)['ext_id']]
+
+                    for each_layer in range(self.num_layers):
+                        next_layer_embed_dict = {}
+                        for each_edge in each_prod_rule.rhs.edges:
+                            v = layer_wise_embed_dict[each_edge]
+                            for each_node in each_prod_rule.rhs.nodes_in_edge(each_edge):
+                                v = v + layer_wise_embed_dict[each_node]
+                            next_layer_embed_dict[each_edge] = self.layer2layer_activation(self.layer2layer_list[each_layer](v))
+                            out[each_batch_idx, each_idx, :] \
+                                = out[each_batch_idx, each_idx, :] + self.layer2out_activation(self.layer2out_list[each_layer](v))
+                        for each_node in each_prod_rule.rhs.nodes:
+                            v = layer_wise_embed_dict[each_node]
+                            for each_edge in each_prod_rule.rhs.adj_edges(each_node):
+                                v = v + layer_wise_embed_dict[each_edge]
+                            next_layer_embed_dict[each_node] = self.layer2layer_activation(self.layer2layer_list[each_layer](v))
+                            out[each_batch_idx, each_idx, :]\
+                                = out[each_batch_idx, each_idx, :] + self.layer2out_activation(self.layer2out_list[each_layer](v))
+                        layer_wise_embed_dict = next_layer_embed_dict
+                        
+        return out
+
+
+class MolecularProdRuleEmbeddingLastLayer(nn.Module):
+    
+    ''' molecular fingerprint layer
+    '''
+
+    def __init__(self, prod_rule_corpus, layer2layer_activation, layer2out_activation,
+                 out_dim=32, element_embed_dim=32,
+                 num_layers=3, padding_idx=None, use_gpu=False):
+        super().__init__()
+        if padding_idx is not None:
+            assert padding_idx == -1, 'padding_idx must be -1.'
+        self.prod_rule_corpus = prod_rule_corpus
+        self.layer2layer_activation = layer2layer_activation
+        self.layer2out_activation = layer2out_activation
+        self.out_dim = out_dim
+        self.element_embed_dim = element_embed_dim
+        self.num_layers = num_layers
+        self.padding_idx = padding_idx
+        self.use_gpu = use_gpu
+
+        self.layer2layer_list = []
+        self.layer2out_list = []
+
+        if self.use_gpu:
+            self.atom_embed = nn.Embedding(self.prod_rule_corpus.num_edge_symbol, self.element_embed_dim).cuda()
+            self.bond_embed = nn.Embedding(self.prod_rule_corpus.num_node_symbol, self.element_embed_dim).cuda()
+            for _ in range(num_layers+1):
+                self.layer2layer_list.append(nn.Linear(self.element_embed_dim, self.element_embed_dim).cuda())
+                self.layer2out_list.append(nn.Linear(self.element_embed_dim, self.out_dim).cuda())
+        else:
+            self.atom_embed = nn.Embedding(self.prod_rule_corpus.num_edge_symbol, self.element_embed_dim)
+            self.bond_embed = nn.Embedding(self.prod_rule_corpus.num_node_symbol, self.element_embed_dim)
+            for _ in range(num_layers+1):
+                self.layer2layer_list.append(nn.Linear(self.element_embed_dim, self.element_embed_dim))
+                self.layer2out_list.append(nn.Linear(self.element_embed_dim, self.out_dim))
+
+
+    def forward(self, prod_rule_idx_seq):
+        ''' forward model for mini-batch
+
+        Parameters
+        ----------
+        prod_rule_idx_seq : (batch_size, length)
+
+        Returns
+        -------
+        Variable, shape (batch_size, length, out_dim)
+        '''
+        batch_size, length = prod_rule_idx_seq.shape
+        if self.use_gpu:
+            out = Variable(torch.zeros((batch_size, length, self.out_dim))).cuda()
+        else:
+            out = Variable(torch.zeros((batch_size, length, self.out_dim)))
+        for each_batch_idx in range(batch_size):
+            for each_idx in range(length):
+                if int(prod_rule_idx_seq[each_batch_idx, each_idx]) == len(self.prod_rule_corpus.prod_rule_list):
+                    continue
+                else:
+                    each_prod_rule = self.prod_rule_corpus.prod_rule_list[int(prod_rule_idx_seq[each_batch_idx, each_idx])]
+
+                    if self.use_gpu:
+                        layer_wise_embed_dict = {each_edge: self.atom_embed(
+                            Variable(torch.LongTensor(
+                                [each_prod_rule.rhs.edge_attr(each_edge)['symbol_idx']]
+                            ), requires_grad=False).cuda())
+                                                 for each_edge in each_prod_rule.rhs.edges}
+                        layer_wise_embed_dict.update({each_node: self.bond_embed(
+                                                         Variable(
+                                                             torch.LongTensor([
+                                                                     each_prod_rule.rhs.node_attr(each_node)['symbol_idx']]),
+                                                             requires_grad=False).cuda()
+                                                     ) for each_node in each_prod_rule.rhs.nodes})
+                    else:
+                        layer_wise_embed_dict = {each_edge: self.atom_embed(
+                            Variable(torch.LongTensor(
+                                [each_prod_rule.rhs.edge_attr(each_edge)['symbol_idx']]
+                            ), requires_grad=False))
+                                                 for each_edge in each_prod_rule.rhs.edges}
+                        layer_wise_embed_dict.update({each_node: self.bond_embed(
+                                                         Variable(
+                                                             torch.LongTensor([
+                                                                     each_prod_rule.rhs.node_attr(each_node)['symbol_idx']]), 
+                                                             requires_grad=False)
+                                                     ) for each_node in each_prod_rule.rhs.nodes})
+
+                    for each_layer in range(self.num_layers):
+                        next_layer_embed_dict = {}
+                        for each_edge in each_prod_rule.rhs.edges:
+                            v = layer_wise_embed_dict[each_edge]
+                            for each_node in each_prod_rule.rhs.nodes_in_edge(each_edge):
+                                v += layer_wise_embed_dict[each_node]
+                            next_layer_embed_dict[each_edge] = self.layer2layer_activation(self.layer2layer_list[each_layer](v))
+                        for each_node in each_prod_rule.rhs.nodes:
+                            v = layer_wise_embed_dict[each_node]
+                            for each_edge in each_prod_rule.rhs.adj_edges(each_node):
+                                v += layer_wise_embed_dict[each_edge]
+                            next_layer_embed_dict[each_node] = self.layer2layer_activation(self.layer2layer_list[each_layer](v))
+                        layer_wise_embed_dict = next_layer_embed_dict
+                    for each_edge in each_prod_rule.rhs.edges:
+                        out[each_batch_idx, each_idx, :] = self.layer2out_activation(self.layer2out_list[self.num_layers](v))
+                    for each_edge in each_prod_rule.rhs.edges:
+                        out[each_batch_idx, each_idx, :] = self.layer2out_activation(self.layer2out_list[self.num_layers](v))
+                        
+        return out
+
+
+class MolecularProdRuleEmbeddingUsingFeatures(nn.Module):
+    
+    ''' molecular fingerprint layer
+    '''
+
+    def __init__(self, prod_rule_corpus, layer2layer_activation, layer2out_activation,
+                 out_dim=32, num_layers=3, padding_idx=None, use_gpu=False):
+        super().__init__()
+        if padding_idx is not None:
+            assert padding_idx == -1, 'padding_idx must be -1.'
+        self.feature_dict, self.feature_dim = prod_rule_corpus.construct_feature_vectors()
+        self.prod_rule_corpus = prod_rule_corpus
+        self.layer2layer_activation = layer2layer_activation
+        self.layer2out_activation = layer2out_activation
+        self.out_dim = out_dim
+        self.num_layers = num_layers
+        self.padding_idx = padding_idx
+        self.use_gpu = use_gpu
+
+        self.layer2layer_list = []
+        self.layer2out_list = []
+
+        if self.use_gpu:
+            for each_key in self.feature_dict:
+                self.feature_dict[each_key] = self.feature_dict[each_key].to_dense().cuda()
+            for _ in range(num_layers):
+                self.layer2layer_list.append(nn.Linear(self.feature_dim, self.feature_dim).cuda())
+                self.layer2out_list.append(nn.Linear(self.feature_dim, self.out_dim).cuda())
+        else:
+            for _ in range(num_layers):
+                self.layer2layer_list.append(nn.Linear(self.feature_dim, self.feature_dim))
+                self.layer2out_list.append(nn.Linear(self.feature_dim, self.out_dim))
+
+
+    def forward(self, prod_rule_idx_seq):
+        ''' forward model for mini-batch
+
+        Parameters
+        ----------
+        prod_rule_idx_seq : (batch_size, length)
+
+        Returns
+        -------
+        Variable, shape (batch_size, length, out_dim)
+        '''
+        batch_size, length = prod_rule_idx_seq.shape
+        if self.use_gpu:
+            out = Variable(torch.zeros((batch_size, length, self.out_dim))).cuda()
+        else:
+            out = Variable(torch.zeros((batch_size, length, self.out_dim)))
+        for each_batch_idx in range(batch_size):
+            for each_idx in range(length):
+                if int(prod_rule_idx_seq[each_batch_idx, each_idx]) == len(self.prod_rule_corpus.prod_rule_list):
+                    continue
+                else:
+                    each_prod_rule = self.prod_rule_corpus.prod_rule_list[int(prod_rule_idx_seq[each_batch_idx, each_idx])]
+                    edge_list = sorted(list(each_prod_rule.rhs.edges))
+                    node_list = sorted(list(each_prod_rule.rhs.nodes))
+                    adj_mat = torch.FloatTensor(each_prod_rule.rhs_adj_mat(edge_list + node_list).todense() + np.identity(len(edge_list)+len(node_list)))
+                    if self.use_gpu:
+                        adj_mat = adj_mat.cuda()
+                    layer_wise_embed = [
+                        self.feature_dict[each_prod_rule.rhs.edge_attr(each_edge)['symbol']]
+                        for each_edge in edge_list]\
+                            + [self.feature_dict[each_prod_rule.rhs.node_attr(each_node)['symbol']]
+                               for each_node in node_list]
+                    for each_node in each_prod_rule.ext_node.values():
+                        layer_wise_embed[each_prod_rule.rhs.num_edges + node_list.index(each_node)] \
+                                = layer_wise_embed[each_prod_rule.rhs.num_edges + node_list.index(each_node)] \
+                                + self.feature_dict[('ext_id', each_prod_rule.rhs.node_attr(each_node)['ext_id'])]
+                    layer_wise_embed = torch.stack(layer_wise_embed)
+
+                    for each_layer in range(self.num_layers):
+                        message = adj_mat @ layer_wise_embed
+                        next_layer_embed = self.layer2layer_activation(self.layer2layer_list[each_layer](message))
+                        out[each_batch_idx, each_idx, :] \
+                                = out[each_batch_idx, each_idx, :] \
+                                + self.layer2out_activation(self.layer2out_list[each_layer](message)).sum(dim=0)
+                        layer_wise_embed = next_layer_embed
+        return out
diff --git a/models/mhg_model/images/mhg_example.png b/models/mhg_model/images/mhg_example.png
new file mode 100644
index 0000000000000000000000000000000000000000..3a7dd8ce73476fba75ed242e67147946d99740eb
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diff --git a/models/mhg_model/images/mhg_example1.png b/models/mhg_model/images/mhg_example1.png
new file mode 100644
index 0000000000000000000000000000000000000000..150b71f10580655433a6f59a60cbc2afc07d8dc8
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diff --git a/models/mhg_model/images/mhg_example2.png b/models/mhg_model/images/mhg_example2.png
new file mode 100644
index 0000000000000000000000000000000000000000..b00f97a7fb3bec25c0e6e42990d18aaa216eff2d
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diff --git a/models/mhg_model/load.py b/models/mhg_model/load.py
new file mode 100644
index 0000000000000000000000000000000000000000..09c8042cdf58b3f657d056955c450d4962a0fe52
--- /dev/null
+++ b/models/mhg_model/load.py
@@ -0,0 +1,84 @@
+# -*- coding:utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+import os
+import pickle
+import sys
+
+from rdkit import Chem
+import torch
+from torch_geometric.utils.smiles import from_smiles
+
+from typing import Any, Dict, List, Optional, Union
+from typing_extensions import Self
+
+from .graph_grammar.io.smi import hg_to_mol
+from .models.mhgvae import GrammarGINVAE
+from huggingface_hub import hf_hub_download
+
+
+class PretrainedModelWrapper:
+    model: GrammarGINVAE
+
+    def __init__(self, model_dict: Dict[str, Any]) -> None:
+        json_params = model_dict['gnn_params']
+        encoder_params = json_params['encoder_params']
+        encoder_params['node_feature_size'] = model_dict['num_features']
+        encoder_params['edge_feature_size'] = model_dict['num_edge_features']
+        self.model = GrammarGINVAE(model_dict['hrg'], rank=-1, encoder_params=encoder_params,
+                                   decoder_params=json_params['decoder_params'],
+                                   prod_rule_embed_params=json_params["prod_rule_embed_params"],
+                                   batch_size=512, max_len=model_dict['max_length'])
+        self.model.load_state_dict(model_dict['model_state_dict'])
+
+        self.model.eval()
+
+    def to(self, device: Union[str, int, torch.device]) -> Self:
+        dev_type = type(device)
+        if dev_type != torch.device:
+            if dev_type == str or torch.cuda.is_available():
+                device = torch.device(device)
+            else:
+                device = torch.device("mps", device)
+
+        self.model = self.model.to(device)
+        return self
+
+    def encode(self, data: List[str]) -> List[torch.tensor]:
+        # Need to encode them into a graph nn
+        output = []
+        for d in data:
+            params = next(self.model.parameters())
+            g = from_smiles(d)
+            if (g.cpu() and params != 'cpu') or (not g.cpu() and params == 'cpu'):
+                g.to(params.device)
+            ltvec = self.model.graph_embed(g.x, g.edge_index, g.edge_attr, g.batch)
+            output.append(ltvec[0])
+        return output
+
+    def decode(self, data: List[torch.tensor]) -> List[str]:
+        output = []
+        for d in data:
+            mu, logvar = self.model.get_mean_var(d.unsqueeze(0))
+            z = self.model.reparameterize(mu, logvar)
+            flags, _, hgs = self.model.decode(z)
+            if flags[0]:
+                reconstructed_mol, _ = hg_to_mol(hgs[0], True)
+                output.append(Chem.MolToSmiles(reconstructed_mol))
+            else:
+                output.append(None)
+        return output
+
+
+def load(model_name: str = "models/mhg_model/pickles/mhggnn_pretrained_model_0724_2023.pickle") -> Optional[
+    PretrainedModelWrapper]:
+    repo_id = "ibm/materials.mhg-ged"
+    filename = "mhggnn_pretrained_model_0724_2023.pickle"
+    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    with open(file_path, "rb") as f:
+        model_dict = pickle.load(f)
+        return PretrainedModelWrapper(model_dict)
+    return None
diff --git a/models/mhg_model/mhg_gnn.egg-info/PKG-INFO b/models/mhg_model/mhg_gnn.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..205fc9ded7ff7f8a67d589d1bc0fa997a77a067c
--- /dev/null
+++ b/models/mhg_model/mhg_gnn.egg-info/PKG-INFO
@@ -0,0 +1,102 @@
+Metadata-Version: 2.1
+Name: mhg-gnn
+Version: 0.0
+Summary: Package for mhg-gnn
+Author: team
+License: TBD
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.9
+Description-Content-Type: text/markdown
+Requires-Dist: networkx>=2.8
+Requires-Dist: numpy<2.0.0,>=1.23.5
+Requires-Dist: pandas>=1.5.3
+Requires-Dist: rdkit-pypi<2023.9.6,>=2022.9.4
+Requires-Dist: torch>=2.0.0
+Requires-Dist: torchinfo>=1.8.0
+Requires-Dist: torch-geometric>=2.3.1
+
+# mhg-gnn
+
+This repository provides PyTorch source code assosiated with our publication, "MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network"
+
+**Paper:** [Arxiv Link](https://arxiv.org/pdf/2309.16374)
+
+For more information contact: SEIJITKD@jp.ibm.com
+
+![mhg-gnn](images/mhg_example1.png)
+
+## Introduction
+
+We present MHG-GNN, an autoencoder architecture
+that has an encoder based on GNN and a decoder based on a sequential model with MHG.
+Since the encoder is a GNN variant, MHG-GNN can accept any molecule as input, and  
+demonstrate high predictive performance on molecular graph data.
+In addition, the decoder inherits the theoretical guarantee of MHG on always generating a structurally valid molecule as output.
+
+## Table of Contents
+
+1. [Getting Started](#getting-started)
+    1. [Pretrained Models and Training Logs](#pretrained-models-and-training-logs)
+    2. [Replicating Conda Environment](#replicating-conda-environment)
+2. [Feature Extraction](#feature-extraction)
+
+## Getting Started
+
+**This code and environment have been tested on Intel E5-2667 CPUs at 3.30GHz and NVIDIA A100 Tensor Core GPUs.**
+
+### Pretrained Models and Training Logs
+
+We provide checkpoints of the MHG-GNN model pre-trained on a dataset of ~1.34M molecules curated from PubChem. (later) For model weights: [HuggingFace Link]()
+
+Add the MHG-GNN `pre-trained weights.pt` to the `models/` directory according to your needs. 
+
+### Replacicating Conda Environment
+
+Follow these steps to replicate our Conda environment and install the necessary libraries:
+
+```
+conda create --name mhg-gnn-env python=3.8.18
+conda activate mhg-gnn-env
+```
+
+#### Install Packages with Conda
+
+```
+conda install -c conda-forge networkx=2.8
+conda install numpy=1.23.5
+# conda install -c conda-forge rdkit=2022.9.4
+conda install pytorch=2.0.0 torchvision torchaudio -c pytorch
+conda install -c conda-forge torchinfo=1.8.0
+conda install pyg -c pyg
+```
+
+#### Install Packages with pip
+```
+pip install rdkit torch-nl==0.3 torch-scatter torch-sparse
+```
+
+## Feature Extraction
+
+The example notebook [mhg-gnn_encoder_decoder_example.ipynb](notebooks/mhg-gnn_encoder_decoder_example.ipynb) contains code to load checkpoint files and use the pre-trained model for encoder and decoder tasks.
+
+To load mhg-gnn, you can simply use:
+
+```python
+import torch
+import load
+
+model = load.load()
+```
+
+To encode SMILES into embeddings, you can use:
+
+```python
+with torch.no_grad():
+    repr = model.encode(["CCO", "O=C=O", "OC(=O)c1ccccc1C(=O)O"])
+```
+
+For decoder, you can use the function, so you can return from embeddings to SMILES strings:
+
+```python
+orig = model.decode(repr)
+```
diff --git a/models/mhg_model/mhg_gnn.egg-info/SOURCES.txt b/models/mhg_model/mhg_gnn.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6429c60d226eadd5f9fce9ba11de93451412c34
--- /dev/null
+++ b/models/mhg_model/mhg_gnn.egg-info/SOURCES.txt
@@ -0,0 +1,46 @@
+README.md
+setup.cfg
+setup.py
+./graph_grammar/__init__.py
+./graph_grammar/hypergraph.py
+./graph_grammar/algo/__init__.py
+./graph_grammar/algo/tree_decomposition.py
+./graph_grammar/graph_grammar/__init__.py
+./graph_grammar/graph_grammar/base.py
+./graph_grammar/graph_grammar/corpus.py
+./graph_grammar/graph_grammar/hrg.py
+./graph_grammar/graph_grammar/symbols.py
+./graph_grammar/graph_grammar/utils.py
+./graph_grammar/io/__init__.py
+./graph_grammar/io/smi.py
+./graph_grammar/nn/__init__.py
+./graph_grammar/nn/dataset.py
+./graph_grammar/nn/decoder.py
+./graph_grammar/nn/encoder.py
+./graph_grammar/nn/graph.py
+./models/__init__.py
+./models/mhgvae.py
+graph_grammar/__init__.py
+graph_grammar/hypergraph.py
+graph_grammar/algo/__init__.py
+graph_grammar/algo/tree_decomposition.py
+graph_grammar/graph_grammar/__init__.py
+graph_grammar/graph_grammar/base.py
+graph_grammar/graph_grammar/corpus.py
+graph_grammar/graph_grammar/hrg.py
+graph_grammar/graph_grammar/symbols.py
+graph_grammar/graph_grammar/utils.py
+graph_grammar/io/__init__.py
+graph_grammar/io/smi.py
+graph_grammar/nn/__init__.py
+graph_grammar/nn/dataset.py
+graph_grammar/nn/decoder.py
+graph_grammar/nn/encoder.py
+graph_grammar/nn/graph.py
+mhg_gnn.egg-info/PKG-INFO
+mhg_gnn.egg-info/SOURCES.txt
+mhg_gnn.egg-info/dependency_links.txt
+mhg_gnn.egg-info/requires.txt
+mhg_gnn.egg-info/top_level.txt
+models/__init__.py
+models/mhgvae.py
\ No newline at end of file
diff --git a/models/mhg_model/mhg_gnn.egg-info/dependency_links.txt b/models/mhg_model/mhg_gnn.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/models/mhg_model/mhg_gnn.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/models/mhg_model/mhg_gnn.egg-info/requires.txt b/models/mhg_model/mhg_gnn.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..54aa1b371905a3d46c6cbc15741cfb9b8a376c7d
--- /dev/null
+++ b/models/mhg_model/mhg_gnn.egg-info/requires.txt
@@ -0,0 +1,7 @@
+networkx>=2.8
+numpy<2.0.0,>=1.23.5
+pandas>=1.5.3
+rdkit-pypi<2023.9.6,>=2022.9.4
+torch>=2.0.0
+torchinfo>=1.8.0
+torch-geometric>=2.3.1
diff --git a/models/mhg_model/mhg_gnn.egg-info/top_level.txt b/models/mhg_model/mhg_gnn.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d606741958961ccbe1f156f690406bcd74658ad4
--- /dev/null
+++ b/models/mhg_model/mhg_gnn.egg-info/top_level.txt
@@ -0,0 +1,2 @@
+graph_grammar
+models
diff --git a/models/mhg_model/models/__init__.py b/models/mhg_model/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1c71cf8133e8cd3edbcb23b4ecf2cd326ec0316
--- /dev/null
+++ b/models/mhg_model/models/__init__.py
@@ -0,0 +1,5 @@
+# -*- coding:utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
diff --git a/models/mhg_model/models/__pycache__/__init__.cpython-310.pyc b/models/mhg_model/models/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cf44636d116bd6ed8dca74ce181001b16c38ec28
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diff --git a/models/mhg_model/models/__pycache__/mhgvae.cpython-310.pyc b/models/mhg_model/models/__pycache__/mhgvae.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..09f575ccae3a5bd332558d5b51b6043e547532ed
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diff --git a/models/mhg_model/models/mhgvae.py b/models/mhg_model/models/mhgvae.py
new file mode 100644
index 0000000000000000000000000000000000000000..829a2c5567e7ffb2b61842840b83d428c2e2cbe0
--- /dev/null
+++ b/models/mhg_model/models/mhgvae.py
@@ -0,0 +1,956 @@
+# -*- coding:utf-8 -*-
+# Rhizome
+# Version beta 0.0, August 2023
+# Property of IBM Research, Accelerated Discovery
+#
+
+"""
+PLEASE NOTE THIS IMPLEMENTATION INCLUDES ADAPTED SOURCE CODE
+OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE, 
+E.G., GRUEncoder/GRUDecoder, GrammarSeq2SeqVAE AND EVEN SOME METHODS OF GrammarGINVAE.
+THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. 
+"""
+
+import numpy as np
+import logging
+
+import torch
+from torch.autograd import Variable
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.modules.loss import _Loss
+
+from torch_geometric.nn import MessagePassing
+from torch_geometric.nn import global_add_pool
+
+
+from ..graph_grammar.graph_grammar.symbols import NTSymbol
+from ..graph_grammar.nn.encoder import EncoderBase
+from ..graph_grammar.nn.decoder import DecoderBase
+
+def get_atom_edge_feature_dims():
+    from torch_geometric.utils.smiles import x_map, e_map
+    func = lambda x: len(x[1])
+    return list(map(func, x_map.items())), list(map(func, e_map.items()))
+
+
+class FeatureEmbedding(nn.Module):
+    def __init__(self, input_dims, embedded_dim):
+        super().__init__()
+        self.embedding_list = nn.ModuleList()
+        for dim in input_dims:
+            embedding = nn.Embedding(dim, embedded_dim)
+            self.embedding_list.append(embedding)
+    
+    def forward(self, x):
+        output = 0
+        for i in range(x.shape[1]):
+            input = x[:, i].to(torch.int)
+            device = next(self.parameters()).device
+            if device != input.device:
+                input = input.to(device)
+            emb = self.embedding_list[i](input)
+            output += emb
+        return output
+    
+
+class GRUEncoder(EncoderBase):
+
+    def __init__(self, input_dim: int, hidden_dim: int, num_layers: int,
+                 bidirectional: bool, dropout: float, batch_size: int, rank: int=-1,
+                 no_dropout: bool=False):
+        super().__init__()
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.num_layers = num_layers
+        self.bidirectional = bidirectional
+        self.dropout = dropout
+        self.batch_size = batch_size
+        self.rank = rank
+        self.model = nn.GRU(input_size=self.input_dim,
+                            hidden_size=self.hidden_dim,
+                            num_layers=self.num_layers,
+                            batch_first=True,
+                            bidirectional=self.bidirectional,
+                            dropout=self.dropout if not no_dropout else 0)
+        if self.rank >= 0:
+            if torch.cuda.is_available():
+                self.model = self.model.to(rank)
+            else:
+                # support mac mps
+                self.model = self.model.to(torch.device("mps", rank))
+        self.init_hidden(self.batch_size)
+        
+    def init_hidden(self, bsize):
+        self.h0 = torch.zeros(((self.bidirectional + 1) * self.num_layers,
+                               min(self.batch_size, bsize),
+                               self.hidden_dim),
+                              requires_grad=False)
+        if self.rank >= 0:        
+            if torch.cuda.is_available():
+                self.h0 = self.h0.to(self.rank)
+            else:
+                # support mac mps
+                self.h0 = self.h0.to(torch.device("mps", self.rank))
+
+    def to(self, device):
+        newself = super().to(device)
+        newself.model = newself.model.to(device)
+        newself.h0 = newself.h0.to(device)
+        newself.rank = next(newself.parameters()).get_device()
+        return newself
+
+    def forward(self, in_seq_emb):
+        ''' forward model
+
+        Parameters
+        ----------
+        in_seq_emb : Tensor, shape (batch_size, max_len, input_dim)
+
+        Returns
+        -------
+        hidden_seq_emb : Tensor, shape (batch_size, max_len, 1 + bidirectional, hidden_dim)
+        '''
+        # Kishi: I think original MHG had this init_hidden()
+        self.init_hidden(in_seq_emb.size(0))
+        max_len = in_seq_emb.size(1)
+        hidden_seq_emb, self.h0 = self.model(
+            in_seq_emb, self.h0)
+        # As shown as returns, convert hidden_seq_emb: (batch_size, seq_len, (1 or 2) * hidden_size) -->
+        # (batch_size, seq_len, 1 or 2, hidden_size)
+        # In the original input the original GRU/LSTM with bidirectional encoding 
+        # has contactinated tensors 
+        # (first half for forward RNN, latter half for backward RNN)
+        # so convert them in a more friendly format packed for each RNN
+        hidden_seq_emb = hidden_seq_emb.view(-1,
+                                             max_len,
+                                             1 + self.bidirectional,
+                                             self.hidden_dim)
+        return hidden_seq_emb
+
+
+class GRUDecoder(DecoderBase):
+
+    def __init__(self, input_dim: int, hidden_dim: int, num_layers: int,
+                 dropout: float, batch_size: int, rank: int=-1,
+                 no_dropout: bool=False):
+        super().__init__()
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.batch_size = batch_size
+        self.rank = rank
+        self.model = nn.GRU(input_size=self.input_dim,
+                            hidden_size=self.hidden_dim,
+                            num_layers=self.num_layers,
+                            batch_first=True,
+                            bidirectional=False,
+                            dropout=self.dropout if not no_dropout else 0
+        )
+        if self.rank >= 0:
+            if torch.cuda.is_available():
+                self.model = self.model.to(self.rank)
+            else:
+                # support mac mps
+                self.model = self.model.to(torch.device("mps", self.rank))
+        self.init_hidden(self.batch_size)
+
+    def init_hidden(self, bsize):
+        self.hidden_dict['h'] = torch.zeros((self.num_layers,
+                                             min(self.batch_size, bsize),
+                                             self.hidden_dim),
+                                            requires_grad=False)
+        if self.rank >= 0:
+            if torch.cuda.is_available():
+                self.hidden_dict['h'] = self.hidden_dict['h'].to(self.rank)
+            else:
+                self.hidden_dict['h'] = self.hidden_dict['h'].to(torch.device("mps", self.rank))
+
+    def to(self, device):
+        newself = super().to(device)
+        newself.model = newself.model.to(device)
+        for k in self.hidden_dict.keys():
+            newself.hidden_dict[k] = newself.hidden_dict[k].to(device)
+        newself.rank = next(newself.parameters()).get_device()
+        return newself
+
+    def forward_one_step(self, tgt_emb_in):
+        ''' one-step forward model
+
+        Parameters
+        ----------
+        tgt_emb_in : Tensor, shape (batch_size, input_dim)
+
+        Returns
+        -------
+        Tensor, shape (batch_size, hidden_dim)
+        '''
+        bsize = tgt_emb_in.size(0)
+        tgt_emb_out, self.hidden_dict['h'] \
+            = self.model(tgt_emb_in.view(bsize, 1, -1),
+                         self.hidden_dict['h'])
+        return tgt_emb_out
+
+
+class NodeMLP(nn.Module):
+    def __init__(self, input_size, output_size, hidden_size):
+        super().__init__()
+        self.lin1 = nn.Linear(input_size, hidden_size)
+        self.nbat = nn.BatchNorm1d(hidden_size)
+        self.lin2 = nn.Linear(hidden_size, output_size)
+        
+    def forward(self, x):
+        x = self.lin1(x)
+        x = self.nbat(x)
+        x = x.relu()
+        x = self.lin2(x)
+        return x
+    
+    
+class GINLayer(MessagePassing):
+    def __init__(self, node_input_size, node_output_size, node_hidden_size, edge_input_size):
+        super().__init__()
+        self.node_mlp = NodeMLP(node_input_size, node_output_size, node_hidden_size)
+        self.edge_mlp = FeatureEmbedding(edge_input_size, node_output_size)
+        self.eps = nn.Parameter(torch.tensor([0.0]))
+        
+    def forward(self, x, edge_index, edge_attr):
+        msg = self.propagate(edge_index, x=x ,edge_attr=edge_attr)
+        x = (1.0 + self.eps) * x + msg
+        x = x.relu()
+        x = self.node_mlp(x)
+        return x
+    
+    def message(self, x_j, edge_attr):
+        edge_attr = self.edge_mlp(edge_attr)
+        x_j = x_j + edge_attr
+        x_j = x_j.relu()
+        return x_j
+    
+    def update(self, aggr_out):
+        return aggr_out
+
+#TODO implement the case where features of atoms and edges are considered
+# Check GraphMVP and ogb (open graph benchmark) to realize this
+class GIN(torch.nn.Module):
+    def __init__(self, node_feature_size, edge_feature_size, hidden_channels=64, 
+                 proximity_size=3, dropout=0.1):
+        super().__init__()
+        #print("(num node features, num edge features)=", (node_feature_size, edge_feature_size))
+        hsize = hidden_channels * 2
+        atom_dim, edge_dim = get_atom_edge_feature_dims()
+        self.trans = FeatureEmbedding(atom_dim, hidden_channels)
+        ml = []
+        for _ in range(proximity_size):
+            ml.append(GINLayer(hidden_channels, hidden_channels, hsize, edge_dim))
+        self.mlist = nn.ModuleList(ml)
+        #It is possible to calculate relu with x.relu() where x is an output
+        #self.activations = nn.ModuleList(actl)
+        self.dropout = dropout
+        self.proximity_size = proximity_size
+        
+    def forward(self, x, edge_index, edge_attr, batch_size):
+        x = x.to(torch.float)
+        #print("before: edge_weight.shape=", edge_attr.shape)
+        edge_attr = edge_attr.to(torch.float)
+        #print("after: edge_weight.shape=", edge_attr.shape)
+        x = self.trans(x)
+        # TODO Check if this x is consistent with global_add_pool
+        hlist = [global_add_pool(x, batch_size)]
+        for id, m in enumerate(self.mlist):
+            x = m(x, edge_index=edge_index, edge_attr=edge_attr)
+            #print("Done with one layer") 
+            ###if id != self.proximity_size - 1:
+            x = x.relu()
+            x = F.dropout(x, p=self.dropout, training=self.training)
+            #h = global_mean_pool(x, batch_size)
+            h = global_add_pool(x, batch_size)
+            hlist.append(h)
+            #print("Done with one relu call: x.shape=", x.shape)
+        #print("calling golbal mean pool")
+        #print("calling dropout x.shape=", x.shape)
+        #print("x=", x)
+        #print("hlist[0].shape=", hlist[0].shape)
+        x = torch.cat(hlist, dim=1)
+        #print("x.shape=", x.shape)
+        x = F.dropout(x, p=self.dropout, training=self.training)
+
+        return x
+
+
+# TODO copied from MHG implementation and adapted here. 
+class GrammarSeq2SeqVAE(nn.Module):
+
+    '''
+    Variational seq2seq with grammar.
+    TODO: rewrite this class using mixin
+    '''
+
+    def __init__(self, hrg, rank=-1, latent_dim=64, max_len=80,
+                 batch_size=64, padding_idx=-1, 
+                 encoder_params={'hidden_dim': 384, 'num_layers': 3, 'bidirectional': True,
+                                 'dropout': 0.1},
+                 decoder_params={'hidden_dim': 384, #'num_layers': 2,
+                                 'num_layers': 3,
+                                 'dropout': 0.1},
+                 prod_rule_embed_params={'out_dim': 128},
+                 no_dropout=False):
+
+        super().__init__()
+        # TODO USE GRU FOR ENCODING AND DECODING
+        self.hrg = hrg
+        self.rank = rank
+        self.prod_rule_corpus = hrg.prod_rule_corpus
+        self.prod_rule_embed_params = prod_rule_embed_params
+
+        self.vocab_size = hrg.num_prod_rule + 1
+        self.batch_size = batch_size
+        self.padding_idx = np.mod(padding_idx, self.vocab_size)
+        self.no_dropout = no_dropout
+
+        self.latent_dim = latent_dim
+        self.max_len = max_len
+        self.encoder_params = encoder_params
+        self.decoder_params = decoder_params
+        
+        # TODO Simple embedding is used. Check if a domain-dependent embedding works or not. 
+        embed_out_dim = self.prod_rule_embed_params['out_dim']        
+        #use MolecularProdRuleEmbedding later on
+        self.src_embedding = nn.Embedding(self.vocab_size, embed_out_dim,
+                                          padding_idx=self.padding_idx)
+        self.tgt_embedding = nn.Embedding(self.vocab_size, embed_out_dim,
+                                          padding_idx=self.padding_idx)
+
+        # USE a GRU-based encoder in MHG
+        self.encoder = GRUEncoder(input_dim=embed_out_dim, batch_size=self.batch_size,
+                                      rank=self.rank, no_dropout=self.no_dropout,
+                                  **self.encoder_params)
+
+        lin_dim = (self.encoder_params.get('bidirectional', False) + 1) * self.encoder_params['hidden_dim']
+        lin_out_dim = self.latent_dim
+        self.hidden2mean = nn.Linear(lin_dim, lin_out_dim, bias=False)
+        self.hidden2logvar = nn.Linear(lin_dim, lin_out_dim)
+   
+        # USE a GRU-based decoder in MHG
+        self.decoder = GRUDecoder(input_dim=embed_out_dim, batch_size=self.batch_size,
+                                  rank=self.rank, no_dropout=self.no_dropout, **self.decoder_params)
+        self.latent2tgt_emb = nn.Linear(self.latent_dim, embed_out_dim)
+        self.latent2hidden_dict = nn.ModuleDict()
+        dec_lin_out_dim = self.decoder_params['hidden_dim']
+        for each_hidden in self.decoder.hidden_dict.keys():
+            self.latent2hidden_dict[each_hidden] = nn.Linear(self.latent_dim, dec_lin_out_dim)
+            if self.rank >= 0:
+                if torch.cuda.is_available():
+                    self.latent2hidden_dict[each_hidden] = self.latent2hidden_dict[each_hidden].to(self.rank)
+                else:
+                    # support mac mps
+                    self.latent2hidden_dict[each_hidden] = self.latent2hidden_dict[each_hidden].to(torch.device("mps", self.rank))
+
+        self.dec2vocab = nn.Linear(dec_lin_out_dim, self.vocab_size)
+        self.encoder.init_hidden(self.batch_size)
+        self.decoder.init_hidden(self.batch_size)
+
+        # TODO Do we need this?
+        if hasattr(self.src_embedding, 'weight'):
+            self.src_embedding.weight.data.uniform_(-0.1, 0.1)
+        if hasattr(self.tgt_embedding, 'weight'):
+            self.tgt_embedding.weight.data.uniform_(-0.1, 0.1)
+            
+        self.encoder.init_hidden(self.batch_size)
+        self.decoder.init_hidden(self.batch_size)
+    
+    def to(self, device):
+        newself = super().to(device)
+        newself.src_embedding = newself.src_embedding.to(device)
+        newself.tgt_embedding = newself.tgt_embedding.to(device)
+        newself.encoder = newself.encoder.to(device)
+        newself.decoder = newself.decoder.to(device)
+        newself.dec2vocab = newself.dec2vocab.to(device)
+        newself.hidden2mean = newself.hidden2mean.to(device)
+        newself.hidden2logvar = newself.hidden2logvar.to(device)
+        newself.latent2tgt_emb = newself.latent2tgt_emb.to(device)
+        newself.latent2hidden_dict = newself.latent2hidden_dict.to(device)
+        return newself
+    
+    def forward(self, in_seq, out_seq):
+        ''' forward model
+
+        Parameters
+        ----------
+        in_seq : Variable, shape (batch_size, length)
+            each element corresponds to word index.
+            where the index should be less than `vocab_size`
+
+        Returns
+        -------
+        Variable, shape (batch_size, length, vocab_size)
+            logit of each word (applying softmax yields the probability)
+        '''
+        mu, logvar = self.encode(in_seq)
+        z = self.reparameterize(mu, logvar)
+        return self.decode(z, out_seq), mu, logvar
+
+    def encode(self, in_seq):
+        src_emb = self.src_embedding(in_seq)
+        src_h = self.encoder.forward(src_emb)
+        if self.encoder_params.get('bidirectional', False):
+            concat_src_h = torch.cat((src_h[:, -1, 0, :], src_h[:, 0, 1, :]), dim=1)
+            return self.hidden2mean(concat_src_h), self.hidden2logvar(concat_src_h)
+        else:
+            return self.hidden2mean(src_h[:, -1, :]), self.hidden2logvar(src_h[:, -1, :])
+
+    def reparameterize(self, mu, logvar, training=True):
+        if training:
+            std = logvar.mul(0.5).exp_()
+            device = next(self.parameters()).device
+            eps = Variable(std.data.new(std.size()).normal_())
+            if device != eps.get_device():
+                eps.to(device)
+            return eps.mul(std).add_(mu)
+        else:
+            return mu
+
+    #TODO Not tested. Need to implement this in case of molecular structure generation
+    def sample(self, sample_size=-1, deterministic=True, return_z=False):
+        self.eval()
+        self.init_hidden()
+        if sample_size == -1:
+            sample_size = self.batch_size
+
+        num_iter = int(np.ceil(sample_size / self.batch_size))
+        hg_list = []
+        z_list = []
+        for _ in range(num_iter):
+            z = Variable(torch.normal(
+                torch.zeros(self.batch_size, self.latent_dim),
+                torch.ones(self.batch_size * self.latent_dim))).cuda()
+            _, each_hg_list = self.decode(z, deterministic=deterministic)
+            z_list.append(z)
+            hg_list += each_hg_list
+        z = torch.cat(z_list)[:sample_size]
+        hg_list = hg_list[:sample_size]
+        if return_z:
+            return hg_list, z.cpu().detach().numpy()
+        else:
+            return hg_list
+
+    def decode(self, z=None, out_seq=None, deterministic=True):
+        if z is None:
+            z = Variable(torch.normal(
+                torch.zeros(self.batch_size, self.latent_dim),
+                torch.ones(self.batch_size * self.latent_dim)))
+        if self.rank >= 0:
+            z = z.to(next(self.parameters()).device)
+
+        hidden_dict_0 = {}
+        for each_hidden in self.latent2hidden_dict.keys():
+            hidden_dict_0[each_hidden] = self.latent2hidden_dict[each_hidden](z)
+        bsize = z.size(0)
+        self.decoder.init_hidden(bsize) 
+        self.decoder.feed_hidden(hidden_dict_0)
+
+        if out_seq is not None:
+            tgt_emb0 = self.latent2tgt_emb(z)
+            tgt_emb0 = tgt_emb0.view(tgt_emb0.shape[0], 1, tgt_emb0.shape[1])
+            out_seq_emb = self.tgt_embedding(out_seq)
+            tgt_emb = torch.cat((tgt_emb0, out_seq_emb), dim=1)[:, :-1, :]
+            tgt_emb_pred_list = []
+            for each_idx in range(self.max_len):
+                tgt_emb_pred = self.decoder.forward_one_step(tgt_emb[:, each_idx, :].view(bsize, 1, -1))
+                tgt_emb_pred_list.append(tgt_emb_pred)
+            vocab_logit = self.dec2vocab(torch.cat(tgt_emb_pred_list, dim=1))
+            return vocab_logit
+        else:
+            with torch.no_grad():
+                tgt_emb = self.latent2tgt_emb(z)
+                tgt_emb = tgt_emb.view(tgt_emb.shape[0], 1, tgt_emb.shape[1])
+                tgt_emb_pred_list = []
+                stack_list = []
+                hg_list = []
+                nt_symbol_list = []
+                nt_edge_list = []
+                gen_finish_list = []
+                for _ in range(bsize):
+                    stack_list.append([])
+                    hg_list.append(None)
+                    nt_symbol_list.append(NTSymbol(degree=0,
+                                                   is_aromatic=False,
+                                                   bond_symbol_list=[]))
+                    nt_edge_list.append(None)
+                    gen_finish_list.append(False)
+
+                for idx in range(self.max_len):
+                    tgt_emb_pred = self.decoder.forward_one_step(tgt_emb)
+                    tgt_emb_pred_list.append(tgt_emb_pred)
+                    vocab_logit = self.dec2vocab(tgt_emb_pred)
+                    for each_batch_idx in range(bsize):
+                        if not gen_finish_list[each_batch_idx]: # if generation has not finished
+                            # get production rule greedily
+                            prod_rule = self.hrg.prod_rule_corpus.sample(vocab_logit[each_batch_idx, :, :-1].squeeze().cpu().numpy(),
+                                                                         nt_symbol_list[each_batch_idx],
+                                                                         deterministic=deterministic)
+                            # convert production rule into an index
+                            tgt_id = self.hrg.prod_rule_list.index(prod_rule)
+                            # apply the production rule
+                            hg_list[each_batch_idx], nt_edges = prod_rule.applied_to(hg_list[each_batch_idx], nt_edge_list[each_batch_idx])
+                            # add non-terminals to the stack
+                            stack_list[each_batch_idx].extend(nt_edges[::-1])
+                            # if the stack size is 0, generation has finished!
+                            if len(stack_list[each_batch_idx]) == 0:
+                                gen_finish_list[each_batch_idx] = True
+                            else:
+                                nt_edge_list[each_batch_idx] = stack_list[each_batch_idx].pop()
+                                nt_symbol_list[each_batch_idx] = hg_list[each_batch_idx].edge_attr(nt_edge_list[each_batch_idx])['symbol']
+                        else:
+                            tgt_id = np.mod(self.padding_idx, self.vocab_size)
+                        indice_tensor = torch.LongTensor([tgt_id])
+                        device = next(self.parameters()).device
+                        if indice_tensor.device != device: 
+                            indice_tensor = indice_tensor.to(device)
+                        tgt_emb[each_batch_idx, :] = self.tgt_embedding(indice_tensor)
+                vocab_logit = self.dec2vocab(torch.cat(tgt_emb_pred_list, dim=1))
+                #for id, v in enumerate(gen_finish_list):
+                #if not v:
+                #    print("bacth id={} not finished generating a sequence: ".format(id))
+                return gen_finish_list, vocab_logit, hg_list
+
+
+# TODO A lot of duplicates with GrammarVAE. Clean up it if necessary
+class GrammarGINVAE(nn.Module):
+
+    '''
+    Variational autoencoder based on GIN and grammar
+    '''
+
+    def __init__(self, hrg, rank=-1, max_len=80,
+                 batch_size=64, padding_idx=-1, 
+                 encoder_params={'node_feature_size': 4, 'edge_feature_size': 3, 
+                                 'hidden_channels': 64, 'proximity_size': 3,
+                                 'dropout': 0.1},
+                 decoder_params={'hidden_dim': 384, 'num_layers': 3,
+                                 'dropout': 0.1},
+                 prod_rule_embed_params={'out_dim': 128},
+                 no_dropout=False):
+
+        super().__init__()
+        # TODO USE GRU FOR ENCODING AND DECODING
+        self.hrg = hrg
+        self.rank = rank
+        self.prod_rule_corpus = hrg.prod_rule_corpus
+        self.prod_rule_embed_params = prod_rule_embed_params
+
+        self.vocab_size = hrg.num_prod_rule + 1
+        self.batch_size = batch_size
+        self.padding_idx = np.mod(padding_idx, self.vocab_size)
+        self.no_dropout = no_dropout
+        self.max_len = max_len
+        self.encoder_params = encoder_params
+        self.decoder_params = decoder_params
+        
+        # TODO Simple embedding is used. Check if a domain-dependent embedding works or not. 
+        embed_out_dim = self.prod_rule_embed_params['out_dim']        
+        #use MolecularProdRuleEmbedding later on
+        self.tgt_embedding = nn.Embedding(self.vocab_size, embed_out_dim,
+                                          padding_idx=self.padding_idx)
+
+        self.encoder = GIN(**self.encoder_params)
+        self.latent_dim = self.encoder_params['hidden_channels']
+        self.proximity_size = self.encoder_params['proximity_size']
+        hidden_dim = self.decoder_params['hidden_dim']
+        self.hidden2mean = nn.Linear(self.latent_dim * (1 + self.proximity_size), self.latent_dim, bias=False)
+        self.hidden2logvar = nn.Linear(self.latent_dim * (1 + self.proximity_size), self.latent_dim)
+   
+        self.decoder = GRUDecoder(input_dim=embed_out_dim, batch_size=self.batch_size,
+                                  rank=self.rank, no_dropout=self.no_dropout, **self.decoder_params)
+        self.latent2tgt_emb = nn.Linear(self.latent_dim, embed_out_dim)
+        self.latent2hidden_dict = nn.ModuleDict()
+        for each_hidden in self.decoder.hidden_dict.keys():
+            self.latent2hidden_dict[each_hidden] = nn.Linear(self.latent_dim, hidden_dim)
+            if self.rank >= 0:
+                if torch.cuda.is_available():
+                    self.latent2hidden_dict[each_hidden] = self.latent2hidden_dict[each_hidden].to(self.rank)
+                else:
+                    # support mac mps
+                    self.latent2hidden_dict[each_hidden] = self.latent2hidden_dict[each_hidden].to(torch.device("mps", self.rank))
+
+        self.dec2vocab = nn.Linear(hidden_dim, self.vocab_size)
+        self.decoder.init_hidden(self.batch_size)
+
+        # TODO Do we need this?
+        if hasattr(self.tgt_embedding, 'weight'):
+            self.tgt_embedding.weight.data.uniform_(-0.1, 0.1)
+        self.decoder.init_hidden(self.batch_size)
+    
+    def to(self, device):
+        newself = super().to(device)
+        newself.encoder = newself.encoder.to(device)
+        newself.decoder = newself.decoder.to(device)
+        newself.rank = next(newself.encoder.parameters()).get_device()
+        return newself
+    
+    def forward(self, x, edge_index, edge_attr, batch_size, out_seq=None, sched_prob = None):
+        mu, logvar = self.encode(x, edge_index, edge_attr, batch_size)
+        z = self.reparameterize(mu, logvar)
+        return self.decode(z, out_seq, sched_prob=sched_prob), mu, logvar
+
+    #TODO Not tested. Need to implement this in case of molecular structure generation
+    def sample(self, sample_size=-1, deterministic=True, return_z=False):
+        self.eval()
+        self.init_hidden()
+        if sample_size == -1:
+            sample_size = self.batch_size
+
+        num_iter = int(np.ceil(sample_size / self.batch_size))
+        hg_list = []
+        z_list = []
+        for _ in range(num_iter):
+            z = Variable(torch.normal(
+                torch.zeros(self.batch_size, self.latent_dim),
+                torch.ones(self.batch_size * self.latent_dim))).cuda()
+            _, each_hg_list = self.decode(z, deterministic=deterministic)
+            z_list.append(z)
+            hg_list += each_hg_list
+        z = torch.cat(z_list)[:sample_size]
+        hg_list = hg_list[:sample_size]
+        if return_z:
+            return hg_list, z.cpu().detach().numpy()
+        else:
+            return hg_list
+
+    def decode(self, z=None, out_seq=None, deterministic=True, sched_prob=None):
+        if z is None:
+            z = Variable(torch.normal(
+                torch.zeros(self.batch_size, self.latent_dim),
+                torch.ones(self.batch_size * self.latent_dim)))
+        if self.rank >= 0:
+            z = z.to(next(self.parameters()).device)
+
+        hidden_dict_0 = {}
+        for each_hidden in self.latent2hidden_dict.keys():
+            hidden_dict_0[each_hidden] = self.latent2hidden_dict[each_hidden](z)
+        bsize = z.size(0)
+        self.decoder.init_hidden(bsize) 
+        self.decoder.feed_hidden(hidden_dict_0)
+
+        if out_seq is not None:
+            tgt_emb0 = self.latent2tgt_emb(z)
+            tgt_emb0 = tgt_emb0.view(tgt_emb0.shape[0], 1, tgt_emb0.shape[1])
+            out_seq_emb = self.tgt_embedding(out_seq)
+            tgt_emb = torch.cat((tgt_emb0, out_seq_emb), dim=1)[:, :-1, :]
+            tgt_emb_pred_list = []
+            tgt_emb_pred = None
+            for each_idx in range(self.max_len):
+                if tgt_emb_pred is None or sched_prob is None or torch.rand(1)[0] <= sched_prob:
+                    inp = tgt_emb[:, each_idx, :].view(bsize, 1, -1)
+                else:
+                    cur_logit = self.dec2vocab(tgt_emb_pred)
+                    yi = torch.argmax(cur_logit, dim=2)
+                    inp = self.tgt_embedding(yi)
+                tgt_emb_pred = self.decoder.forward_one_step(inp)
+                tgt_emb_pred_list.append(tgt_emb_pred)
+            vocab_logit = self.dec2vocab(torch.cat(tgt_emb_pred_list, dim=1))
+            return vocab_logit
+        else:
+            with torch.no_grad():
+                tgt_emb = self.latent2tgt_emb(z)
+                tgt_emb = tgt_emb.view(tgt_emb.shape[0], 1, tgt_emb.shape[1])
+                tgt_emb_pred_list = []
+                stack_list = []
+                hg_list = []
+                nt_symbol_list = []
+                nt_edge_list = []
+                gen_finish_list = []
+                for _ in range(bsize):
+                    stack_list.append([])
+                    hg_list.append(None)
+                    nt_symbol_list.append(NTSymbol(degree=0,
+                                                   is_aromatic=False,
+                                                   bond_symbol_list=[]))
+                    nt_edge_list.append(None)
+                    gen_finish_list.append(False)
+
+                for _ in range(self.max_len):
+                    tgt_emb_pred = self.decoder.forward_one_step(tgt_emb)
+                    tgt_emb_pred_list.append(tgt_emb_pred)
+                    vocab_logit = self.dec2vocab(tgt_emb_pred)
+                    for each_batch_idx in range(bsize):
+                        if not gen_finish_list[each_batch_idx]: # if generation has not finished
+                            # get production rule greedily
+                            prod_rule = self.hrg.prod_rule_corpus.sample(vocab_logit[each_batch_idx, :, :-1].squeeze().cpu().numpy(),
+                                                                         nt_symbol_list[each_batch_idx],
+                                                                         deterministic=deterministic)
+                            # convert production rule into an index
+                            tgt_id = self.hrg.prod_rule_list.index(prod_rule)
+                            # apply the production rule
+                            hg_list[each_batch_idx], nt_edges = prod_rule.applied_to(hg_list[each_batch_idx], nt_edge_list[each_batch_idx])
+                            # add non-terminals to the stack
+                            stack_list[each_batch_idx].extend(nt_edges[::-1])
+                            # if the stack size is 0, generation has finished!
+                            if len(stack_list[each_batch_idx]) == 0:
+                                gen_finish_list[each_batch_idx] = True
+                            else:
+                                nt_edge_list[each_batch_idx] = stack_list[each_batch_idx].pop()
+                                nt_symbol_list[each_batch_idx] = hg_list[each_batch_idx].edge_attr(nt_edge_list[each_batch_idx])['symbol']
+                        else:
+                            tgt_id = np.mod(self.padding_idx, self.vocab_size)
+                        indice_tensor = torch.LongTensor([tgt_id])
+                        if self.rank >= 0:
+                            indice_tensor = indice_tensor.to(next(self.parameters()).device)
+                        tgt_emb[each_batch_idx, :] = self.tgt_embedding(indice_tensor)
+                vocab_logit = self.dec2vocab(torch.cat(tgt_emb_pred_list, dim=1))
+                return gen_finish_list, vocab_logit, hg_list
+
+    #TODO Not tested. Need to implement this in case of molecular structure generation
+    def conditional_distribution(self, z, tgt_id_list):
+        self.eval()
+        self.init_hidden()
+        z = z.cuda()
+
+        hidden_dict_0 = {}
+        for each_hidden in self.latent2hidden_dict.keys():
+            hidden_dict_0[each_hidden] = self.latent2hidden_dict[each_hidden](z)
+        self.decoder.feed_hidden(hidden_dict_0)
+
+        with torch.no_grad():
+            tgt_emb = self.latent2tgt_emb(z)
+            tgt_emb = tgt_emb.view(tgt_emb.shape[0], 1, tgt_emb.shape[1])
+            nt_symbol_list = []
+            stack_list = []
+            hg_list = []
+            nt_edge_list = []
+            gen_finish_list = []
+            for _ in range(self.batch_size):
+                nt_symbol_list.append(NTSymbol(degree=0,
+                                               is_aromatic=False,
+                                               bond_symbol_list=[]))
+                stack_list.append([])
+                hg_list.append(None)
+                nt_edge_list.append(None)
+                gen_finish_list.append(False)
+
+            for each_position in range(len(tgt_id_list[0])):
+                tgt_emb_pred = self.decoder.forward_one_step(tgt_emb)
+                for each_batch_idx in range(self.batch_size):
+                    if not gen_finish_list[each_batch_idx]: # if generation has not finished
+                        # use the prespecified target ids
+                        tgt_id = tgt_id_list[each_batch_idx][each_position]
+                        prod_rule = self.hrg.prod_rule_list[tgt_id]
+                        # apply the production rule
+                        hg_list[each_batch_idx], nt_edges = prod_rule.applied_to(hg_list[each_batch_idx], nt_edge_list[each_batch_idx])
+                        # add non-terminals to the stack
+                        stack_list[each_batch_idx].extend(nt_edges[::-1])
+                        # if the stack size is 0, generation has finished!
+                        if len(stack_list[each_batch_idx]) == 0:
+                            gen_finish_list[each_batch_idx] = True
+                        else:
+                            nt_edge_list[each_batch_idx] = stack_list[each_batch_idx].pop()
+                            nt_symbol_list[each_batch_idx] = hg_list[each_batch_idx].edge_attr(nt_edge_list[each_batch_idx])['symbol']
+                    else:
+                        tgt_id = np.mod(self.padding_idx, self.vocab_size)
+                    indice_tensor = torch.LongTensor([tgt_id])
+                    indice_tensor = indice_tensor.cuda()
+                    tgt_emb[each_batch_idx, :] = self.tgt_embedding(indice_tensor)
+
+            # last one step
+            conditional_logprob_list = []
+            tgt_emb_pred = self.decoder.forward_one_step(tgt_emb)
+            vocab_logit = self.dec2vocab(tgt_emb_pred)
+            for each_batch_idx in range(self.batch_size):
+                if not gen_finish_list[each_batch_idx]: # if generation has not finished
+                    # get production rule greedily
+                    masked_logprob = self.hrg.prod_rule_corpus.masked_logprob(
+                        vocab_logit[each_batch_idx, :, :-1].squeeze().cpu().numpy(),
+                        nt_symbol_list[each_batch_idx])
+                    conditional_logprob_list.append(masked_logprob)
+                else:
+                    conditional_logprob_list.append(None)
+        return conditional_logprob_list
+
+    #TODO Not tested. Need to implement this in case of molecular structure generation
+    def decode_with_beam_search(self, z, beam_width=1):
+        ''' Decode a latent vector using beam search.
+
+        Parameters
+        ----------
+        z
+            latent vector
+        beam_width : int
+            parameter for beam search
+
+        Returns
+        -------
+        List of Hypergraphs
+        '''
+        if self.batch_size != 1:
+            raise ValueError('this method works only under batch_size=1')
+        if self.padding_idx != -1:
+            raise ValueError('this method works only under padding_idx=-1')
+        top_k_tgt_id_list = [[]] * beam_width
+        logprob_list = [0.] * beam_width
+
+        for each_len in range(self.max_len):
+            expanded_logprob_list = np.repeat(logprob_list, self.vocab_size) # including padding_idx
+            expanded_length_list = np.array([0] * (beam_width * self.vocab_size))
+            for each_beam_idx, each_candidate in enumerate(top_k_tgt_id_list):
+                conditional_logprob = self.conditional_distribution(z, [each_candidate])[0]
+                if conditional_logprob is None:
+                    expanded_logprob_list[(each_beam_idx + 1) * self.vocab_size - 1]\
+                        = logprob_list[each_beam_idx]
+                    expanded_logprob_list[each_beam_idx * self.vocab_size : (each_beam_idx + 1) * self.vocab_size - 1]\
+                        = -np.inf
+                    expanded_length_list[each_beam_idx * self.vocab_size : (each_beam_idx + 1) * self.vocab_size]\
+                        = len(each_candidate)
+                else:
+                    expanded_logprob_list[each_beam_idx * self.vocab_size : (each_beam_idx + 1) * self.vocab_size - 1]\
+                        = logprob_list[each_beam_idx] + conditional_logprob
+                    expanded_logprob_list[(each_beam_idx + 1) * self.vocab_size - 1]\
+                        = -np.inf
+                    expanded_length_list[each_beam_idx * self.vocab_size : (each_beam_idx + 1) * self.vocab_size]\
+                        = len(each_candidate) + 1
+            score_list = np.array(expanded_logprob_list) / np.array(expanded_length_list)
+            if each_len == 0:
+                top_k_list = np.argsort(score_list[:self.vocab_size])[::-1][:beam_width]
+            else:
+                top_k_list = np.argsort(score_list)[::-1][:beam_width]
+            next_top_k_tgt_id_list = []
+            next_logprob_list = []
+            for each_top_k in top_k_list:
+                beam_idx = each_top_k // self.vocab_size
+                vocab_idx = each_top_k % self.vocab_size
+                if vocab_idx == self.vocab_size - 1:
+                    next_top_k_tgt_id_list.append(top_k_tgt_id_list[beam_idx])
+                    next_logprob_list.append(expanded_logprob_list[each_top_k])
+                else:
+                    next_top_k_tgt_id_list.append(top_k_tgt_id_list[beam_idx] + [vocab_idx])
+                    next_logprob_list.append(expanded_logprob_list[each_top_k])
+            top_k_tgt_id_list = next_top_k_tgt_id_list
+            logprob_list = next_logprob_list
+
+        # construct hypergraphs
+        hg_list = []
+        for each_tgt_id_list in top_k_tgt_id_list:
+            hg = None
+            stack = []
+            nt_edge = None
+            for each_idx, each_prod_rule_id in enumerate(each_tgt_id_list):
+                prod_rule = self.hrg.prod_rule_list[each_prod_rule_id]
+                hg, nt_edges = prod_rule.applied_to(hg, nt_edge)
+                stack.extend(nt_edges[::-1])
+                try:
+                    nt_edge = stack.pop()
+                except IndexError:
+                    if each_idx == len(each_tgt_id_list) - 1:
+                        break
+                    else:
+                        raise ValueError('some bugs')
+            hg_list.append(hg)
+        return hg_list
+
+    def graph_embed(self, x, edge_index, edge_attr, batch_size):
+        src_h = self.encoder.forward(x, edge_index, edge_attr, batch_size)
+        return src_h        
+
+    def encode(self, x, edge_index, edge_attr, batch_size):
+        #print("device for src_emb=", src_emb.get_device())
+        #print("device for self.encoder=", next(self.encoder.parameters()).get_device())
+        src_h = self.graph_embed(x, edge_index, edge_attr, batch_size)
+        mu, lv = self.get_mean_var(src_h)
+        return mu, lv
+    
+    def get_mean_var(self, src_h):
+        #src_h = torch.tanh(src_h)
+        mu = self.hidden2mean(src_h)
+        lv = self.hidden2logvar(src_h)
+        mu = torch.tanh(mu)
+        lv = torch.tanh(lv)
+        return mu, lv
+
+    def reparameterize(self, mu, logvar, training=True):
+        if training:
+            std = logvar.mul(0.5).exp_()
+            eps = Variable(std.data.new(std.size()).normal_())
+            if self.rank >= 0:
+                eps = eps.to(next(self.parameters()).device)
+            return eps.mul(std).add_(mu)
+        else:
+            return mu
+
+# Copied from the MHG implementation and adapted
+class GrammarVAELoss(_Loss):
+
+    '''
+    a loss function for Grammar VAE
+
+    Attributes
+    ----------
+    hrg : HyperedgeReplacementGrammar
+    beta : float
+        coefficient of KL divergence
+    '''
+
+    def __init__(self, rank, hrg, beta=1.0, **kwargs):
+        super().__init__(**kwargs)
+        self.hrg = hrg
+        self.beta = beta
+        self.rank = rank
+
+    def forward(self, mu, logvar, in_seq_pred, in_seq):
+        ''' compute VAE loss
+
+        Parameters
+        ----------
+        in_seq_pred : torch.Tensor, shape (batch_size, max_len, vocab_size)
+            logit
+        in_seq : torch.Tensor, shape (batch_size, max_len)
+            each element corresponds to a word id in vocabulary.
+        mu : torch.Tensor, shape (batch_size, hidden_dim)
+        logvar : torch.Tensor, shape (batch_size, hidden_dim)
+            mean and log variance of the normal distribution
+        '''
+        batch_size = in_seq_pred.shape[0]
+        max_len = in_seq_pred.shape[1]
+        vocab_size = in_seq_pred.shape[2]
+        mask = torch.zeros(in_seq_pred.shape)
+        
+        for each_batch in range(batch_size):
+            flag = True
+            for each_idx in range(max_len):
+                prod_rule_idx = in_seq[each_batch, each_idx]
+                if prod_rule_idx == vocab_size - 1:
+                    #### DETERMINE WHETHER THIS SHOULD BE SKIPPED OR NOT
+                    mask[each_batch, each_idx, prod_rule_idx] = 1
+                    #break
+                    continue
+                lhs = self.hrg.prod_rule_corpus.prod_rule_list[prod_rule_idx].lhs_nt_symbol
+                lhs_idx = self.hrg.prod_rule_corpus.nt_symbol_list.index(lhs)
+                mask[each_batch, each_idx, :-1] = torch.FloatTensor(self.hrg.prod_rule_corpus.lhs_in_prod_rule[lhs_idx])
+            if self.rank >= 0:
+                mask = mask.to(next(self.parameters()).device)
+        in_seq_pred = mask * in_seq_pred
+
+        cross_entropy = F.cross_entropy(
+            in_seq_pred.view(-1, vocab_size),
+            in_seq.view(-1),
+            reduction='sum',
+            #ignore_index=self.ignore_index if self.ignore_index is not None else -100
+            )
+        kl_div = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+        return cross_entropy + self.beta * kl_div
+
+
+class VAELoss(_Loss):
+    def __init__(self, beta=0.01):
+        super().__init__()
+        self.beta = beta
+
+    def forward(self, mean, log_var, dec_outputs, targets):
+
+        device = mean.get_device()
+        if device >= 0:
+            targets = targets.to(mean.get_device())
+        reconstruction = F.cross_entropy(dec_outputs.view(-1, dec_outputs.size(2)), targets.view(-1), reduction='sum')
+        
+        KL = 0.5 * torch.sum(1 + log_var - mean ** 2 - torch.exp(log_var))
+        loss = - self.beta * KL + reconstruction
+        return loss
diff --git a/models/mhg_model/notebooks/mhg-gnn_encoder_decoder_example.ipynb b/models/mhg_model/notebooks/mhg-gnn_encoder_decoder_example.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..84ff55ec04adaa87452caec876def45757592879
--- /dev/null
+++ b/models/mhg_model/notebooks/mhg-gnn_encoder_decoder_example.ipynb
@@ -0,0 +1,114 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "829ddc03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('..')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea820e23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import load"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9a51fa8",
+   "metadata": {},
+   "source": [
+    "# Load MHG-GNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6ea1fc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_ckp = \"models/model_checkpoints/mhg_model/pickles/mhggnn_pretrained_model_radius7_1116_2023.pickle\"\n",
+    "\n",
+    "model = load.load(model_name = model_ckp)\n",
+    "if model is None:\n",
+    "    print(\"Model not loaded, please check you have MHG pickle file\")\n",
+    "else:\n",
+    "    print(\"MHG model loaded\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4a0b557",
+   "metadata": {},
+   "source": [
+    "# Embeddings\n",
+    "\n",
+    "※ replace the smiles exaple list with your dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c63a6be6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    repr = model.encode([\"CCO\", \"O=C=O\", \"OC(=O)c1ccccc1C(=O)O\"])\n",
+    "    \n",
+    "# Print the latent vectors\n",
+    "print(repr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a59f9442",
+   "metadata": {},
+   "source": [
+    "# Decoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a0d8a41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig = model.decode(repr)\n",
+    "print(orig)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/models/mhg_model/paper/MHG-GNN_Combination of Molecular Hypergraph Grammar with Graph Neural Network.pdf b/models/mhg_model/paper/MHG-GNN_Combination of Molecular Hypergraph Grammar with Graph Neural Network.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..a7dcc1270d1f444f77366013ad2d3d93ebb426ab
Binary files /dev/null and b/models/mhg_model/paper/MHG-GNN_Combination of Molecular Hypergraph Grammar with Graph Neural Network.pdf differ
diff --git a/models/mhg_model/setup.cfg b/models/mhg_model/setup.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..12d7e158b8963bb101610dcb0a81fd3cc04eaae0
--- /dev/null
+++ b/models/mhg_model/setup.cfg
@@ -0,0 +1,37 @@
+[metadata]
+name = mhg-gnn
+version = attr: .__version__
+description = Package for mhg-gnn
+author= team
+long_description_content_type=text/markdown
+long_description = file: README.md
+python_requires = >= 3.9.7 
+license = TBD
+
+classifiers =
+    Programming Language :: Python :: 3
+    Programming Language :: Python :: 3.9
+
+[options]
+install_requires =
+	networkx>=2.8
+	numpy>=1.23.5, <2.0.0
+	pandas>=1.5.3
+	rdkit-pypi>=2022.9.4, <2023.9.6
+	torch>=2.0.0
+	torchinfo>=1.8.0
+	torch-geometric>=2.3.1
+	requests>=2.32.2
+	scikit-learn>=1.5.0
+	urllib3>=2.2.2
+
+
+setup_requires =
+    setuptools
+package_dir =
+    = .
+packages=find:
+include_package_data = True
+
+[options.packages.find]
+where = .
diff --git a/models/mhg_model/setup.py b/models/mhg_model/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..45f160da695819ef6906f6dd332e8398cf419b8e
--- /dev/null
+++ b/models/mhg_model/setup.py
@@ -0,0 +1,6 @@
+#!/usr/bin/env python
+
+import setuptools
+
+if __name__ == "__main__":
+    setuptools.setup()
\ No newline at end of file
diff --git a/models/selfies_model/README.md b/models/selfies_model/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..01f70f739727bf443957cdef04353175e0c4f47f
--- /dev/null
+++ b/models/selfies_model/README.md
@@ -0,0 +1,87 @@
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: feature-extraction
+tags:
+- chemistry
+---
+
+# selfies-ted
+
+selfies-ted is a project for encoding SMILES (Simplified Molecular Input Line Entry System) into SELFIES (SELF-referencing Embedded Strings) and generating embeddings for molecular representations.
+
+![selfies-ted](selfies-ted.png)
+## Model Architecture
+
+Configuration details
+
+Encoder and Decoder FFN dimensions: 256
+Number of attention heads: 4
+Number of encoder and decoder layers: 2
+Total number of hidden layers: 6
+Maximum position embeddings: 128
+Model dimension (d_model): 256
+
+## Pretrained Models and Training Logs
+We provide checkpoints of the selfies-ted model pre-trained on a dataset of molecules curated from PubChem. The pre-trained model shows competitive performance on molecular representation tasks. For model weights: "HuggingFace link".
+
+To install and use the pre-trained model:
+
+Download the selfies_ted_model.pkl file from the "HuggingFace link".
+Add the selfies-ted selfies_ted_model.pkl to the models/ directory. The directory structure should look like the following:
+
+```
+models/
+└── selfies_ted_model.pkl
+```
+
+## Installation
+
+To use this project, you'll need to install the required dependencies. We recommend using a virtual environment:
+
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows use `venv\Scripts\activate`
+```
+
+Install the required dependencies
+
+```
+pip install -r requirements.txt
+```
+
+
+## Usage
+
+### Import
+
+```
+import load
+```
+### Training the Model
+
+To train the model, use the train.py script:
+
+```
+python train.py -f <path_to_your_data_file>
+```
+
+
+Note: The actual usage may depend on the specific implementation in load.py. Please refer to the source code for detailed functionality.
+
+### Load the model and tokenizer
+```
+load.load("path/to/checkpoint.pkl")
+```
+### Encode SMILES strings
+```
+smiles_list = ["COC", "CCO"]
+```
+```
+embeddings = load.encode(smiles_list)
+```
+
+
+## Example Notebook
+
+Example notebook of this project is `selfies-ted-example.ipynb`. 
diff --git a/models/selfies_model/__pycache__/load.cpython-310.pyc b/models/selfies_model/__pycache__/load.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dac73a8c05644ec5d846f0d7254713d12d141572
Binary files /dev/null and b/models/selfies_model/__pycache__/load.cpython-310.pyc differ
diff --git a/models/selfies_model/load.py b/models/selfies_model/load.py
new file mode 100644
index 0000000000000000000000000000000000000000..929e54cc2f62c89b883252d96366d1a8e1ec13c1
--- /dev/null
+++ b/models/selfies_model/load.py
@@ -0,0 +1,96 @@
+import os
+import sys
+import torch
+import selfies as sf  # selfies>=2.1.1
+import pickle
+import pandas as pd
+import numpy as np
+from datasets import Dataset
+from rdkit import Chem
+from transformers import AutoTokenizer, AutoModel
+
+
+class SELFIES(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.model = None
+        self.tokenizer = None
+        self.invalid = []
+
+    def get_selfies(self, smiles_list):
+        self.invalid = []
+        spaced_selfies_batch = []
+        for i, smiles in enumerate(smiles_list):
+            try:
+                selfies = sf.encoder(smiles.rstrip())
+            except:
+                try:
+                    smiles = Chem.MolToSmiles(Chem.MolFromSmiles(smiles.rstrip()))
+                    selfies = sf.encoder(smiles)
+                except:
+                    selfies = "[]"
+                    self.invalid.append(i)
+
+            spaced_selfies_batch.append(selfies.replace('][', '] ['))
+
+        return spaced_selfies_batch
+
+
+    def get_embedding(self, selfies):
+        encoding = self.tokenizer(selfies["selfies"], return_tensors='pt', max_length=128, truncation=True, padding='max_length')
+        input_ids = encoding['input_ids']
+        attention_mask = encoding['attention_mask']
+        outputs = self.model.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        model_output = outputs.last_hidden_state
+
+        input_mask_expanded = attention_mask.unsqueeze(-1).expand(model_output.size()).float()
+        sum_embeddings = torch.sum(model_output * input_mask_expanded, 1)
+        sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+        model_output = sum_embeddings / sum_mask
+
+        encoding["embedding"] = model_output
+
+        return encoding
+
+
+    def load(self, checkpoint="bart-2908.pickle"):
+        """
+            inputs :
+                   checkpoint (pickle object)
+        """
+
+        self.tokenizer = AutoTokenizer.from_pretrained("ibm/materials.selfies-ted")
+        self.model = AutoModel.from_pretrained("ibm/materials.selfies-ted")
+        """if os.path.isfile(checkpoint):
+            with open(checkpoint, "rb") as input_file:
+                self.model, self.tokenizer = pickle.load(input_file)
+        for p in sys.path:
+            file = p + "/" + checkpoint
+            if os.path.isfile(file):
+                with open(file, "rb") as input_file:
+                    self.model, self.tokenizer = pickle.load(input_file)"""
+
+
+
+
+    # TODO: remove `use_gpu` argument in validation pipeline
+    def encode(self, smiles_list=[], use_gpu=False, return_tensor=False):
+        """
+            inputs :
+                   checkpoint (pickle object)
+            :return: embedding
+        """
+        selfies = self.get_selfies(smiles_list)
+        selfies_df = pd.DataFrame(selfies,columns=["selfies"])
+        data = Dataset.from_pandas(selfies_df)
+        embedding = data.map(self.get_embedding, batched=True, num_proc=1, batch_size=128)
+        emb = np.asarray(embedding["embedding"].copy())
+
+        for idx in self.invalid:
+            emb[idx] = np.nan
+            print("Cannot encode {0} to selfies and embedding replaced by NaN".format(smiles_list[idx]))
+
+        if return_tensor:
+            return torch.tensor(emb)
+        return pd.DataFrame(emb)
diff --git a/models/selfies_model/requirements.txt b/models/selfies_model/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9183360cca79111e2e64fe4b65849e4df75c195f
--- /dev/null
+++ b/models/selfies_model/requirements.txt
@@ -0,0 +1,12 @@
+torch>=2.1.0
+transformers>=4.38
+numpy>=1.26.1
+datasets>=2.13.1
+evaluate>=0.4.0
+selfies>=2.1.0
+scikit-learn>=1.2.1
+pyarrow>=14.0.1
+requests>=2.31.0
+urllib3>=2.0.7
+aiohttp>=3.9.0
+zipp>=3.17.0
\ No newline at end of file
diff --git a/models/selfies_model/selfies-ted-example.ipynb b/models/selfies_model/selfies-ted-example.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..856f98cde351f6fa5b3fcfdebd2c5ad6726fc380
--- /dev/null
+++ b/models/selfies_model/selfies-ted-example.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9d9b6eb8-9edb-44bd-9e5a-3a6ea67f5117",
+   "metadata": {},
+   "source": [
+    "### Import library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c3ac4418",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from load import SELFIES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "790061cf-5470-4564-987e-aa2e492337db",
+   "metadata": {},
+   "source": [
+    "### Initialize and load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "85847f26-e2f4-475a-a88e-41fd9cccfc0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = SELFIES()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "095e864c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model.load(checkpoint=\"bart-2908.pickle\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55f1a68c-c462-4dee-9139-9befb469f176",
+   "metadata": {},
+   "source": [
+    "### Example to get embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2357ef0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b494cbf9878a4f5c8f4093e38fb82fd5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/3 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "smiles_list = [\"CCO\", \"O=C=O\", \"OC(=O)c1ccccc1C(=O)O\"]\n",
+    "embeddings = model.encode(smiles_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3871c513-d0a9-4e70-9c18-3f0b491e07b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 1024)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embeddings.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "289a8795-d6d8-4828-b2b2-b4d4a97a4604",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/models/selfies_model/selfies-ted.png b/models/selfies_model/selfies-ted.png
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index 0000000000000000000000000000000000000000..a71127e0a6baf0110d8074e63a08ba060c931121
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diff --git a/models/smi_ted/smi_ted_light/.DS_Store b/models/smi_ted/smi_ted_light/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..de7bbc77aa6dc3f5ffc19d37275ecbb2cce50280
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diff --git a/models/smi_ted/smi_ted_light/__pycache__/load.cpython-310.pyc b/models/smi_ted/smi_ted_light/__pycache__/load.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8c3ce8cd7211637ceff37b78cb08365c96eb4296
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diff --git a/models/smi_ted/smi_ted_light/bert_vocab_curated.txt b/models/smi_ted/smi_ted_light/bert_vocab_curated.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4de43b6c87add8821aa8d2d33d58232929c86fbd
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/bert_vocab_curated.txt
@@ -0,0 +1,2393 @@
+<bos>
+<eos>
+<pad>
+<mask>
+C
+c
+(
+)
+1
+O
+N
+2
+=
+n
+3
+[C@H]
+[C@@H]
+F
+S
+4
+Cl
+-
+o
+s
+[nH]
+#
+/
+Br
+[C@]
+[C@@]
+[N+]
+[O-]
+5
+\
+.
+I
+6
+[S@]
+[S@@]
+P
+[N-]
+[Si]
+7
+[n+]
+[2H]
+8
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+%13
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+[NH-]
+%14
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+[U+2]
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+[Zr+2]
+[3H]
+[Ge]
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+[Cu+2]
+[P@@]
+p
+[Pt]
+%16
+[Ca+2]
+[Zr]
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+[C+]
+[Ti]
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+[V]
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+[Pd+2]
+[Cu]
+%17
+[Cu+]
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+[Li]
+[Pd]
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+[Ac]
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+[AlH2]
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+[85Kr]
+[110Ag]
+[58Co]
+[241Pu]
+[234Th]
+[140La]
+[63Ni]
+[152Eu]
+[132IH]
+[226Rn]
+[154Eu]
+[36ClH]
+[228Ac]
+[155Eu]
+[106Rh]
+[243Am]
+[227Ac]
+[243Cm]
+[236U]
+[144Pr]
+[232U]
+[32SH2]
+[88Y]
+[82BrH]
+[135IH]
+[242Cm]
+[115Cd]
+[242Pu]
+[46Sc]
+[56Mn]
+[234Pa]
+[41Ar]
+[147Nd]
+[187W]
+[151Sm]
+[59Ni]
+[233Pa]
+[52Mn]
+[94Nb]
+[219Rn]
+[236Pu]
+[13NH3]
+[93Zr]
+[51Cr+6]
+[TlH3]
+[123Xe]
+[160Tb]
+[170Tm]
+[182Ta]
+[175Yb]
+[93Mo]
+[143Ce]
+[191Os]
+[126IH]
+[48V]
+[113Cd]
+[47Sc]
+[181Hf]
+[185W]
+[143Pr]
+[191Pt]
+[181W]
+[33PH3]
+[97Ru]
+[97Tc]
+[111Ag]
+[169Er]
+[107Pd]
+[103Ru+2]
+[34SH2]
+[137Ce]
+[242Am]
+[117SnH2]
+[57Ni]
+[239U]
+[60Cu]
+[250Cf]
+[193Au]
+[69Zn]
+[55Co]
+[139Ce]
+[127Xe]
+[159Gd]
+[56Co]
+[177Hf]
+[244Pu]
+[38ClH]
+[142Pr]
+[199Hg]
+[179Hf]
+[178Hf]
+[237U]
+[156Eu]
+[157Eu]
+[105Ru]
+[171Tm]
+[199Au]
+[155Sm]
+[80BrH]
+[108Ag]
+[128IH]
+[48Sc]
+[45Ti]
+[176Lu]
+[121SnH2]
+[148Pm]
+[57Fe]
+[10BH3]
+[96Tc]
+[133IH]
+[143Pm]
+[105Rh]
+[130IH]
+[134IH]
+[131IH]
+[71Zn]
+[105Ag]
+[97Zr]
+[235Pu]
+[231Th]
+[109Pd]
+[93Y]
+[190Ir]
+[135Xe]
+[53Mn]
+[134Ce]
+[234Np]
+[240Am]
+[246Cf]
+[240Cm]
+[241Cm]
+[226Th]
+[39ClH]
+[229Th]
+[245Cm]
+[240U]
+[240Np]
+[249Cm]
+[243Pu]
+[145Pm]
+[199Pt]
+[246Bk]
+[193Pt]
+[230U]
+[250Cm]
+[44Ti]
+[175Hf]
+[254Fm]
+[255Fm]
+[257Fm]
+[92Y]
+[188Ir]
+[171Lu]
+[257Md]
+[247Bk]
+[121IH]
+[250Bk]
+[179Lu]
+[224Ac]
+[195Hg]
+[244Am]
+[246Pu]
+[194Au]
+[252Fm]
+[173Hf]
+[246Cm]
+[135Ce]
+[49Cr]
+[248Cf]
+[247Cm]
+[248Cm]
+[174Ta]
+[176Ta]
+[154Tb]
+[172Ta]
+[177Ta]
+[175Ta]
+[180Ta]
+[158Tb]
+[115Ag]
+[189Os]
+[251Cf]
+[145Pr]
+[147Pr]
+[76BrH]
+[102Rh]
+[238Np]
+[185Os]
+[246Am]
+[233Np]
+[166Dy]
+[254Es]
+[244Cf]
+[193Os]
+[245Am]
+[245Bk]
+[239Am]
+[238Am]
+[97Nb]
+[245Pu]
+[254Cf]
+[188W]
+[250Es]
+[251Es]
+[237Am]
+[182Hf]
+[258Md]
+[232Np]
+[238Cm]
+[60Fe]
+[109Pd+2]
+[234Pu]
+[141Ce+3]
+[136Nd]
+[136Pr]
+[173Ta]
+[110Ru]
+[147Tb]
+[253Fm]
+[139Nd]
+[178Re]
+[177Re]
+[200Au]
+[182Re]
+[156Tb]
+[155Tb]
+[157Tb]
+[161Tb]
+[161Ho]
+[167Tm]
+[173Lu]
+[179Ta]
+[171Er]
+[44Sc]
+[49Sc]
+[49V]
+[51Mn]
+[90Nb]
+[88Nb]
+[88Zr]
+[36SH2]
+[174Yb]
+[178Lu]
+[179W]
+[83BrH]
+[107Cd]
+[75BrH]
+[62Co]
+[48Cr]
+[63Zn]
+[102Ag]
+[154Sm]
+[168Er]
+[65Ni]
+[137La]
+[187Ir]
+[144Pm]
+[146Pm]
+[160Gd]
+[166Yb]
+[162Dy]
+[47V]
+[141Nd]
+[141Sm]
+[166Er]
+[150Sm]
+[146Eu]
+[149Eu]
+[174Lu]
+[17NH3]
+[102Ru]
+[170Hf]
+[188Pt]
+[61Ni]
+[56Ni]
+[149Gd]
+[151Gd]
+[141Pm]
+[147Gd]
+[146Gd]
+[161Er]
+[103Ag]
+[145Eu]
+[153Tb]
+[155Dy]
+[184Re]
+[180Os]
+[182Os]
+[186Pt]
+[181Os]
+[181Re]
+[151Tb]
+[178Ta]
+[178W]
+[189Pt]
+[194Hg]
+[145Sm]
+[150Tb]
+[132La]
+[158Gd]
+[104Ag]
+[193Hg]
+[94Ru]
+[137Pr]
+[155Ho]
+[117Cd]
+[99Ru]
+[146Nd]
+[218Rn]
+[95Y]
+[79Kr]
+[120IH]
+[138Pr]
+[100Pd]
+[166Tm]
+[90Mo]
+[151Nd]
+[231U]
+[138Nd]
+[89Nb]
+[98Nb]
+[162Ho]
+[142Sm]
+[186Ta]
+[104Tc]
+[184Ta]
+[185Ta]
+[170Er]
+[107Rh]
+[131La]
+[169Lu]
+[74BrH]
+[150Pm]
+[172Tm]
+[197Pt]
+[230Pu]
+[170Lu]
+[86Zr]
+[176W]
+[177W]
+[101Pd]
+[105Pd]
+[108Pd]
+[149Nd]
+[164Ho]
+[159Ho]
+[167Ho]
+[176Yb]
+[156Sm]
+[77BrH]
+[189Re]
+[99Rh]
+[100Rh]
+[151Pm]
+[232Pa]
+[228Pa]
+[230Pa]
+[66Ni]
+[194Os]
+[135La]
+[138La]
+[141La]
+[142La]
+[195Ir]
+[96Nb]
+[157Ho]
+[183Hf]
+[162Tm]
+[172Er]
+[148Eu]
+[150Eu]
+[15CH4]
+[89Kr]
+[143La]
+[58Ni]
+[61Co]
+[158Eu]
+[165Er]
+[167Yb]
+[173Tm]
+[175Tm]
+[172Hf]
+[172Lu]
+[93Tc]
+[177Yb]
+[124IH]
+[194Ir]
+[147Eu]
+[101Mo]
+[180Hf]
+[189Ir]
+[87Y]
+[43Sc]
+[195Au]
+[112Ag]
+[84BrH]
+[106Ag]
+[109Ag]
+[101Rh]
+[162Yb]
+[228Rn]
+[139Pr]
+[94Y]
+[201Au]
+[40PH3]
+[110Ag+]
+[104Cd]
+[133Ba+2]
+[226Ac]
+[145Gd]
+[186Ir]
+[184Ir]
+[224Rn]
+[185Ir]
+[182Ir]
+[184Hf]
+[200Pt]
+[227Pa]
+[178Yb]
+[72Br-]
+[72BrH]
+[248Am]
+[238Th]
+[161Gd]
+[35S-2]
+[107Ag]
+[FeH6-4]
+[89Sr]
+[SnH3-]
+[SeH3]
+[TeH3+]
+[SbH4+]
+[AsH4+]
+[4He]
+[AsH3-]
+[1HH]
+[3H+]
+[82Rb]
+[85Sr]
+[90Sr]
+[137Cs]
+[133Ba]
+[131Cs]
+[SbH5]
+[224Ra]
+[22Na]
+[210Bi]
+[214Bi]
+[228Ra]
+[127Sb]
+[136Cs]
+[125Sb]
+[134Cs]
+[140Ba]
+[45Ca]
+[206Pb]
+[207Pb]
+[24Na]
+[86Rb]
+[212Bi]
+[208Pb]
+[124Sb]
+[204Pb]
+[44K]
+[129Te]
+[113Sn]
+[204Tl]
+[87Sr]
+[208Tl]
+[87Rb]
+[47Ca]
+[135Cs]
+[216Po]
+[137Ba]
+[207Bi]
+[212Po]
+[79Se]
+[223Ra]
+[86Sr]
+[122Sb]
+[26Al]
+[32Si]
+[126Sn]
+[225Ra]
+[114In]
+[72Ga]
+[132Te]
+[10Be]
+[125Sn]
+[73As]
+[206Bi]
+[117Sn]
+[40Ca]
+[41Ca]
+[89Rb]
+[116In]
+[129Sb]
+[91Sr]
+[71Ge]
+[139Ba]
+[69Ga]
+[120Sb]
+[121Sn]
+[123Sn]
+[131Te]
+[77Ge]
+[135Ba]
+[82Sr]
+[43K]
+[131Ba]
+[92Sr]
+[88Rb]
+[129Cs]
+[144Cs]
+[127Cs]
+[200Tl]
+[202Tl]
+[141Ba]
+[117Sb]
+[116Sb]
+[78As]
+[131Sb]
+[126Sb]
+[128Sb]
+[130Sb]
+[67Ge]
+[68Ge]
+[78Ge]
+[66Ge]
+[223Fr]
+[132Cs]
+[125Cs]
+[138Cs]
+[133Te]
+[84Rb]
+[83Rb]
+[81Rb]
+[142Ba]
+[200Bi]
+[115Sb]
+[194Tl]
+[70Se]
+[112In]
+[118Sb]
+[70Ga]
+[27Mg]
+[202Bi]
+[83Se]
+[9Li]
+[69As]
+[79Rb]
+[81Sr]
+[83Sr]
+[78Se]
+[109In]
+[29Al]
+[118Sn]
+[117In]
+[119Sb]
+[114Sn]
+[138Ba]
+[69Ge]
+[73Ga]
+[74Ge]
+[206Tl]
+[199Tl]
+[130Cs]
+[28Mg]
+[116Te]
+[112Sn]
+[126Ba]
+[211Bi]
+[81Se]
+[127Sn]
+[143Cs]
+[134Te]
+[80Sr]
+[45K]
+[215Po]
+[207Po]
+[111Sn]
+[211Po]
+[128Ba]
+[198Tl]
+[227Ra]
+[213Po]
+[220Ra]
+[128Sn]
+[203Po]
+[205Po]
+[65Ga]
+[197Tl]
+[88Sr]
+[110In]
+[31Si]
+[201Bi]
+[121Te]
+[205Bi]
+[203Bi]
+[195Tl]
+[209Tl]
+[110Sn]
+[222Fr]
+[207At]
+[119In]
+[As@]
+[129IH]
+[157Dy]
+[111IH]
+[230Ra]
+[144Pr+3]
+[SiH3+]
+[3He]
+[AsH5]
+[72Se]
+[95Tc]
+[103Pd]
+[121Sn+2]
+[211Rn]
+[38SH2]
+[127IH]
+[74Br-]
+[133I-]
+[100Tc+4]
+[100Tc]
+[36Cl-]
+[89Y+3]
+[104Rh]
+[152Sm]
+[226Ra]
+[19FH]
+[104Pd]
+[148Gd]
+[157Lu]
+[33SH2]
+[121I-]
+[17FH]
+[71Se]
+[157Sm]
+[148Tb]
+[164Dy]
+[15OH2]
+[15O+]
+[39K]
+[40Ar]
+[50Cr+3]
+[50Cr]
+[52Ti]
+[103Pd+2]
+[130Ba]
+[142Pm]
+[153Gd+3]
+[151Eu]
+[103Rh]
+[124Xe]
+[152Tb]
+[17OH2]
+[20Ne]
+[52Fe]
+[94Zr+4]
+[94Zr]
+[149Pr]
+[16OH2]
+[53Cr+6]
+[53Cr]
+[81Br-]
+[112Pd]
+[125Xe]
+[155Gd]
+[157Gd]
+[168Yb]
+[184Os]
+[166Tb]
+[221Fr]
+[212Ra]
+[75Br-]
+[79Br-]
+[113Ag]
+[23Na]
+[34Cl-]
+[34ClH]
+[38Cl-]
+[56Fe]
+[68Cu]
+[77Br-]
+[90Zr+4]
+[90Zr]
+[102Pd]
+[154Eu+3]
+[57Mn]
+[165Tm]
+[152Dy]
+[217At]
+[77se]
+[13cH-]
+[122Te]
+[156Gd]
+[124Te]
+[53Ni]
+[131Xe]
+[174Hf+4]
+[174Hf]
+[76Se]
+[168Tm]
+[167Dy]
+[154Gd]
+[95Ru]
+[210At]
+[85Br]
+[59Co]
+[122Xe]
+[27Al]
+[54Cr]
+[198Hg]
+[85Rb+]
+[214Tl]
+[229Rn]
+[218Pb]
+[218Bi]
+[167Tm+3]
+[18o+]
+[P@@H+]
+[P@H+]
+[13N+]
+[212Pb+2]
+[217Bi]
+[249Cf+2]
+[18OH3+]
+[90Sr-]
+[Cf+3]
+[200Hg]
+[86Tc]
+[141Pr+3]
+[141Pr]
+[16nH]
+[14NH4+]
+[132Xe]
+[83Kr]
+[70Zn+2]
+[137Ba+2]
+[36Ar]
+[38Ar]
+[21Ne]
+[126Xe]
+[136Xe]
+[128Xe]
+[134Xe]
+[84Kr]
+[86Kr]
+[78Kr]
+[80Kr]
+[82Kr]
+[67Zn+2]
+[65Cu+2]
+[110Te]
+[58Fe+3]
+[142Nd]
+[38K]
+[198Au+3]
+[122IH]
+[38PH3]
+[130I-]
+[40K+]
+[38K+]
+[28Mg+2]
+[208Tl+]
+[13OH2]
+[198Bi]
+[192Bi]
+[194Bi]
+[196Bi]
+[132I-]
+[83Sr+2]
+[169Er+3]
+[122I-]
+[120I-]
+[92Sr+2]
+[126I-]
+[24Mg]
+[84Sr]
+[118Pd+2]
+[118Pd]
+[AsH4]
+[127I-]
+[9C-]
+[11CH3+]
+[17B]
+[7B]
+[4HH]
+[18C-]
+[22CH3-]
+[22CH4]
+[17C-]
+[15CH3]
+[16CH3]
+[11NH3]
+[21NH3]
+[11N-]
+[11NH]
+[16CH]
+[17CH2]
+[99Ru+2]
+[181Ta+2]
+[181Ta]
+[20CH]
+[32PH2]
+[55Fe+2]
+[SH3]
+[S@H]
+[Mn-]
+[IH4]
+[ThH]
+[GaH-]
+[BiH+]
+[EuH2]
+[FeH4-3]
+[FeH6]
+[IH5]
+[NiH+]
+[SrH2]
+[VH4]
+[YH3]
+[seH+]
+<unk>
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/.DS_Store b/models/smi_ted/smi_ted_light/fast_transformers/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5f406d85ffa41d520b23db8798ef9c3d7286dfbe
Binary files /dev/null and b/models/smi_ted/smi_ted_light/fast_transformers/.DS_Store differ
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d06d627f926ad2fc356fd2dbf13db2058c52421
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/__init__.py
@@ -0,0 +1,15 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Provide a library with fast transformer implementations."""
+
+__author__ = "Angelos Katharopoulos, Apoorv Vyas"
+__copyright__ = "Copyright (c) 2020 Idiap Research Institute"
+__license__ = "MIT"
+__maintainer__ = "Angelos Katharopoulos, Apoorv Vyas"
+__email__ = "angelos.katharopoulos@idiap.ch, avyas@idiap.ch"
+__url__ = "https://github.com/idiap/fast-transformers"
+__version__ = "0.4.0"
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/__pycache__/__init__.cpython-310.pyc b/models/smi_ted/smi_ted_light/fast_transformers/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..73af6af2c2d83909634abd923e502bd7dbfa5a92
Binary files /dev/null and b/models/smi_ted/smi_ted_light/fast_transformers/__pycache__/__init__.cpython-310.pyc differ
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/__pycache__/masking.cpython-310.pyc b/models/smi_ted/smi_ted_light/fast_transformers/__pycache__/masking.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..23179123309f7da7bffe8caf5120a365a24ba099
Binary files /dev/null and b/models/smi_ted/smi_ted_light/fast_transformers/__pycache__/masking.cpython-310.pyc differ
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/__pycache__/transformers.cpython-310.pyc b/models/smi_ted/smi_ted_light/fast_transformers/__pycache__/transformers.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ea3385b92f1105d27e7196a786f8d96b758cec55
Binary files /dev/null and b/models/smi_ted/smi_ted_light/fast_transformers/__pycache__/transformers.cpython-310.pyc differ
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/aggregate/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/aggregate/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e84039d37674f6858878f5b3fe3c7f91a41ba3
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/aggregate/__init__.py
@@ -0,0 +1,128 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+
+import torch
+
+from .aggregate_cpu import aggregate as aggregate_cpu, \
+    broadcast as broadcast_cpu
+try:
+    from .aggregate_cuda import aggregate as aggregate_gpu, \
+        broadcast as broadcast_gpu
+    from .clustered_aggregate_cuda import \
+        clustered_broadcast as clustered_broadcast_gpu, \
+        clustered_aggregate as clustered_aggregate_gpu
+
+except ImportError:
+    pass
+
+
+def aggregate(X, G, F, Y=None):
+    device = X.device
+    if Y is None:
+        Y = torch.zeros(
+            F.shape + (X.shape[-1],),
+            device=device,
+            dtype=X.dtype
+        )
+    else:
+        Y.zero_()
+
+    if device.type == "cpu":
+        aggregate_cpu(X, G, F, Y)
+    else:
+        aggregate_gpu(X, G, F, Y)
+
+    return Y
+
+
+def broadcast(Y, G, F, X=None):
+    device = Y.device
+    if X is None:
+        X = torch.zeros(
+            G.shape + (Y.shape[-1],),
+            device=device,
+            dtype=Y.dtype
+        )
+
+    if device.type == "cpu":
+        broadcast_cpu(Y, G, F, X)
+    else:
+        broadcast_gpu(Y, G, F, X)
+
+    return X
+
+
+# Divide the cluster into groups of equal size
+# as constrained by the shared memory
+def set_group(C, E):
+    C_per_block = int(192 * 64 / (E+1))
+    G_min = (C + C_per_block - 1) // C_per_block
+    for G in range(G_min, C+1):
+        if C % G == 0:
+            return G
+
+
+def clustered_broadcast(Y, groups, counts, factors, X=None):
+    device = Y.device
+    if X is None:
+        X = torch.zeros(
+            groups.shape + (Y.shape[-1],),
+            device=device,
+            dtype=Y.dtype
+        )
+    if device.type == "cpu":
+        broadcast_cpu(Y, groups, factors, X)
+    else:
+        N, H, C, E = Y.shape
+        _, _, L, _ = X.shape
+
+        # Following are some booking keeping parameters to facilitate the
+        # broadcast kernel that takes advantage of clustering
+        # More information can be found in the cuda file
+        with torch.no_grad():
+            threads = 256
+            G = set_group(C, E)
+            group_counts = counts.view(N, H, G, -1).sum(-1)
+            block_counts = (group_counts + threads - 1) // threads
+            total_blocks = block_counts.sum().item()
+            indx_maps = torch.ones(
+                (total_blocks, 5),
+                device=X.device,
+                dtype=torch.int32
+            )
+
+        clustered_broadcast_gpu(
+            Y,
+            groups,
+            factors,
+            X,
+            block_counts.int(),
+            group_counts.int(),
+            threads,
+            G,
+            total_blocks,
+            indx_maps
+        )
+    return X
+
+
+def clustered_aggregate(X, G, F, lengths, Y=None):
+    device = X.device
+    if Y is None:
+        Y = torch.zeros(
+            F.shape + (X.shape[-1],),
+            device=device,
+            dtype=X.dtype
+        )
+    else:
+        Y.zero_()
+
+    if device.type == "cpu":
+        aggregate_cpu(X, G, F, Y)
+    else:
+        clustered_aggregate_gpu(X, G, F, lengths, Y)
+    return Y
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/aggregate/aggregate_cpu.cpython-39-x86_64-linux-gnu.so b/models/smi_ted/smi_ted_light/fast_transformers/aggregate/aggregate_cpu.cpython-39-x86_64-linux-gnu.so
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b602161ecdb10f3e3597463d9764a8a20ea12787
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/__init__.py
@@ -0,0 +1,20 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implementations of different types of attention mechanisms."""
+
+
+from .attention_layer import AttentionLayer
+from .full_attention import FullAttention
+from .linear_attention import LinearAttention
+#from .causal_linear_attention import CausalLinearAttention
+#from .clustered_attention import ClusteredAttention
+#from .improved_clustered_attention import ImprovedClusteredAttention
+#from .reformer_attention import ReformerAttention
+#from .conditional_full_attention import ConditionalFullAttention
+#from .exact_topk_attention import ExactTopKAttention
+#from .improved_clustered_causal_attention import ImprovedClusteredCausalAttention
+#from .local_attention import LocalAttention
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/attention_layer.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/attention_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9233c5bc461b185b7385fed190981da0df2ceab
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/attention_layer.py
@@ -0,0 +1,113 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""The base attention layer performs all the query key value projections and
+output projections leaving the implementation of the attention to the inner
+attention module.
+
+The transformer layers, however, are agnostic of the attention implementation
+and any layer that implements the same interface can substitute for the
+attention layer.
+"""
+
+from torch.nn import Linear, Module
+
+from ..events import EventDispatcher, QKVEvent
+
+
+class AttentionLayer(Module):
+    """Implement the attention layer. Namely project the inputs to multi-head
+    queries, keys and values, call the attention implementation and then
+    reproject the output.
+
+    It can be thought of as a decorator (see decorator design patter) of an
+    attention layer.
+
+    Arguments
+    ---------
+        attention: Specific inner attention implementation that just computes a
+                   weighted average of values given a similarity of queries and
+                   keys.
+        d_model: The input feature dimensionality
+        n_heads: The number of heads for the multi head attention
+        d_keys: The dimensionality of the keys/queries
+                (default: d_model/n_heads)
+        d_values: The dimensionality of the values (default: d_model/n_heads)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, attention, d_model, n_heads, d_keys=None,
+                 d_values=None, event_dispatcher=""):
+        super(AttentionLayer, self).__init__()
+
+        # Fill d_keys and d_values
+        d_keys = d_keys or (d_model//n_heads)
+        d_values = d_values or (d_model//n_heads)
+
+        self.inner_attention = attention
+        self.query_projection = Linear(d_model, d_keys * n_heads)
+        self.key_projection = Linear(d_model, d_keys * n_heads)
+        self.value_projection = Linear(d_model, d_values * n_heads)
+        self.out_projection = Linear(d_values * n_heads, d_model)
+        self.n_heads = n_heads
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        """Apply attention to the passed in queries/keys/values after
+        projecting them to multiple heads.
+
+        In the argument description we make use of the following sizes
+
+            - N: the batch size
+            - L: The maximum length of the queries
+            - S: The maximum length of the keys (the actual length per sequence
+              is given by the length mask)
+            - D: The input feature dimensionality passed in the constructor as
+              'd_model'
+
+        Arguments
+        ---------
+            queries: (N, L, D) The tensor containing the queries
+            keys: (N, S, D) The tensor containing the keys
+            values: (N, S, D) The tensor containing the values
+            attn_mask: An implementation of BaseMask that encodes where each
+                       query can attend to
+            query_lengths: An implementation of  BaseMask that encodes how
+                           many queries each sequence in the batch consists of
+            key_lengths: An implementation of BaseMask that encodes how
+                         many queries each sequence in the batch consists of
+
+        Returns
+        -------
+            The new value for each query as a tensor of shape (N, L, D).
+        """
+        # Extract the dimensions into local variables
+        N, L, _ = queries.shape
+        _, S, _ = keys.shape
+        H = self.n_heads
+
+        # Project the queries/keys/values
+        queries = self.query_projection(queries).view(N, L, H, -1)
+        keys = self.key_projection(keys).view(N, S, H, -1)
+        values = self.value_projection(values).view(N, S, H, -1)
+
+        # Let the world know of the qkv
+        self.event_dispatcher.dispatch(QKVEvent(self, queries, keys, values))
+
+        # Compute the attention
+        new_values = self.inner_attention(
+            queries,
+            keys,
+            values,
+            attn_mask,
+            query_lengths,
+            key_lengths
+        ).view(N, L, -1)
+
+        # Project the output and return
+        return self.out_projection(new_values)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/causal_linear_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/causal_linear_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..31736ee61f2452bfeaed9fe86e4025f7257ad851
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/causal_linear_attention.py
@@ -0,0 +1,116 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement causally masked linear attention."""
+
+import torch
+from torch.nn import Module
+
+from ..attention_registry import AttentionRegistry, Optional, Callable, Int, \
+    EventDispatcherInstance
+from ..events import EventDispatcher
+from ..causal_product import causal_dot_product
+from ..feature_maps import elu_feature_map
+
+
+def causal_linear(Q, K, V):
+    Q = Q.permute(0,2,1,3).contiguous()
+    K = K.permute(0,2,1,3).contiguous()
+    V = V.permute(0,2,1,3).contiguous()
+    V_new = causal_dot_product(Q, K, V)
+    return V_new.permute(0,2,1,3).contiguous()
+
+
+class CausalLinearAttention(Module):
+    """Implement causally masked attention using dot product of feature maps in
+    O(N D^2) complexity.
+
+    See fast_transformers.attention.linear_attention.LinearAttention for the
+    general concept of replacing the softmax with feature maps. In addition to
+    that, we also make use of the fact that causal masking is a triangular mask
+    which allows us to apply the masking and still compute the attention in O(N
+    D^2) complexity.
+
+    Arguments
+    ---------
+        feature_map: callable, a callable that applies the feature map to the
+                     last dimension of a tensor (default: elu(x)+1)
+        eps: float, a small number to ensure the numerical stability of the
+             denominator (default: 1e-6)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, query_dimensions, feature_map=None, eps=1e-6,
+                 event_dispatcher=""):
+        super(CausalLinearAttention, self).__init__()
+        self.feature_map = (
+            feature_map(query_dimensions) if feature_map else
+            elu_feature_map(query_dimensions)
+        )
+        self.eps = eps
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def _make_sizes_compatible(self, Q, K):
+        """Either slice or pad K in case that the sizes do not match between Q
+        and K."""
+        N, L, H, E = Q.shape
+        _, S, _, _ = K.shape
+        if L == S:
+            return Q, K
+
+        if L < S:
+            return Q, K[:, :L, :, :]
+
+        if L > S:
+            return Q, torch.cat([K, K.new_zeros(N, L-S, H, E)], dim=1)
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        # Apply the feature map to the queries and keys
+        self.feature_map.new_feature_map(queries.device)
+        Q = self.feature_map.forward_queries(queries)
+        K = self.feature_map.forward_keys(keys)
+
+        # Apply the key padding mask and make sure the attn_mask is a
+        # lower triangular causal mask
+        if not attn_mask.lower_triangular:
+            raise RuntimeError(("CausalLinearAttention only supports full "
+                                "lower triangular masks"))
+        K = K * key_lengths.float_matrix[:, :, None, None]
+
+        # Ensure that Q and K have compatible sizes for the following
+        # computations, namely L == S
+        Q, K = self._make_sizes_compatible(Q, K)
+
+        # TODO: Shall we divide the Q and K with a relatively large number to
+        #       avoid numerical instabilities in computing the denominator?
+        #       We used to divide each with the max norm of all q and k but
+        #       that seems relatively costly for a simple normalization.
+
+        # Compute the normalizers
+        Z = 1/(torch.einsum("nlhi,nlhi->nlh", Q, K.cumsum(1)) + self.eps)
+
+        # Compute the unnormalized result
+        V = causal_linear(
+            Q,
+            K,
+            values
+        )
+
+        return V * Z[:, :, :, None]
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "causal-linear", CausalLinearAttention,
+    [
+        ("query_dimensions", Int),
+        ("feature_map", Optional(Callable)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/clustered_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/clustered_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..634fafd88727543de44807412be0defc10308dbc
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/clustered_attention.py
@@ -0,0 +1,195 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement clustered self attention."""
+
+from math import sqrt
+
+import torch
+import torch.autograd
+from torch.nn import Dropout, Module
+from torch.nn.init import normal_
+
+from ..attention_registry import AttentionRegistry, Optional, Float, Int, \
+    Bool, EventDispatcherInstance
+from ..events import EventDispatcher
+from ..masking import FullMask
+from ..aggregate import clustered_aggregate, clustered_broadcast
+from ..clustering.hamming import cluster
+from ..hashing import compute_hashes
+
+
+class _GroupQueries(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, Q, clusters, counts, lengths):
+        factors = 1./counts.float()
+        q_grouped = clustered_aggregate(Q, clusters, factors, lengths)
+        ctx.save_for_backward(clusters, counts, factors)
+
+        return q_grouped
+
+    @staticmethod
+    def backward(ctx, grad_q_grouped):
+        clusters, counts, factors = ctx.saved_tensors
+        grad_q = clustered_broadcast(grad_q_grouped, clusters, counts, factors)
+
+        return grad_q, None, None, None
+
+
+class _BroadcastValues(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, v_grouped, clusters, counts, lengths):
+        factors = torch.ones_like(counts, dtype=v_grouped.dtype)
+        V = clustered_broadcast(v_grouped, clusters, counts, factors)
+        ctx.save_for_backward(clusters, counts, factors, lengths)
+
+        return V
+
+    @staticmethod
+    def backward(ctx, grad_v):
+        clusters, counts, factors, lengths = ctx.saved_tensors
+        grad_v_grouped = clustered_aggregate(grad_v, clusters, factors, lengths)
+
+        return grad_v_grouped, None, None, None
+
+
+class ClusteredAttention(Module):
+    """Use LSH and clustering in the resulting Hamming space to group queries
+    that will have minimal L2 distance from each other.
+
+    Given the queries, keys, and values as Q, K, and V respectively, we
+    first cluster the queries in "C" groups and compute the "C" query centroids
+    Q_c.
+
+    We now use to the centroids Q_c to compute the attention using:
+
+        V'_c = softmax(Q_c.mm(K.t()), dim=-1).mm(V).
+
+    Now the computed values V'_c are "broadcasted" back to the query members
+    of the corresponding cluster.
+
+    Arguments
+    ---------
+        clusters: How many clusters to group the queries into
+        iterations: The number of lloyd iterations to perform (default: 10)
+        bits: How many bits to use for the hash (default: 32)
+        hash_bias: If true, hamming distance proportional to L2 distance
+                   If false, hamming distance proportional to cosine distance
+                   (default: True)
+        softmax_temp: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.1)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, clusters, iterations=10, bits=32,
+                 hash_bias=True, softmax_temp=None, attention_dropout=0.1,
+                 event_dispatcher=""):
+        super(ClusteredAttention, self).__init__()
+        self.clusters = clusters
+        self.iterations = iterations
+        self.bits = bits
+        self.hash_bias = hash_bias
+        self.softmax_temp = softmax_temp
+        self.dropout = Dropout(attention_dropout)
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def _create_query_groups(self, Q, query_lengths):
+        N, H, L, E = Q.shape
+
+        # Compute the hashes for all the queries
+        planes = Q.new_empty((self.bits, E+1))
+        normal_(planes)
+        if not self.hash_bias:
+            planes[:, -1] = 0
+        hashes = compute_hashes(Q.view(N*H*L, E), planes).view(N, H, L)
+
+        # Cluster the hashes and return the cluster index per query
+        clusters, counts =  cluster(
+            hashes,
+            query_lengths._lengths.int(),
+            clusters=self.clusters,
+            iterations=self.iterations,
+            bits=self.bits
+        )
+        sorted_clusters, sorted_indx = torch.sort(clusters, dim=-1)
+        return (sorted_clusters, counts), sorted_indx
+
+    def _group_queries(self, Q, groups, lengths):
+        """Aggregate the Qs based on the index of cluster they belong to. Make
+        sure to allow for gradient propagation backwards from the grouped
+        queries to each query."""
+        q_grouped = _GroupQueries.apply(Q, *groups, lengths)
+        return q_grouped
+
+    def _broadcast_values(self, V, groups, lengths):
+        """Broadcast the values back to the correct positions but make sure
+        that the gradient flows properly."""
+        V_new = _BroadcastValues.apply(V.contiguous(), *groups, lengths)
+        return V_new
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        # Make sure that there is no attention mask
+        assert attn_mask.all_ones, ("Clustered attention cannot use an "
+                                    "arbitrary attention mask.")
+
+        queries = queries.permute(0,2,1,3).contiguous()
+        keys = keys.permute(0,2,1,3).contiguous()
+        values = values.permute(0,2,1,3).contiguous()
+
+        N, H, L, E = queries.shape
+        _, _, S, D = values.shape
+        softmax_temp = self.softmax_temp or 1./sqrt(E)
+
+        # Cluster the queries into groups
+        groups, sorted_indx = self._create_query_groups(queries, query_lengths)
+        # Re-organize queries so that first group belong to first cluster
+        # next to second cluster and so on. This improves kernel implementations.
+        # Note that this step is introduced after NeurIPS submission and
+        # now the complexity is O(N log(N)).
+        q_offset = torch.arange(N*H, device=queries.device).unsqueeze(-1) * L
+        q_flat = (sorted_indx.view(N*H, -1) + q_offset).reshape(-1)
+        s_queries = queries.reshape(-1, E).index_select(0, q_flat).view(N,H,L,E)
+
+        # Aggregate the re-arranged queries.
+        Q_grouped = self._group_queries(s_queries, groups, query_lengths._lengths.int())
+        # Compute the attention
+        QK = torch.einsum("nhle,nhse->nhls", Q_grouped, keys)
+        QK = QK + key_lengths.additive_matrix[:, None, None, :]
+        A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1))
+        V = torch.einsum("nhls,nhsd->nhld", A, values)
+
+        # Broadcast grouped attention
+        V_broadcast = self._broadcast_values(V, groups, query_lengths._lengths.int())
+
+        # Reverse the previous mapping
+        rev_indx = torch.argsort(sorted_indx, dim=-1)
+        q_rev_flat = (rev_indx.view(N*H, -1) + q_offset).reshape(-1)
+        V_new = V_broadcast.reshape(-1, D).index_select(0, q_rev_flat).view(N,H,L,D)
+        V_new = V_new.permute(0, 2, 1, 3).contiguous()
+        return V_new
+
+
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "clustered", ClusteredAttention,
+    [
+        ("clusters", Int),
+        ("iterations", Optional(Int, 10)),
+        ("bits", Optional(Int, 63)),
+        ("hash_bias", Optional(Bool, True)),
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/conditional_full_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/conditional_full_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1aadc44b1bac27edf23429ac6945049e9764c11
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/conditional_full_attention.py
@@ -0,0 +1,66 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement a self attention that delegates to full attention or another
+attention depending on the input sequence length."""
+
+import torch
+from torch.nn import Module
+
+from ..attention_registry import AttentionRegistry, Optional, Int, Float, \
+    EventDispatcherInstance
+from ..events import EventDispatcher
+from .full_attention import FullAttention
+
+
+class ConditionalFullAttention(Module):
+    """"Delegate to full attention if the input sequence is short.
+
+    Arguments
+    ---------
+        other_attention: Use the passed attention module if the sequence is
+                         longer than 'length_limit'.
+        length_limit: An integer denoting the maximum sequence length to
+                      consider.
+        softmax_temp: See fast_transformers.attention.full_attention.
+        attention_dropout: See fast_transformers.attention.full_attention.
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, other_attention, length_limit=512, softmax_temp=None,
+                 attention_dropout=0.1, event_dispatcher=""):
+        super(ConditionalFullAttention, self).__init__()
+        self.full_attention = FullAttention(softmax_temp, attention_dropout)
+        self.other_attention = other_attention
+        self.length_limit = length_limit
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        # Extract some shapes to compare with the length limit
+        L = queries.shape[1]
+        S = values.shape[1]
+
+        if L > self.length_limit or S > self.length_limit:
+            return self.other_attention(queries, keys, values, attn_mask,
+                                        query_lengths, key_lengths)
+        else:
+            return self.full_attention(queries, keys, values, attn_mask,
+                                       query_lengths, key_lengths)
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "conditional-full", ConditionalFullAttention,
+    [
+        ("length_limit", Optional(Int, 512)),
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/exact_topk_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/exact_topk_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..8723b2d4b56b878125ecc305fe489cc8779dea20
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/exact_topk_attention.py
@@ -0,0 +1,88 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement the oracle top-k attention. The top-k keys are exact ones.
+MultiHeadAttention module. Note that this module is to be used in conjuction
+with the AttentionLayer in order to work."""
+
+from math import sqrt
+
+import torch
+from torch.nn import Dropout, Module
+
+from ..attention_registry import AttentionRegistry, Optional, Int, Float, \
+    EventDispatcherInstance
+from ..events import EventDispatcher
+
+
+class ExactTopKAttention(Module):
+    """Implement the oracle top-k softmax attention.
+
+    Arguments
+    ---------
+        top-k: The top k keys to attend to  (default: 32)
+        softmax_temp: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.1)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, topk=32, softmax_temp=None, attention_dropout=0.1,
+                 event_dispatcher=""):
+        super(ExactTopKAttention, self).__init__()
+        self.topk = topk
+        self.softmax_temp = softmax_temp
+        self.dropout = Dropout(attention_dropout)
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        # Extract some shapes and compute the temperature
+        N, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        softmax_temp = self.softmax_temp or 1./sqrt(E)
+
+        # Compute the unnormalized attention and apply the masks
+        QK = torch.einsum("nlhe,nshe->nhls", queries, keys)
+        topk = min(self.topk, S)
+        
+        if not attn_mask.all_ones:
+            QK = QK + attn_mask.additive_matrix
+        QK = QK + key_lengths.additive_matrix[:, None, None]
+
+        topk_values, topk_idx = torch.topk(QK, topk, sorted=False, dim=-1)
+        mask = QK.new_ones(QK.shape) *  float("-inf") 
+        mask[
+            torch.arange(N, device=QK.device).view(N, 1, 1, 1),
+            torch.arange(H, device=QK.device).view(1, H, 1, 1),
+            torch.arange(L, device=QK.device).view(1, 1, L, 1),
+            topk_idx,
+        ] = 0.
+
+        QK = QK + mask 
+
+        # Compute the attention and the weighted average
+        A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1))
+        V = torch.einsum("nhls,nshd->nlhd", A, values)
+
+        # Make sure that what we return is contiguous
+        return V.contiguous()
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "exact-topk", ExactTopKAttention,
+    [
+        ("topk", Optional(Int, 32)),
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/full_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/full_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6372ae87cda90c4ac0096414b672cdcd5874848
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/full_attention.py
@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement the full attention similar to the one implemented by PyTorch's
+MultiHeadAttention module. Note that this module is to be used in conjuction
+with the `fast_transformers.attention.attention_layer.AttentionLayer` in order
+to work."""
+
+from math import sqrt
+
+import torch
+from torch.nn import Dropout, Module
+
+from ..attention_registry import AttentionRegistry, Optional, Float, \
+    EventDispatcherInstance
+from ..events import EventDispatcher, AttentionEvent
+
+
+class FullAttention(Module):
+    """Implement the scaled dot product attention with softmax.
+
+    Arguments
+    ---------
+        softmax_temp: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.1)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, softmax_temp=None, attention_dropout=0.1,
+                 event_dispatcher=""):
+        super(FullAttention, self).__init__()
+        self.softmax_temp = softmax_temp
+        self.dropout = Dropout(attention_dropout)
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        """Implements the multihead softmax attention.
+
+        Arguments
+        ---------
+            queries: (N, L, H, E) The tensor containing the queries
+            keys: (N, S, H, E) The tensor containing the keys
+            values: (N, S, H, D) The tensor containing the values
+            attn_mask: An implementation of BaseMask that encodes where each
+                       query can attend to
+            query_lengths: An implementation of  BaseMask that encodes how
+                           many queries each sequence in the batch consists of
+            key_lengths: An implementation of BaseMask that encodes how
+                         many queries each sequence in the batch consists of
+        """
+        # Extract some shapes and compute the temperature
+        N, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        softmax_temp = self.softmax_temp or 1./sqrt(E)
+
+        # Scale the queries instead of applying the softmax temperature to the
+        # dot products
+        queries = queries * softmax_temp
+
+        # Compute the unnormalized attention and apply the masks
+        QK = torch.einsum("nlhe,nshe->nhls", queries, keys)
+        if not attn_mask.all_ones:
+            QK = QK + attn_mask.additive_matrix
+        if not key_lengths.all_ones:
+            QK = QK + key_lengths.additive_matrix[:, None, None]
+
+        # Compute the attention and the weighted average
+        A = self.dropout(torch.softmax(QK, dim=-1))
+        V = torch.einsum("nhls,nshd->nlhd", A, values)
+
+        # Let the world know of the attention matrix
+        self.event_dispatcher.dispatch(AttentionEvent(self, A))
+
+        # Make sure that what we return is contiguous
+        return V.contiguous()
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "full", FullAttention,
+    [
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/improved_clustered_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/improved_clustered_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..278fe5ee179e8b1dce15ef22f25867a950b5aefc
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/improved_clustered_attention.py
@@ -0,0 +1,268 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement improved clustered self attention."""
+
+from math import sqrt
+
+import torch
+import torch.autograd
+from torch.nn import Dropout, Module
+from torch.nn.init import normal_
+
+from ..attention_registry import AttentionRegistry, Optional, Float, Int, \
+    Bool, EventDispatcherInstance
+from ..events import EventDispatcher
+from ..masking import FullMask
+from ..aggregate import clustered_aggregate, clustered_broadcast
+from ..clustering.hamming import cluster
+from ..hashing import compute_hashes
+from ..sparse_product import sparse_dot_product, sparse_weighted_average
+from ..sparse_product import clustered_sparse_dot_product, \
+    clustered_sparse_weighted_average
+
+
+class _GroupQueries(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, Q, clusters, counts, lengths):
+        factors = 1./counts.float()
+        q_grouped = clustered_aggregate(Q, clusters, factors, lengths)
+        ctx.save_for_backward(clusters, counts, factors)
+
+        return q_grouped
+
+    @staticmethod
+    def backward(ctx, grad_q_grouped):
+        clusters, counts, factors = ctx.saved_tensors
+        grad_q = clustered_broadcast(grad_q_grouped, clusters, counts, factors)
+
+        return grad_q, None, None, None
+
+
+class _BroadcastValues(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, v_grouped, clusters, counts, lengths):
+        factors = torch.ones_like(counts, dtype=v_grouped.dtype)
+        V = clustered_broadcast(v_grouped, clusters, counts, factors)
+        ctx.save_for_backward(clusters, counts, factors, lengths)
+
+        return V
+
+    @staticmethod
+    def backward(ctx, grad_v):
+        clusters, counts, factors, lengths = ctx.saved_tensors
+        grad_v_grouped = clustered_aggregate(grad_v, clusters, factors, lengths)
+
+        return grad_v_grouped, None, None, None, None
+
+
+class ImprovedClusteredAttention(Module):
+    """
+    Immproved clustered attention approximation by recompution attention
+    for each query with the top-k keys for the corresponding cluster.
+
+    Given the queries, keys, and values as Q, K, and V respectively, we
+    first cluster the queries in "C" groups and compute the "C" query centroids
+    Q_c.
+
+    We now use to the centroids Q_c to identify the top-k keys with highest
+    dot products.
+
+    Subsequently, for each query we compute the sparse dot product with
+    the corresponding top-k keys to improve the attention approximation.
+
+    Arguments
+    ---------
+        clusters: How many clusters to group the queries into
+        iterations: The number of lloyd iterations to perform (default: 10)
+        bits: How many bits to use for the hash (default: 32)
+        hash_bias: If true, hamming distance proportional to L2 distance
+                   If false, hamming distance proportional to cosine distance
+                   (default: True)
+        topk: Number of top-k keys to for improved approximation (default: 32)
+        softmax_temp: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.1)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, clusters, iterations=10, bits=32,
+                 hash_bias=True, topk=32, softmax_temp=None,
+                 attention_dropout=0.1, event_dispatcher=""):
+        super(ImprovedClusteredAttention, self).__init__()
+        self.clusters = clusters
+        self.iterations = iterations
+        self.bits = bits
+        self.hash_bias = hash_bias
+        self.topk = topk
+        self.softmax_temp = softmax_temp
+        self.dropout = Dropout(attention_dropout)
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def _create_query_groups(self, Q, query_lengths):
+        N, H, L, E = Q.shape
+
+        # Compute the hashes for all the queries
+        planes = Q.new_empty((self.bits, E+1))
+        normal_(planes)
+        if not self.hash_bias:
+            planes[:, -1] = 0
+        hashes = compute_hashes(Q.view(N*H*L, E), planes).view(N, H, L)
+
+        # Cluster the hashes and return the cluster index per query
+        clusters, counts =  cluster(
+            hashes,
+            query_lengths._lengths.int(),
+            clusters=self.clusters,
+            iterations=self.iterations,
+            bits=self.bits
+        )
+        sorted_clusters, sorted_indx = torch.sort(clusters, dim=-1)
+        return (sorted_clusters, counts), sorted_indx
+
+    def _topk_attention(self, Q, K, V,
+                        clusters, counts,
+                        topk, topk_values,
+                        A_bottomk, softmax_temp,
+                        query_lengths):
+        """Return the attention with just the topk heads."""
+        # Extract some indices
+        N, H, L, E = Q.shape
+        _, _, S, _ = K.shape
+        _, _, C, k = topk.shape
+
+        # We need to pass the output tensor to initialize to 0
+        QK = clustered_sparse_dot_product(
+            Q, K, topk,
+            clusters, counts,
+            query_lengths._lengths.int()
+        )
+        # We need to mask the topk dot products if topk > input_length
+        QK = QK.masked_fill(
+            torch.isinf(topk_values[:,0,0,:]).view(N, 1, 1, k),
+            float("-inf")
+        )
+        A = torch.softmax(softmax_temp * QK, dim=-1)
+        assert A_bottomk.is_contiguous()
+        A_bottomk = clustered_broadcast(
+            A_bottomk.unsqueeze(3),
+            clusters,
+            counts,
+            torch.ones_like(counts, dtype=torch.float32)
+        )
+        A = A * (1.0 - A_bottomk)
+        A = self.dropout(A)
+        assert A.is_contiguous()
+        V_new = clustered_sparse_weighted_average(A, V, topk, clusters, counts)
+
+        return V_new
+
+    def _broadcast_values(self, V, clusters, counts, lengths):
+        """Broadcast the values back to the correct positions but make sure
+        that the gradient flows properly."""
+        V_new = _BroadcastValues.apply(V.contiguous(), clusters, counts, lengths)
+        return V_new
+
+    def _bottomk_attention(self, QK, V, clusters, counts, query_lengths, topk, softmax_temp):
+        """Return the attention with just the bottomk keys."""
+        N, H, C, S = QK.shape
+
+        A = torch.softmax(softmax_temp * QK, dim=-1)
+        mask = QK.new_ones(QK.shape)
+        mask[
+            torch.arange(N, device=QK.device).view(N, 1, 1, 1),
+            torch.arange(H, device=QK.device).view(1, H, 1, 1),
+            torch.arange(C, device=QK.device).view(1, 1, C, 1),
+            topk,
+        ] = 0
+        A = A * mask
+        A_bottomk = A.sum(-1)
+        A = self.dropout(A)
+        # Compute the values
+        V_new = torch.einsum("nhls,nhse->nhle", A, V)
+        # Broadcast the values back depending on the groups
+        V_new = self._broadcast_values(V_new, clusters, counts, query_lengths._lengths.int())
+
+        return V_new, A_bottomk
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        # Make sure that there is no attention mask
+        assert attn_mask.all_ones, ("Improved-clustered attention cannot "
+                                    "use an arbitrary attention mask.")
+
+        queries = queries.permute(0,2,1,3).contiguous()
+        keys = keys.permute(0,2,1,3).contiguous()
+        values = values.permute(0,2,1,3).contiguous()
+        N, H, L, E = queries.shape
+        _, _, S, D = values.shape
+        softmax_temp = self.softmax_temp or 1./sqrt(E)
+
+        # Cluster the queries into groups
+        groups, sorted_indx = self._create_query_groups(queries, query_lengths)
+        clusters, counts = groups
+
+        # Re-organize queries so that first group belong to first cluster
+        # next to second cluster and so on. This improves kernel implementations.
+        # Note that this step is introduced after NeurIPS submission and
+        # now the complexity is O(N log(N)).
+        q_offset = torch.arange(N*H, device=queries.device).unsqueeze(-1) * L
+        q_flat = (sorted_indx.view(N*H, -1) + q_offset).reshape(-1)
+        s_queries = queries.reshape(-1, E).index_select(0, q_flat).view(N,H,L,E)
+
+        # Aggregate the re-arranged queries.
+        Q_grouped = _GroupQueries.apply(s_queries, *groups, query_lengths.lengths.int())
+        # Compute the attention
+        QK = torch.einsum("nhle,nhse->nhls", Q_grouped, keys)
+        QK = QK + key_lengths.additive_matrix[:, None, None, :]
+        topk_values, topk = torch.topk(QK, min(self.topk, S), sorted=False, dim=-1)
+        assert topk.is_contiguous()
+
+        # Now compute the attention with only the bottom keys
+        V_bottomk, A_bottomk = self._bottomk_attention(
+            QK, values,
+            clusters, counts,
+            query_lengths,
+            topk,
+            softmax_temp
+        )
+
+        # Now compute the attention with only the top keys
+        V_topk = self._topk_attention(
+            s_queries, keys, values,
+            clusters, counts,
+            topk, topk_values,
+            A_bottomk,
+            softmax_temp,
+            query_lengths
+        )
+        V_sorted_new = V_topk + V_bottomk
+
+        # Reverse the previous mapping
+        sorted_rev_indx = torch.argsort(sorted_indx, dim=-1)
+        q_rev_flat = (sorted_rev_indx.view(N*H, -1) + q_offset).reshape(-1)
+        V_new = V_sorted_new.reshape(-1, D).index_select(0, q_rev_flat).view(N,H,L,D)
+        return V_new.permute(0, 2, 1, 3).contiguous()
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "improved-clustered", ImprovedClusteredAttention,
+    [
+        ("clusters", Int),
+        ("iterations", Optional(Int, 10)),
+        ("bits", Optional(Int, 63)),
+        ("hash_bias", Optional(Bool, True)),
+        ("topk", Optional(Int, 32)),
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/improved_clustered_causal_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/improved_clustered_causal_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..d799e30abd7e8364252b5cf0d8eccb7db2509571
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/improved_clustered_causal_attention.py
@@ -0,0 +1,257 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement improved clustered causal self attention."""
+
+from math import sqrt
+
+import torch
+import torch.autograd
+from torch.nn import Dropout, Module
+from torch.nn.init import normal_
+
+from ..attention_registry import AttentionRegistry, Optional, Float, Int, \
+    Bool, EventDispatcherInstance
+from ..events import EventDispatcher
+from ..masking import FullMask
+from ..aggregate import clustered_aggregate, clustered_broadcast
+from ..clustering.hamming import cluster
+from ..hashing import compute_hashes
+from ..sparse_product import sparse_dot_product, sparse_weighted_average
+from ..sparse_product import clustered_sparse_dot_product, \
+    clustered_sparse_weighted_average
+
+
+class _GroupQueries(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, Q, clusters, counts, lengths):
+        factors = 1./counts.float()
+        q_grouped = clustered_aggregate(Q, clusters, factors, lengths)
+        ctx.save_for_backward(clusters, counts, factors)
+
+        return q_grouped
+
+    @staticmethod
+    def backward(ctx, grad_q_grouped):
+        clusters, counts, factors = ctx.saved_tensors
+        grad_q = clustered_broadcast(grad_q_grouped, clusters, counts, factors)
+
+        return grad_q, None, None, None
+
+
+class _BroadcastValues(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, v_grouped, clusters, counts, lengths):
+        factors = torch.ones_like(counts, dtype=v_grouped.dtype)
+        V = clustered_broadcast(v_grouped, clusters, counts, factors)
+        ctx.save_for_backward(clusters, counts, factors, lengths)
+
+        return V
+
+    @staticmethod
+    def backward(ctx, grad_v):
+        clusters, counts, factors, lengths = ctx.saved_tensors
+        grad_v_grouped = clustered_aggregate(grad_v, clusters, factors, lengths)
+
+        return grad_v_grouped, None, None, None, None
+
+
+class ImprovedClusteredCausalAttention(Module):
+    """
+    Immproved clustered causal attention approximation by recomputing attention
+    for each query with the top-k keys for the corresponding cluster.
+
+    Given the queries, keys, and values as Q, K, and V respectively, we
+    first cluster the queries in "C" groups and compute the "C" query centroids
+    Q_c.
+
+    We now use to the centroids Q_c to identify the top-k keys with highest
+    dot products.
+
+    Subsequently, for each query we compute the sparse dot product with
+    the corresponding top-k keys to improve the attention approximation.
+
+    Key difference with improved clustered attention is that we only use
+    top-k keys with causal mask, we do not compute attention on the
+    bottom-k keys.
+
+    Arguments
+    ---------
+        clusters: How many clusters to group the queries into
+        iterations: The number of lloyd iterations to perform (default: 10)
+        bits: How many bits to use for the hash (default: 32)
+        hash_bias: If true, hamming distance proportional to L2 distance
+                   If false, hamming distance proportional to cosine distance
+                   (default: True)
+        topk: Number of top-k keys to for improved approximation (default: 32)
+        softmax_temp: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.1)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, clusters, iterations=10, bits=32,
+                 hash_bias=True, topk=32, softmax_temp=None,
+                 attention_dropout=0.1, event_dispatcher=""):
+        super(ImprovedClusteredCausalAttention, self).__init__()
+        self.clusters = clusters
+        self.iterations = iterations
+        self.bits = bits
+        self.hash_bias = hash_bias
+        self.topk = topk
+        self.softmax_temp = softmax_temp
+        self.dropout = Dropout(attention_dropout)
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def _create_query_groups(self, Q, query_lengths):
+        N, H, L, E = Q.shape
+
+        # Compute the hashes for all the queries
+        planes = Q.new_empty((self.bits, E+1))
+        normal_(planes)
+        if not self.hash_bias:
+            planes[:, -1] = 0
+        hashes = compute_hashes(Q.view(N*H*L, E), planes).view(N, H, L)
+
+        # Cluster the hashes and return the cluster index per query
+        clusters, counts =  cluster(
+            hashes,
+            query_lengths.lengths.int(),
+            clusters=self.clusters,
+            iterations=self.iterations,
+            bits=self.bits
+        )
+        sorted_clusters, sorted_indx = torch.sort(clusters, dim=-1)
+        return (sorted_clusters, counts), sorted_indx
+
+    def _topk_attention(self, Q, K, V,
+                        q_flat, q_rev_flat,
+                        clusters, counts,
+                        topk, topk_values,
+                        softmax_temp,
+                        query_lengths):
+        """Return the attention with just the topk heads."""
+        # Extract some indices
+        N, H, L, E = Q.shape
+        _, _, S, _ = K.shape
+        _, _, C, k = topk.shape
+
+        # We need to pass the output tensor to initialize to 0
+        QK = clustered_sparse_dot_product(
+            Q, K, topk,
+            clusters, counts,
+            query_lengths.lengths.int()
+        )
+        # We need to mask out the future
+        assert topk.is_contiguous()
+        topk_broadcast = clustered_broadcast(
+            topk.float(),
+            clusters,
+            counts,
+            torch.ones_like(counts, dtype=torch.float32)
+        )
+        # Need to be careful here we changed the order of the keys the
+        # masking on future needs to be applied in the same way
+        seq_ids = torch.arange(L, device=QK.device).view(1, 1, L, 1).repeat(N, H, 1, 1)
+        # permute the ids in the same way as input so as to mask the right
+        # entries for each query
+        s_seq_ids = seq_ids.reshape(-1, 1).index_select(0, q_flat).view(N,H,L,1)
+        future_mask = topk_broadcast.long() > s_seq_ids
+        QK = QK.masked_fill(
+            future_mask,
+            float("-1e7")
+        )
+        A = torch.softmax(softmax_temp * QK, dim=-1)
+        # Mask again to ensure no probabilities leak due to float(-1e7)
+        # Leakage could be very high as we use a small top-k
+        A = A * (1. - future_mask.float())
+        A = self.dropout(A)
+        assert A.is_contiguous()
+        V_new = clustered_sparse_weighted_average(A, V, topk, clusters, counts)
+
+        return V_new
+
+    def _broadcast_values(self, V, clusters, counts, lengths):
+        """Broadcast the values back to the correct positions but make sure
+        that the gradient flows properly."""
+        V_new = _BroadcastValues.apply(V.contiguous(), clusters, counts, lengths)
+        return V_new
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+
+        # Apply the key padding mask and make sure the attn_mask is a
+        # lower triangular causal mask
+        if not attn_mask.lower_triangular:
+            raise RuntimeError(("ImprovedClusteredCausalAttention only supports "
+                                "lower triangular masks"))
+        queries = queries.permute(0,2,1,3).contiguous()
+        keys = keys.permute(0,2,1,3).contiguous()
+        values = values.permute(0,2,1,3).contiguous()
+        N, H, L, E = queries.shape
+        _, _, S, D = values.shape
+        softmax_temp = self.softmax_temp or 1./sqrt(E)
+
+        # Cluster the queries into groups
+        groups, sorted_indx = self._create_query_groups(queries, query_lengths)
+        clusters, counts = groups
+
+        # Re-organize queries so that first group belong to first cluster
+        # next to second cluster and so on. This improves kernel implementations.
+        # Note that this step is introduced after NeurIPS submission and
+        # now the complexity is O(N log(N)).
+        q_offset = torch.arange(N*H, device=queries.device).unsqueeze(-1) * L
+        q_flat = (sorted_indx.view(N*H, -1) + q_offset).reshape(-1)
+        s_queries = queries.reshape(-1, E).index_select(0, q_flat).view(N,H,L,E)
+
+        # Aggregate the re-arranged queries.
+        Q_grouped = _GroupQueries.apply(s_queries, *groups, query_lengths.lengths.int())
+        # Compute the attention
+        QK = torch.einsum("nhle,nhse->nhls", Q_grouped, keys)
+        QK = QK + key_lengths.additive_matrix[:, None, None, :]
+        # Set topk to minimum of key lengths if it is smaller than self.topk
+        cur_topk = min(self.topk, min(key_lengths.lengths).item())
+        topk_values, topk = torch.topk(QK, cur_topk, sorted=False, dim=-1)
+        assert topk.is_contiguous()
+
+        # Reverse mapping
+        sorted_rev_indx = torch.argsort(sorted_indx, dim=-1)
+        q_rev_flat = (sorted_rev_indx.view(N*H, -1) + q_offset).reshape(-1)
+
+        # Compute the attention with only the top keys
+        V_topk = self._topk_attention(
+            s_queries, keys, values,
+            q_flat, q_rev_flat,
+            clusters, counts,
+            topk, topk_values,
+            softmax_temp,
+            query_lengths
+        )
+        V_sorted_new = V_topk
+
+        # Reverse the mapping to get correct values
+        V_new = V_sorted_new.reshape(-1, D).index_select(0, q_rev_flat).view(N,H,L,D)
+        return V_new.permute(0, 2, 1, 3).contiguous()
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "causal-improved-clustered", ImprovedClusteredCausalAttention,
+    [
+        ("clusters", Int),
+        ("iterations", Optional(Int, 10)),
+        ("bits", Optional(Int, 63)),
+        ("hash_bias", Optional(Bool, True)),
+        ("topk", Optional(Int, 32)),
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/linear_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/linear_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbad2ea326a0fa3d6a1fcce5ff4e0b617d8af924
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/linear_attention.py
@@ -0,0 +1,92 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement unmasked linear attention."""
+
+import torch
+from torch.nn import Module
+
+from ..attention_registry import AttentionRegistry, Optional, Callable, Int, \
+    EventDispatcherInstance
+from ..events import EventDispatcher
+from ..feature_maps import elu_feature_map
+
+
+class LinearAttention(Module):
+    """Implement unmasked attention using dot product of feature maps in
+    O(N D^2) complexity.
+
+    Given the queries, keys and values as Q, K, V instead of computing
+
+        V' = softmax(Q.mm(K.t()), dim=-1).mm(V),
+
+    we make use of a feature map function Φ(.) and perform the following
+    computation
+
+        V' = normalize(Φ(Q).mm(Φ(K).t())).mm(V).
+
+    The above can be computed in O(N D^2) complexity where D is the
+    dimensionality of Q, K and V and N is the sequence length. Depending on the
+    feature map, however, the complexity of the attention might be limited.
+
+    Arguments
+    ---------
+        feature_map: callable, a callable that applies the feature map to the
+                     last dimension of a tensor (default: elu(x)+1)
+        eps: float, a small number to ensure the numerical stability of the
+             denominator (default: 1e-6)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, query_dimensions, feature_map=None, eps=1e-6,
+                 event_dispatcher=""):
+        super(LinearAttention, self).__init__()
+        self.feature_map = (
+            feature_map(query_dimensions) if feature_map else
+            elu_feature_map(query_dimensions)
+        )
+        self.eps = eps
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        # Apply the feature map to the queries and keys
+        self.feature_map.new_feature_map(queries.device)
+        Q = self.feature_map.forward_queries(queries)
+        K = self.feature_map.forward_keys(keys)
+
+        # Apply the key padding mask and make sure that the attn_mask is
+        # all_ones
+        if not attn_mask.all_ones:
+            raise RuntimeError(("LinearAttention does not support arbitrary "
+                                "attention masks"))
+        K = K * key_lengths.float_matrix[:, :, None, None]
+
+        # Compute the KV matrix, namely the dot product of keys and values so
+        # that we never explicitly compute the attention matrix and thus
+        # decrease the complexity
+        KV = torch.einsum("nshd,nshm->nhmd", K, values)
+
+        # Compute the normalizer
+        Z = 1/(torch.einsum("nlhd,nhd->nlh", Q, K.sum(dim=1))+self.eps)
+
+        # Finally compute and return the new values
+        V = torch.einsum("nlhd,nhmd,nlh->nlhm", Q, KV, Z)
+
+        return V.contiguous()
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "linear", LinearAttention,
+    [
+        ("query_dimensions", Int),
+        ("feature_map", Optional(Callable)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/local_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/local_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..40b2adfa23c0600741e036723a9c91cdc93368a6
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/local_attention.py
@@ -0,0 +1,101 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Implement local context attention."""
+
+from math import sqrt
+
+import torch
+from torch.nn import Module, Dropout
+from torch.nn import functional as F
+
+from ..attention_registry import AttentionRegistry, Optional, Int, Float, \
+    EventDispatcherInstance
+from ..events import EventDispatcher
+from ..local_product import local_dot_product, local_weighted_average
+
+
+class LocalAttention(Module):
+    """Implement fast local attention where a query can only attend to
+    neighboring keys.
+
+    In this attention module the query Q_i can only attend to a key K_j if
+    |i-j| < local_context/2.
+
+    Arguments
+    ---------
+        local_context: The neighborhood to consider for local attention.
+        softmax_temp: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.1)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, local_context, softmax_temp=None, attention_dropout=0.1,
+                 event_dispatcher=""):
+        super(LocalAttention, self).__init__()
+        self.local_context = local_context
+        self.softmax_temp = softmax_temp
+        self.dropout = Dropout(attention_dropout)
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        """Implements the local attention.
+
+        The attn_mask can be anything but the only values that will be
+        considered will be the ones in the neighborhood of each query.
+
+        Arguments
+        ---------
+            queries: (N, L, H, E) The tensor containing the queries
+            keys: (N, S, H, E) The tensor containing the keys
+            values: (N, S, H, D) The tensor containing the values
+            attn_mask: An implementation of BaseMask that encodes where each
+                       query can attend to
+            query_lengths: An implementation of  BaseMask that encodes how
+                           many queries each sequence in the batch consists of
+            key_lengths: An implementation of BaseMask that encodes how
+                         many queries each sequence in the batch consists of
+        """
+        # Extract some shapes and compute the temperature
+        N, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        context = self.local_context
+        softmax_temp = self.softmax_temp or 1./sqrt(E)
+
+        # Permute the dimensions to NHLE instead of NLHE
+        queries = queries.permute(0, 2, 1, 3).contiguous()
+        keys = keys.permute(0, 2, 1, 3).contiguous()
+        values = values.permute(0, 2, 1, 3).contiguous()
+
+        QK = local_dot_product(
+            queries,
+            keys,
+            attn_mask.additive_matrix_finite,
+            key_lengths.lengths,
+            self.local_context
+        )
+        A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1))
+
+        V_new = local_weighted_average(A, values)
+
+        return V_new.permute(0, 2, 1, 3).contiguous()
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "local", LocalAttention,
+    [
+        ("local_context", Int),
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention/reformer_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/attention/reformer_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee350025805eee768d2a635ccf4bf63f6de1d5da
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention/reformer_attention.py
@@ -0,0 +1,166 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement the Reformer attention from the paper
+"Reformer the efficient transformer"."""
+
+from math import sqrt
+
+import torch
+from torch.nn import Dropout, Module
+from torch.nn.init import normal_
+
+from ..attention_registry import AttentionRegistry, Optional, Int, Float, \
+    Bool, EventDispatcherInstance
+from ..events import EventDispatcher
+from ..masking import FullMask
+
+
+class ReformerAttention(Module):
+    """Implement the attention module of the paper "Reformer the efficient
+    transformer"
+
+    Arguments
+    ---------
+        chunk_size  : Chunk size for each block (default: 32)
+        bits        : Number of bits for hashing (default: 8)
+        rounds      : Number of rounds of attention computation (default: 4)
+        masked      : If true, the query does not attend to itsself (default: False)
+        softmax_temp: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.1)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+
+    def __init__(self, chunk_size=32, bits=8, rounds=4, masked=False,
+                 softmax_temp=None, attention_dropout=0.1,
+                 event_dispatcher=""):
+        super(ReformerAttention, self).__init__()
+
+        self.chunk_size = chunk_size
+        self.bits = bits
+        self.rounds = rounds
+        self.masked = masked
+        self.softmax_temp = softmax_temp
+        self.dropout = Dropout(attention_dropout)
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def _normalize(self, x):
+        norms = torch.sqrt(torch.einsum("nlhe,nlhe->nlh", x, x))
+        x_normed = x / norms.unsqueeze(-1)
+        return x_normed
+
+    def _look_back(self, x):
+        xshape = x.shape
+
+        return torch.cat([
+            x.new_zeros((xshape[0], 1) + xshape[2:]),
+            torch.repeat_interleave(x, 2, dim=1)[:,:-1]
+        ], dim=1).view(xshape[0], xshape[1], 2*xshape[2], *xshape[3:])
+
+    def _reformer_round(self, Q, K, V, mask, softmax_temp):
+        # Hash the queries
+        N, L, H, E = Q.shape
+        planes = Q.new_empty(self.bits, E)
+        normal_(planes)
+        projected = torch.einsum("nlhe,be->nlhb", K, planes)
+        hashes = torch.argmax(
+            torch.cat([projected, -projected], dim=-1),
+            dim=-1
+        )
+
+        # Sort the queries in order to group them
+        group = torch.argsort(hashes, dim=1)
+
+        invert_group = torch.empty_like(group)
+        batch_indices = torch.arange(N, device=hashes.device).view(N, 1, 1)
+        sequence_indices = torch.arange(L, device=hashes.device).view(1, L, 1)
+        head_indices = torch.arange(H, device=hashes.device).view(1, 1, H)
+        invert_group[batch_indices, group, head_indices] = sequence_indices
+        group = group.view(N, -1, self.chunk_size, H)
+        invert_group = invert_group.view(N, -1, self.chunk_size, H)
+        batch_indices = batch_indices.unsqueeze(1)
+        head_indices = head_indices.unsqueeze(0)
+
+        # Reorder Q, V and mask
+        Q_grouped = Q[batch_indices, group, head_indices]
+        K_grouped = K[batch_indices, group, head_indices]
+        V_grouped = V[batch_indices, group, head_indices]
+        mask_grouped = mask[
+            batch_indices.unsqueeze(1),
+            group.unsqueeze(3),
+            self._look_back(group).unsqueeze(2)
+        ]
+
+        mask_grouped[:, 0, :, :Q_grouped.shape[2]] = float("-inf")
+
+        # When everything is masked just unmask everything because it doesn't
+        # matter what the output is at those positions
+        # This is to avoid inf/nans in the new values at masked positions
+        infmask = torch.isinf(mask_grouped)
+        infmask = torch.all(infmask, dim=3, keepdims=True)
+        mask_grouped = mask_grouped.masked_fill(infmask, 0.)
+
+        # Attention
+        K_grouped = self._look_back(K_grouped)
+        QQ = torch.einsum("nblhe,nbshe->nbhls", Q_grouped, K_grouped)
+        QQ = QQ + mask_grouped.permute(0, 1, 4, 2, 3)
+        A = torch.softmax(softmax_temp * QQ, dim=-1)
+        A = self.dropout(A)
+
+        # Values
+        V_grouped = self._look_back(V_grouped)
+        V_new = torch.einsum("nbhls,nbshe->nblhe", A, V_grouped)
+        V_new = V_new.contiguous().view(N, -1,  H, E)
+        V_new = V_new[batch_indices, invert_group, head_indices]
+        V_new = V_new.contiguous().view(N, L, H, E)
+        return V_new
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        # Extract the dimensions of query, key, value
+        N, L, H, E = queries.shape
+
+        softmax_temp = self.softmax_temp or 1./sqrt(E)
+        # Create the mask
+        mask = key_lengths.additive_matrix.unsqueeze(1).expand(N, L, L)
+        if self.masked:
+            mask = mask + torch.eye(L, device=queries.device).unsqueeze(0)*float(-1e9)
+       
+        if not attn_mask.all_ones:
+            mask = mask + attn_mask.additive_matrix.unsqueeze(0)
+        # Get normalized Queries as Keys
+        K = self._normalize(queries)
+        # Zero the masked out keys
+        K = K * key_lengths.float_matrix.view(N, L, 1, 1)
+
+        V_new = 0
+        factor = 1/self.rounds
+        for i in range(self.rounds):
+            V_new = V_new + \
+                    factor * self._reformer_round(queries, K, values, mask, softmax_temp)
+
+        return V_new
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+AttentionRegistry.register(
+    "reformer", ReformerAttention,
+    [
+        ("chunk_size", Optional(Int, 32)),
+        ("bits", Optional(Int, 63)),
+        ("rounds", Optional(Int, 4)),
+        ("masked", Optional(Bool, False)),
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5848430fbb6c94a0c702c217b211c41b29db4438
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/__init__.py
@@ -0,0 +1,17 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Allow for the dynamic registration of new attention implementations.
+
+This module provides a Registry implementation that other modules can use to
+register attention implementations for the builders.
+"""
+
+from .registry import \
+    AttentionRegistry, \
+    RecurrentAttentionRegistry, \
+    RecurrentCrossAttentionRegistry
+from .spec import Spec, Choice, Optional, Int, Float, Bool, Callable, \
+    EventDispatcherInstance
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/__pycache__/__init__.cpython-310.pyc b/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/__pycache__/__init__.cpython-310.pyc
new file mode 100644
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/registry.py b/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..58e8e91d8e4d00ab8adfdc04ef213351dcf22b8f
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/registry.py
@@ -0,0 +1,61 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+
+class Registry(object):
+    """Hold the available attention implementations and their required
+    parameters."""
+    def __init__(self):
+        self._classes = {}
+        self._class_params = {}
+        self._parameters = {}
+
+    def register(self, key, class_object, parameter_tuples):
+        # register the class if the key is new
+        if key in self._classes:
+            raise ValueError("{} is already registered".format(key))
+        self._classes[key] = class_object
+
+        # register the parameters
+        for parameter, spec in parameter_tuples:
+            if (
+                parameter in self._parameters and
+                self._parameters[parameter] != spec
+            ):
+                raise ValueError(("{} is already registered with "
+                                  "spec {!r} instead of {!r}").format(
+                                  parameter,
+                                  self._parameters[parameter],
+                                  spec
+                                ))
+            self._parameters[parameter] = spec
+
+        # note which parameters are needed by this class
+        self._class_params[key] = [p for p, s in parameter_tuples]
+
+    def __contains__(self, key):
+        return key in self._classes
+
+    def __getitem__(self, key):
+        return self._classes[key], self._class_params[key]
+
+    @property
+    def keys(self):
+        return list(self._classes.keys())
+
+    def contains_parameter(self, key):
+        return key in self._parameters
+
+    def validate_parameter(self, key, value):
+        try:
+            return self._parameters[key].get(value)
+        except Exception as e:
+            raise ValueError(("Invalid value {!r} for "
+                              "parameter {!r}").format(value, key)) from e
+
+
+AttentionRegistry = Registry()
+RecurrentAttentionRegistry = Registry()
+RecurrentCrossAttentionRegistry = Registry()
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/spec.py b/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/spec.py
new file mode 100644
index 0000000000000000000000000000000000000000..c133a4ca7fb265b9bdd1edd0d79407a243ab1692
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/attention_registry/spec.py
@@ -0,0 +1,126 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Spec instances allow to describe and check the type and value of
+parameters."""
+
+from ..events import EventDispatcher
+
+
+class Spec(object):
+    """Describe and validate a parameter type.
+
+    Arguments
+    ---------
+        predicate: A callable that checks if the value is acceptable and
+                   returns its canonical value or raises ValueError.
+        name: A name to create a human readable description of the Spec
+    """
+    def __init__(self, predicate, name="CustomSpec"):
+        self._predicate = predicate
+        self._name = name
+
+    def __repr__(self):
+        return self._name
+
+    def check(self, x):
+        try:
+            self._predicate(x)
+            return True
+        except ValueError:
+            return False
+
+    def get(self, x):
+        return self._predicate(x)
+
+    def __eq__(self, y):
+        return self is y
+
+
+class Choice(Spec):
+    """A parameter type for a set of options.
+
+    Arguments
+    ---------
+        choices: A set or list of possible values for this parameter
+    """
+    def __init__(self, choices):
+        self._choices = choices
+
+    def get(self, x):
+        if x in self._choices:
+            return x
+        raise ValueError("{!r} is not in {!r}".format(x, self._choices))
+
+    def __repr__(self):
+        return "Choice({!r})".format(self._choices)
+
+    def __eq__(self, x):
+        if isinstance(x, Choice):
+            return self._choices == x._choices
+        return False
+
+
+class _Callable(Spec):
+    def __init__(self):
+        super(_Callable, self).__init__(None, "Callable")
+
+    def get(self, x):
+        if callable(x):
+            return x
+        raise ValueError("{!r} is not a callable".format(x))
+
+
+class _EventDispatcherInstance(Spec):
+    def __init__(self):
+        super(_EventDispatcherInstance, self).__init__(
+            _EventDispatcherInstance._get_event_dispatcher,
+            "EventDispatcherInstance"
+        )
+
+    @staticmethod
+    def _get_event_dispatcher(x):
+        if isinstance(x, str):
+            return x
+        if isinstance(x, EventDispatcher):
+            return x
+        raise ValueError("{!r} is not an event dispatcher".format(x))
+
+
+class Optional(Spec):
+    """Represent an optional parameter that can either have a value or it can
+    be None.
+
+    Arguments
+    ---------
+        spec: The spec for the value if it is not None
+        default: The returned value in case it is None
+    """
+    def __init__(self, spec, default=None):
+        self._other_spec = spec
+        self._default = default
+
+    def __repr__(self):
+        return "Optional[{!r}, {!r}]".format(self._other_spec, self._default)
+
+    def get(self, x):
+        if x is None:
+            return self._default
+        return self._other_spec.get(x)
+
+    def __eq__(self, x):
+        if isinstance(x, Optional):
+            return (
+                self._other_spec == x._other_spec and
+                self._default == x._default
+            )
+        return False
+
+
+Int = Spec(int, "Int")
+Float = Spec(float, "Float")
+Bool = Spec(bool, "Bool")
+Callable = _Callable()
+EventDispatcherInstance = _EventDispatcherInstance()
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/builders/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/builders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1899a8903406406ed651875ec0beefb11f0eb97f
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/builders/__init__.py
@@ -0,0 +1,59 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""This module implements builders that simplify building complex transformer
+architectures with different attention mechanisms.
+
+The main idea is to facilitate the construction of various attention layers and
+transformer encoder layers and simplify their assembly into one transformer
+module. It also allows for flexibility in the scripts as many builder
+parameters can correspond 1-1 with command line arguments.
+
+Example usage:
+
+    builder = TransformerEncoderBuilder()
+    builder.n_layers = 12
+    builder.n_heads = 8
+    builder.feed_forward_dimensions = 1024
+    builder.query_dimensions = 64
+    builder.value_dimensions = 64
+    builder.dropout = 0.1
+    builder.attention_dropout = 0.1
+    builder.attention_type = "linear"
+    transformer = builder.get()
+"""
+
+__all__ = [
+    "AttentionBuilder",
+    "RecurrentAttentionBuilder",
+    "RecurrentCrossAttentionBuilder"
+]
+
+# Import the attention implementations so that they register themselves with
+# the builder. Attention implementations external to the library should be
+# imported before using the builders.
+#
+# TODO: Should this behaviour change? Namely, should all attention
+#       implementations be imported in order to be useable? This also allows
+#       using the library even partially built, for instance.
+from ..attention import \
+    FullAttention, \
+    LinearAttention
+
+del FullAttention, \
+    LinearAttention
+
+
+from .attention_builders import \
+    AttentionBuilder, \
+    RecurrentAttentionBuilder, \
+    RecurrentCrossAttentionBuilder
+
+from .transformer_builders import \
+    TransformerEncoderBuilder, \
+    RecurrentEncoderBuilder, \
+    TransformerDecoderBuilder, \
+    RecurrentDecoderBuilder
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/builders/attention_builders.py b/models/smi_ted/smi_ted_light/fast_transformers/builders/attention_builders.py
new file mode 100644
index 0000000000000000000000000000000000000000..07ecc6ba72a973cab1345c133a6f4ad07de459e3
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/builders/attention_builders.py
@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+from collections import defaultdict
+
+from .base import BaseBuilder
+from ..attention_registry import \
+    AttentionRegistry, \
+    RecurrentAttentionRegistry, \
+    RecurrentCrossAttentionRegistry
+
+
+class BaseAttentionBuilder(BaseBuilder):
+    def __init__(self, registry):
+        self._registry = registry
+        self._parameters = defaultdict(lambda: None)
+
+    @property
+    def available_attentions(self):
+        """Return a list with the available attention implementations."""
+        return self._registry.keys
+
+    def validate_attention_type(self, attention_type):
+        """Parse the attention type according to the rules used by `get()` and
+        check if the requested attention is constructible."""
+        return all(
+            all(t in self._registry for t in a.split(","))
+            for a in attention_type.split(":")
+        )
+
+    def __setattr__(self, key, value):
+        # Make sure we have normal behaviour for the class members _registry
+        # and _parameters
+        if key in ["_registry", "_parameters"]:
+            return object.__setattr__(self, key, value)
+
+        # Assign everything else in the parameters dictionary
+        if not self._registry.contains_parameter(key):
+            raise AttributeError(("{!r} is not a valid attention "
+                                  "parameter name").format(key))
+        self._parameters[key] = self._registry.validate_parameter(key, value)
+
+    def __getattr__(self, key):
+        if key in self._parameters:
+            return self._parameters[key]
+        else:
+            raise AttributeError()
+
+    def __repr__(self):
+        return (
+            "{}.from_kwargs(\n".format(self.__class__.__name__) +
+            "\n".join(["    {}={!r},".format(k, v)
+                       for k, v in self._parameters.items()])[:-1] +
+            "\n)"
+        )
+
+    def get(self, attention_type):
+        """Construct the attention implementation object and return it.
+
+        The passed in attention_type argument defines the attention to be
+        created. It should be a string and in its simplest form it should
+        be one of the available choices from `available_attentions`.
+
+        However, to enable attention decoration, namely an attention
+        implementation augmenting the functionality of another implementation,
+        the attention type can be a colon separated list of compositions like
+        the following examples:
+
+            - 'att1' means instantiate att1
+            - 'att2:att1' means instantiate att1 and decorate it with att2
+            - 'att3:att1,att4' means instantiate att1 and att4 and decorate
+              them with att3
+
+        Arguments
+        ---------
+            attention_type: A string that contains one or more keys from
+                            `available_attentions` separated with a colon to
+                            denote the decoration pattern.
+        """
+        compositions = reversed(attention_type.split(":"))
+        attentions = []
+        for c in compositions:
+            attentions = [
+                self._construct_attention(t, attentions)
+                for t in c.split(",")
+            ]
+        if len(attentions) > 1:
+            raise ValueError(("Invalid attention_type argument "
+                              "{!r}").format(attention_type))
+        return attentions[0]
+
+    def _construct_attention(self, attention_type, decorated=[]):
+        """Construct an attention implementation object.
+
+        Arguments
+        ---------
+            attention_type: A string that contains a single key from the
+                            `available_attentions`
+            decorated: A list of attention implementations to pass as arguments
+                       to be decorated
+        """
+        if attention_type not in self._registry:
+            raise ValueError(("Unknown attention type "
+                              "{!r}").format(attention_type))
+
+        attention, parameters = self._registry[attention_type]
+        parameter_dictionary = {
+            p: self._registry.validate_parameter(p, self._parameters[p])
+            for p in parameters
+        }
+
+        return attention(*decorated, **parameter_dictionary)
+
+
+class AttentionBuilder(BaseAttentionBuilder):
+    """Build attention implementations for batch sequence processing or
+    training."""
+    def __init__(self):
+        super(AttentionBuilder, self).__init__(AttentionRegistry)
+
+
+class RecurrentAttentionBuilder(BaseAttentionBuilder):
+    """Build attention implementations for autoregressive sequence
+    processing."""
+    def __init__(self):
+        super(RecurrentAttentionBuilder, self).__init__(
+            RecurrentAttentionRegistry
+        )
+
+
+class RecurrentCrossAttentionBuilder(BaseAttentionBuilder):
+    """Build attention implementations for autoregressive cross attention
+    computation."""
+    def __init__(self):
+        super(RecurrentCrossAttentionBuilder, self).__init__(
+            RecurrentCrossAttentionRegistry
+        )
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/builders/base.py b/models/smi_ted/smi_ted_light/fast_transformers/builders/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..576dbe9e59d7f3bb7012abfbe776abdd1ae1f195
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/builders/base.py
@@ -0,0 +1,67 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Provide a class for the others to inherit some useful functionality."""
+
+
+class BaseBuilder(object):
+    @classmethod
+    def from_kwargs(cls, **kwargs):
+        """Construct a builder and set all the keyword arguments as parameters.
+
+        The keyword argument strict is passed to
+        BaseBuilder.from_dictionary separately.
+
+        See BaseBuilder.from_dictionary().
+        """
+        strict = kwargs.pop("strict", True)
+        return cls.from_dictionary(kwargs, strict=strict)
+
+    @classmethod
+    def from_namespace(cls, args, strict=False):
+        """Construct a builder from an argparse Namespace.
+
+        To be used for building transformers from command line arguments.
+
+        See BaseBuilder.from_dictionary().
+        """
+        return cls.from_dictionary(vars(args), strict=strict)
+
+    @classmethod
+    def from_dictionary(cls, dictionary, strict=True):
+        """Construct a builder and set all the parameters in the dictionary.
+
+        Given a dictionary
+
+            d = {"foo": "bar"}
+
+        then
+
+            builder = TransformerEncoderBuilder.from_dictionary(d)
+
+        is equivalent to
+
+            builder = TransformerEncoderBuilder()
+            builder.foo = "bar"
+
+        Arguments
+        ---------
+            dictionary: A dictionary of parameters to set to the builder.
+            strict: bool, If a key is not a parameter and strict is set to True
+                    then a ValueError is raised, otherwise that dictionary key
+                    is ignored (default: True)
+        """
+        builder = cls()
+        for k, v in dictionary.items():
+            try:
+                setattr(builder, k, v)
+            except AttributeError:
+                if strict:
+                    raise ValueError(("The builder has no "
+                                      "parameter {!r}").format(k))
+                else:
+                    continue
+        return builder
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/builders/transformer_builders.py b/models/smi_ted/smi_ted_light/fast_transformers/builders/transformer_builders.py
new file mode 100644
index 0000000000000000000000000000000000000000..27c970cbdb4f203cb241dd1795bdf3bdd338c615
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/builders/transformer_builders.py
@@ -0,0 +1,550 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Build complex transformer architectures for inference or training easily."""
+
+from torch.nn import LayerNorm
+
+from ..attention import AttentionLayer
+from ..transformers import TransformerEncoder, TransformerEncoderLayer, \
+    TransformerDecoder, TransformerDecoderLayer
+from ..recurrent.attention import \
+    RecurrentAttentionLayer, \
+    RecurrentCrossAttentionLayer
+from ..recurrent.transformers import \
+    RecurrentTransformerEncoder, RecurrentTransformerEncoderLayer, \
+    RecurrentTransformerDecoder, RecurrentTransformerDecoderLayer
+from .base import BaseBuilder
+from .attention_builders import AttentionBuilder, RecurrentAttentionBuilder, \
+    RecurrentCrossAttentionBuilder
+
+
+class BaseTransformerBuilder(BaseBuilder):
+    """Contains all the parameters for building a transformer other than the
+    attention part.
+
+    Classes extending the BaseTransformerBuilder should implement the `get()`
+    method that actually builds the transformer.
+    """
+    def __init__(self):
+        # transformer parameters
+        self._n_layers = 4
+        self._n_heads = 4
+        self._d_query = 64
+        self._d_value = 64
+        self._d_ff = 1024
+        self._dropout = 0.1
+        self._activation = "relu"
+        self._final_norm = True
+        self._event_dispatcher = ""  # the default global dispatcher
+
+    @property
+    def n_layers(self):
+        """The number of transformer layers."""
+        return self._n_layers
+
+    @n_layers.setter
+    def n_layers(self, val):
+        self._n_layers = val
+
+    @property
+    def n_heads(self):
+        """The number of heads in each transformer layer."""
+        return self._n_heads
+
+    @n_heads.setter
+    def n_heads(self, val):
+        self._n_heads = val
+
+    @property
+    def feed_forward_dimensions(self):
+        """The dimensions of the fully connected layer in the transformer
+        layers."""
+        return self._d_ff
+
+    @feed_forward_dimensions.setter
+    def feed_forward_dimensions(self, val):
+        self._d_ff = val
+
+    @property
+    def query_dimensions(self):
+        """The dimensions of the queries and keys in each attention layer."""
+        return self._d_query
+
+    @query_dimensions.setter
+    def query_dimensions(self, val):
+        self._d_query = val
+
+    @property
+    def value_dimensions(self):
+        """The dimensions of the values in each attention layer."""
+        return self._d_value
+
+    @value_dimensions.setter
+    def value_dimensions(self, val):
+        self._d_value = val
+
+    @property
+    def dropout(self):
+        """The dropout rate to be applied in the transformer encoder layer."""
+        return self._dropout
+
+    @dropout.setter
+    def dropout(self, val):
+        self._dropout = val
+
+    @property
+    def activation(self):
+        """The activation function for the transformer layer.
+
+        One of {'relu', 'gelu'}.
+        """
+        return self._activation
+
+    @activation.setter
+    def activation(self, val):
+        activations = ["relu", "gelu"]
+        if val not in activations:
+            raise ValueError(("{!r} is not one of the availabel activation "
+                              "types {!r}").format(val, activations))
+        self._activation = val
+
+    @property
+    def final_normalization(self):
+        """Whether to add LayerNorm as the final layer of the
+        TransformerEncoder."""
+        return self._final_norm
+
+    @final_normalization.setter
+    def final_normalization(self, val):
+        self._final_norm = bool(val)
+
+    @property
+    def event_dispatcher(self):
+        """The transformer event dispatcher either as a string or as an
+        EventDispatcher object."""
+        return self._event_dispatcher
+
+    @event_dispatcher.setter
+    def event_dispatcher(self, event_dispatcher):
+        self._event_dispatcher = event_dispatcher
+
+    def get(self):
+        """Build the transformer and return it."""
+        raise NotImplementedError()
+
+
+class BaseTransformerEncoderBuilder(BaseTransformerBuilder):
+    """Implement the logic of building a transformer encoder but leave the
+    specific layers open for changing by the inheriting classes. This allows us
+    to reuse the logic for creating both the TransformerEncoder and the
+    RecurrentTransformerEncoder.
+
+    Inheriting classes should implement the following:
+
+    - _get_attention_builder()
+    - _get_attention_layer_class()
+    - _get_encoder_class()
+    - _get_encoder_layer_class()
+    """
+    def __init__(self):
+        super(BaseTransformerEncoderBuilder, self).__init__()
+        self._attention_builder = self._get_attention_builder()
+        self._attention_type = "full"
+
+    def _get_attention_builder(self):
+        """Return an instance of the appropriate attention builder."""
+        raise NotImplementedError()
+
+    def _get_attention_layer_class(self):
+        """Return the class for the layer that projects queries keys and
+        values."""
+        raise NotImplementedError()
+
+    def _get_encoder_class(self):
+        """Return the class for the transformer encoder."""
+        raise NotImplementedError()
+
+    def _get_encoder_layer_class(self):
+        """Return the class for the transformer encoder layer."""
+        raise NotImplementedError()
+
+    @property
+    def attention(self):
+        """The attention builder instance."""
+        return self._attention_builder
+
+    @property
+    def attention_type(self):
+        """The attention implementation chosen."""
+        return self._attention_type
+
+    @attention_type.setter
+    def attention_type(self, val):
+        if not self._attention_builder.validate_attention_type(val):
+            raise ValueError(("{!r} is not an available attention "
+                              "type").format(val))
+        self._attention_type = val
+
+    def __setattr__(self, key, val):
+        # "protected" attributes are settable (probably from withing the class)
+        if key[0] == "_":
+            return super().__setattr__(key, val)
+
+        # Existing attributes are settable but they might also be attention
+        # parameters so try that as well
+        fail_on_exception = True
+        if hasattr(self, key):
+            super().__setattr__(key, val)
+            fail_on_exception = False
+
+        # Non-existing "public" attributes may be attention parameters
+        try:
+            setattr(self._attention_builder, key, val)
+        except:
+            if fail_on_exception:
+                raise
+
+    def get(self):
+        """Build the transformer and return it."""
+        # Set the event dispatcher to the attention builder
+        self.attention.event_dispatcher = self.event_dispatcher
+
+        # Extract into local variables the classes to be used
+        Encoder = self._get_encoder_class()
+        EncoderLayer = self._get_encoder_layer_class()
+        Attention = self._get_attention_layer_class()
+
+        model_dimensions = self.value_dimensions*self.n_heads
+        return Encoder(
+            [
+                EncoderLayer(
+                    Attention(
+                        self.attention.get(self.attention_type),
+                        model_dimensions,
+                        self.n_heads,
+                        d_keys=self.query_dimensions,
+                        d_values=self.value_dimensions,
+                        event_dispatcher=self.event_dispatcher
+                    ),
+                    model_dimensions,
+                    self.feed_forward_dimensions,
+                    self.dropout,
+                    self.activation,
+                    event_dispatcher=self.event_dispatcher
+                )
+                for _ in range(self.n_layers)
+            ],
+            (LayerNorm(model_dimensions) if self.final_normalization else None),
+            event_dispatcher=self.event_dispatcher
+        )
+
+
+class TransformerEncoderBuilder(BaseTransformerEncoderBuilder):
+    """Build a batch transformer encoder for training or processing of
+    sequences all elements at a time.
+
+    Example usage:
+
+        builder = TransformerEncoderBuilder()
+        builder.n_layers = 12
+        builder.n_heads = 8
+        builder.feed_forward_dimensions = 1024
+        builder.query_dimensions = 64
+        builder.value_dimensions = 64
+        builder.dropout = 0.1
+        builder.attention_dropout = 0.1
+        builder.attention_type = "linear"
+        transformer = builder.get()
+    """
+    def _get_attention_builder(self):
+        """Return an instance of the appropriate attention builder."""
+        return AttentionBuilder()
+
+    def _get_attention_layer_class(self):
+        """Return the class for the layer that projects queries keys and
+        values."""
+        return AttentionLayer
+
+    def _get_encoder_class(self):
+        """Return the class for the transformer encoder."""
+        return TransformerEncoder
+
+    def _get_encoder_layer_class(self):
+        """Return the class for the transformer encoder layer."""
+        return TransformerEncoderLayer
+
+
+class RecurrentEncoderBuilder(BaseTransformerEncoderBuilder):
+    """Build a transformer encoder for autoregressive processing of sequences.
+
+    Example usage:
+
+        builder = RecurrentEncoderBuilder()
+        builder.n_layers = 12
+        builder.n_heads = 8
+        builder.feed_forward_dimensions = 1024
+        builder.query_dimensions = 64
+        builder.value_dimensions = 64
+        builder.dropout = 0.1
+        builder.attention_dropout = 0.1
+        builder.attention_type = "linear"
+        transformer = builder.get()
+    """
+    def _get_attention_builder(self):
+        """Return an attention builder for recurrent attention."""
+        return RecurrentAttentionBuilder()
+
+    def _get_attention_layer_class(self):
+        """Return the class for the recurrent layer that projects queries keys
+        and values."""
+        return RecurrentAttentionLayer
+
+    def _get_encoder_class(self):
+        """Return the class for the recurrent transformer encoder."""
+        return RecurrentTransformerEncoder
+
+    def _get_encoder_layer_class(self):
+        """Return the class for the recurrent transformer encoder layer."""
+        return RecurrentTransformerEncoderLayer
+
+
+class BaseTransformerDecoderBuilder(BaseTransformerBuilder):
+    """Similar to BaseTransformerEncoderBuilder implement the logic of
+    building the transformer decoder without defining concrete layers.
+
+    Inheriting classes should implement the following:
+
+    - _get_self_attention_builder() and _get_cross_attention_builder()
+    - _get_self_attention_layer_class() and _get_cross_attention_layer_class()
+    - _get_decoder_class()
+    - _get_decoder_layer_class()
+    """
+    def __init__(self):
+        super(BaseTransformerDecoderBuilder, self).__init__()
+        self._self_attention_builder = self._get_self_attention_builder()
+        self._cross_attention_builder = self._get_cross_attention_builder()
+        self._self_attention_type = "full"
+        self._cross_attention_type = "full"
+
+    def _get_self_attention_builder(self):
+        """Return an instance of attention builder."""
+        raise NotImplementedError()
+
+    def _get_cross_attention_builder(self):
+        """Return an instance of attention builder."""
+        raise NotImplementedError()
+
+    def _get_self_attention_layer_class(self):
+        """Return a class to project the queries, keys and values to
+        multi-head versions."""
+        raise NotImplementedError()
+
+    def _get_cross_attention_layer_class(self):
+        """Return a class to project the queries, keys and values to
+        multi-head versions."""
+        raise NotImplementedError()
+
+    def _get_decoder_class(self):
+        """Return the class for the transformer decoder."""
+        raise NotImplementedError()
+
+    def _get_decoder_layer_class(self):
+        """Return the class for the transformer decoder layer."""
+        raise NotImplementedError()
+
+    @property
+    def self_attention(self):
+        """The attention builder instance that will be used for the self
+        attention modules."""
+        return self._self_attention_builder
+
+    @property
+    def self_attention_type(self):
+        """The attention implementation used for self attention."""
+        return self._self_attention_type
+
+    @self_attention_type.setter
+    def self_attention_type(self, val):
+        if not self._self_attention_builder.validate_attention_type(val):
+            raise ValueError(("{!r} is not an available self attention "
+                              "type").format(val))
+        self._self_attention_type = val
+
+    @property
+    def cross_attention(self):
+        """The attention builder instance that will be used for the cross
+        attention modules."""
+        return self._cross_attention_builder
+
+    @property
+    def cross_attention_type(self):
+        """The attention implementation used for cross attention."""
+        return self._cross_attention_type
+
+    @cross_attention_type.setter
+    def cross_attention_type(self, val):
+        if not self._cross_attention_builder.validate_attention_type(val):
+            raise ValueError(("{!r} is not an available cross attention "
+                              "type").format(val))
+        self._cross_attention_type = val
+
+    def __setattr__(self, key, val):
+        # "protected" attributes are settable (probably from withing the class)
+        if key[0] == "_":
+            return super().__setattr__(key, val)
+
+        # Existing attributes are settable but they might also be attention
+        # parameters so try that as well
+        fail_on_exception = True
+        if hasattr(self, key):
+            super().__setattr__(key, val)
+            fail_on_exception = False
+
+        # Non-existing "public" attributes may be attention parameters
+        try:
+            setattr(self._self_attention_builder, key, val)
+            setattr(self._cross_attention_builder, key, val)
+        except:
+            if fail_on_exception:
+                raise
+
+    def get(self):
+        """Build the transformer and return it."""
+        # Set the event dispatcher to attention builders
+        self.self_attention.event_dispatcher = self.event_dispatcher
+        self.cross_attention.event_dispatcher = self.event_dispatcher
+
+        # Extract into local variables the classes to be used
+        Decoder = self._get_decoder_class()
+        DecoderLayer = self._get_decoder_layer_class()
+        SelfAttention = self._get_self_attention_layer_class()
+        CrossAttention = self._get_cross_attention_layer_class()
+
+        model_dimensions = self.value_dimensions*self.n_heads
+        return Decoder(
+            [
+                DecoderLayer(
+                    SelfAttention(
+                        self.self_attention.get(self.self_attention_type),
+                        model_dimensions,
+                        self.n_heads,
+                        d_keys=self.query_dimensions,
+                        d_values=self.value_dimensions,
+                        event_dispatcher=self.event_dispatcher
+                    ),
+                    CrossAttention(
+                        self.cross_attention.get(self.cross_attention_type),
+                        model_dimensions,
+                        self.n_heads,
+                        d_keys=self.query_dimensions,
+                        d_values=self.value_dimensions,
+                        event_dispatcher=self.event_dispatcher
+                    ),
+                    model_dimensions,
+                    self.feed_forward_dimensions,
+                    self.dropout,
+                    self.activation,
+                    event_dispatcher=self.event_dispatcher
+                )
+                for _ in range(self.n_layers)
+            ],
+            (LayerNorm(model_dimensions) if self.final_normalization else None),
+            event_dispatcher=self.event_dispatcher
+        )
+
+
+class TransformerDecoderBuilder(BaseTransformerDecoderBuilder):
+    """Build a transformer decoder for training or processing of sequences all
+    elements at a time.
+
+    Example usage:
+
+        builder = TransformerDecoderBuilder()
+        builder.n_layers = 12
+        builder.n_heads = 8
+        builder.feed_forward_dimensions = 1024
+        builder.query_dimensions = 64
+        builder.value_dimensions = 64
+        builder.dropout = 0.1
+        builder.attention_dropout = 0.1
+        builder.self_attention_type = "full"
+        builder.cross_attention_type = "full"
+        transformer = builder.get()
+    """
+    def _get_self_attention_builder(self):
+        """Return an attention builder for creating non-recurrent attention
+        variants."""
+        return AttentionBuilder()
+
+    def _get_cross_attention_builder(self):
+        """Return an attention builder for creating non-recurrent attention
+        variants."""
+        return AttentionBuilder()
+
+    def _get_self_attention_layer_class(self):
+        """Return the non-recurrent attention layer to project queries, keys
+        and values."""
+        return AttentionLayer
+
+    def _get_cross_attention_layer_class(self):
+        """Return the non-recurrent attention layer to project queries, keys
+        and values."""
+        return AttentionLayer
+
+    def _get_decoder_class(self):
+        """Return the transformer decoder class."""
+        return TransformerDecoder
+
+    def _get_decoder_layer_class(self):
+        """Return the transformer decoder layer class."""
+        return TransformerDecoderLayer
+
+
+class RecurrentDecoderBuilder(BaseTransformerDecoderBuilder):
+    """Build a transformer decoder for processing of sequences in
+    autoregressive fashion.
+
+    Example usage:
+
+        builder = RecurrentDecoderBuilder()
+        builder.n_layers = 12
+        builder.n_heads = 8
+        builder.feed_forward_dimensions = 1024
+        builder.query_dimensions = 64
+        builder.value_dimensions = 64
+        builder.dropout = 0.1
+        builder.attention_dropout = 0.1
+        builder.self_attention_type = "full"
+        builder.cross_attention_type = "full"
+        transformer = builder.get()
+    """
+    def _get_self_attention_builder(self):
+        """Return an attention builder for creating non-recurrent attention
+        variants."""
+        return RecurrentAttentionBuilder()
+
+    def _get_cross_attention_builder(self):
+        """Return an attention builder for creating non-recurrent attention
+        variants."""
+        return RecurrentCrossAttentionBuilder()
+
+    def _get_self_attention_layer_class(self):
+        """Return the non-recurrent attention layer to project queries, keys
+        and values."""
+        return RecurrentAttentionLayer
+
+    def _get_cross_attention_layer_class(self):
+        """Return the non-recurrent attention layer to project queries, keys
+        and values."""
+        return RecurrentCrossAttentionLayer
+
+    def _get_decoder_class(self):
+        """Return the transformer decoder class."""
+        return RecurrentTransformerDecoder
+
+    def _get_decoder_layer_class(self):
+        """Return the transformer decoder layer class."""
+        return RecurrentTransformerDecoderLayer
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/causal_product/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/causal_product/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d6973d73ab50ee26f3ff17f7334a441aaca61d6
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/causal_product/__init__.py
@@ -0,0 +1,78 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+import torch
+
+from .causal_product_cpu import causal_dot_product as causal_dot_product_cpu, \
+    causal_dot_backward as causal_dot_backward_cpu
+
+try:
+    from .causal_product_cuda import \
+        causal_dot_product as causal_dot_product_cuda, \
+        causal_dot_backward as causal_dot_backward_cuda
+except ImportError:
+    causal_dot_product_cuda = causal_dot_backward_cuda = None
+
+
+class CausalDotProduct(torch.autograd.Function):
+    """Compute the weighted sum of values but attending only to previous
+    values."""
+    dot = {
+        "cpu": causal_dot_product_cpu,
+        "cuda": causal_dot_product_cuda
+    }
+    dot_backward = {
+        "cpu": causal_dot_backward_cpu,
+        "cuda": causal_dot_backward_cuda
+    }
+
+    @staticmethod
+    def forward(ctx, Q, K, V):
+        # Save the inputs for the gradient computation
+        ctx.save_for_backward(Q, K, V)
+
+        # Create the output tensor
+        device = Q.device
+        N, H, L, _ = Q.shape
+        _, _, _, M = V.shape
+        product = torch.zeros((N, H, L, M), device=device)
+
+        # Actually perform the dot product
+        CausalDotProduct.dot[device.type](
+            Q.data,
+            K.data,
+            V.data,
+            product
+        )
+
+        return product
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        # Extract the saved tensors
+        Q, K, V = ctx.saved_tensors
+
+        # Allocate memory for the gradients
+        grad_Q = torch.zeros_like(Q)
+        grad_K = torch.zeros_like(K)
+        grad_V = torch.zeros_like(V)
+
+        # Actually compute the gradients
+        CausalDotProduct.dot_backward[Q.device.type](
+            Q.data,
+            K.data,
+            V.data,
+            grad_out,
+            grad_Q,
+            grad_K,
+            grad_V
+        )
+
+        return grad_Q, grad_K, grad_V
+
+
+# Alias the autograd functions to python style snake case naming
+causal_dot_product = CausalDotProduct.apply
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/causal_product/causal_product_cpu.cpython-39-x86_64-linux-gnu.so b/models/smi_ted/smi_ted_light/fast_transformers/causal_product/causal_product_cpu.cpython-39-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..58ca3901016b151d88298dcc48c8405a83176c66
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/clustering/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/clustering/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/clustering/hamming/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/clustering/hamming/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..84e2f92998c39ab72740573d69b1f9e50683c2b1
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/clustering/hamming/__init__.py
@@ -0,0 +1,115 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+
+import numpy as np
+
+import torch
+
+from .cluster_cpu import cluster as cluster_cpu
+try:
+    from .cluster_cuda import cluster as cluster_gpu
+except ImportError:
+    pass
+
+
+def cluster(
+    hashes,
+    lengths,
+    groups=None,
+    counts=None,
+    centroids=None,
+    distances=None,
+    bitcounts=None,
+    clusters=30,
+    iterations=10,
+    bits=32
+):
+    """Cluster hashes using a few iterations of K-Means with hamming distance.
+
+    All the tensors default initialized to None are optional buffers to avoid
+    memory allocations. distances and bitcounts are only used by the CUDA
+    version of this call. clusters will be ignored if centroids is provided.
+
+    Arguments
+    ---------
+        hashes: A long tensor of shape (N, H, L) containing a hashcode for each
+                query.
+        lengths: An int tensor of shape (N,) containing the sequence length for
+                 each sequence in hashes.
+        groups: An int tensor buffer of shape (N, H, L) contaning the cluster
+                in which the corresponding hash belongs to.
+        counts: An int tensor buffer of shape (N, H, K) containing the number
+                of elements in each cluster.
+        centroids: A long tensor buffer of shape (N, H, K) containing the
+                   centroid for each cluster.
+        distances: An int tensor of shape (N, H, L) containing the distance to
+                   the closest centroid for each hash.
+        bitcounts: An int tensor of shape (N, H, K, bits) containing the number
+                   of elements that have 1 for a given bit.
+        clusters: The number of clusters to use for each sequence. It is
+                  ignored if centroids is not None.
+        iterations: How many k-means iterations to perform.
+        bits: How many of the least-significant bits in hashes to consider.
+
+    Returns
+    -------
+        groups and counts as defined above.
+    """
+    device = hashes.device
+    N, H, L = hashes.shape
+
+    # Unfortunately cpu and gpu have different APIs so the entire call must be
+    # surrounded by an if-then-else
+    if device.type == "cpu":
+        if groups is None:
+            groups = torch.empty((N, H, L), dtype=torch.int32)
+        if centroids is None:
+            centroids = torch.empty((N, H, clusters), dtype=torch.int64)
+            centroids = hashes[:, :, np.random.choice(L, size=[clusters], replace=False)]
+        K = centroids.shape[2]
+        if counts is None:
+            counts = torch.empty((N, H, K), dtype=torch.int32)
+
+        cluster_cpu(
+            hashes, lengths,
+            centroids, groups, counts,
+            iterations, bits
+        )
+
+        return groups, counts
+
+    else:
+        if groups is None:
+            groups = torch.empty((N, H, L), dtype=torch.int32, device=device)
+        if centroids is None:
+            centroids = torch.empty((N, H, clusters), dtype=torch.int64,
+                                    device=device)
+            centroids = hashes[:, :, np.random.choice(L, size=[clusters], replace=False)]
+        K = centroids.numel() // N // H
+        #K = clusters
+        if counts is None:
+            counts = torch.empty((N, H, K), dtype=torch.int32, device=device)
+        if distances is None:
+            distances = torch.empty((N, H, L), dtype=torch.int32,
+                                    device=device)
+        if bitcounts is None:
+            bitcounts = torch.empty((N, H, K, bits), dtype=torch.int32,
+                                    device=device)
+        groups = groups.view(N, H, L)
+        counts = counts.view(N, H, K)
+        centroids = centroids.view(N, H, K)
+        distances = distances.view(N, H, L)
+        bitcounts = bitcounts.view(N, H, K, -1)
+
+        cluster_gpu(
+            hashes, lengths,
+            centroids, distances, bitcounts, groups, counts,
+            iterations, bits
+        )
+
+        return groups, counts
+        
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/clustering/hamming/cluster_cpu.cpython-39-x86_64-linux-gnu.so b/models/smi_ted/smi_ted_light/fast_transformers/clustering/hamming/cluster_cpu.cpython-39-x86_64-linux-gnu.so
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/events/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/events/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f567baf32fe36048a71c93b05f026b4544448a8c
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/events/__init__.py
@@ -0,0 +1,10 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""This module implements a basic event system that allows the transformer
+internal components to make available any tensor with minimal overhead."""
+
+from .event import Event, AttentionEvent, QKVEvent
+from .event_dispatcher import EventDispatcher
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/events/event.py b/models/smi_ted/smi_ted_light/fast_transformers/events/event.py
new file mode 100644
index 0000000000000000000000000000000000000000..494aaa8073a5b096325c3a1b3e2108adafc0fa6c
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/events/event.py
@@ -0,0 +1,51 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+
+class Event(object):
+    """The Event is the base class for all events that are dispatched from any
+    transformer module.
+
+    This class defines only the basic attributes of an event without any
+    payload.
+
+    Arguments
+    ---------
+        source: torch.nn.Module instance that dispatched this event
+    """
+    def __init__(self, source):
+        self.source = source
+
+
+class AttentionEvent(Event):
+    """An event containing an attention matrix.
+
+    Arguments
+    ---------
+        source: torch.nn.Module instance that dispatched this event
+        attention_matrix: torch.tensor of the multihead attention matrix
+                          computed in the corresponding attention layer
+    """
+    def __init__(self, source, attention_matrix):
+        super(AttentionEvent, self).__init__(source)
+        self.attention_matrix = attention_matrix
+
+
+class QKVEvent(Event):
+    """An event containing the queries, keys and values projected in their
+    multiple heads.
+
+    Arguments
+    ---------
+        source: torch.nn.Module instance that dispatched this event
+        queries: torch.tensor containing the queries in shape NLHE
+        keys: torch.tensor containing the keys in shape NSHE
+        values: torch.tensor containing the values in shape NSHD
+    """
+    def __init__(self, source, queries, keys, values):
+        super(QKVEvent, self).__init__(source)
+        self.queries = queries
+        self.keys = keys
+        self.values = values
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/events/event_dispatcher.py b/models/smi_ted/smi_ted_light/fast_transformers/events/event_dispatcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..18b1b78e00c1ad13a79be8452afb873e4b2785ba
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/events/event_dispatcher.py
@@ -0,0 +1,92 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+from collections import OrderedDict
+
+from .event import Event
+from .filters import event_class
+
+
+class EventDispatcher(object):
+    """An EventDispatcher is a simple way to implement an observer pattern for
+    loose coupling of components. In our case it is used so that the internals
+    of large neural networks can communicate with the outside world in an
+    agnostic and efficient way.
+
+    Example usage
+    -------------
+
+        from fast_transformers.events import EventDispatcher, AttentionEvent
+        from fast_transformers.events.filters import \
+            layer_name_contains
+
+        def attention_event_handler(event):
+            print(event.attention_matrix)
+
+        ed = EventDispatcher()
+        ed.listen(AttentionEvent, attention_event_handler)
+        ed.listen(
+            AttentionEvent & layer_name_contains("layers.12"),
+            attention_event_handler
+        )
+    """
+    _dispatchers = {}
+
+    def __init__(self):
+        self._listeners = OrderedDict()
+
+    def listen(self, event_filter, event_handler):
+        """Add an event handler for the events that pass the event filter.
+
+        Arguments
+        ---------
+            event_filter: callable or Event class to define for which events
+                          this handler will be called
+            event_handler: callable that accepts an instance of Event
+        """
+        if isinstance(event_filter, type) and issubclass(event_filter, Event):
+            event_filter = event_class(event_filter)
+
+        self._listeners[event_handler] = event_filter
+
+    def remove(self, event_handler):
+        """Remove the event_handler from the listeners so that no more events
+        are dispatched to this handler."""
+        self._listeners.pop(event_handler, None)
+
+    def clear(self):
+        """Remove all listeners from the event dispatcher."""
+        self._listeners.clear()
+
+    def dispatch(self, event):
+        """Dispatch an event to the listeners.
+
+        Arguments
+        ---------
+            event: Event instance
+        """
+        for event_handler, event_filter in self._listeners.items():
+            if event_filter(event):
+                event_handler(event)
+
+    @classmethod
+    def get(cls, key=""):
+        """Factory method for creating global event dispatchers for loosely
+        coupling parts of a larger codebase.
+
+        Since global objects are a complete antipattern, we suggest that this
+        is only used to set a default value for an event dispatcher passed as
+        an argument.
+
+        Argument
+        --------
+            key: A key to uniquely identify a dispatcher or an instance of a
+                 dispatcher to be returned as is
+        """
+        if isinstance(key, cls):
+            return key
+        if key not in cls._dispatchers:
+            cls._dispatchers[key] = cls()
+        return cls._dispatchers[key]
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/events/filters.py b/models/smi_ted/smi_ted_light/fast_transformers/events/filters.py
new file mode 100644
index 0000000000000000000000000000000000000000..2eb85e6fe6e990feab82ed7befdc65d95f4499a5
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/events/filters.py
@@ -0,0 +1,141 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Define composable functions to filter events."""
+
+import weakref
+
+from .event import Event
+
+
+class EventFilter(object):
+    """EventFilter instances are predicates (ie functions that return True or
+    False) to be used with an event dispatcher for filtering event
+    instances.
+
+    The main benefit from using raw functions is that an EventFilter composes
+    very easily using operators such as &, |, ~.
+
+    Example
+    --------
+
+        event_filter = AttentionEvent | layer_name_contains("layers.1")
+        event_filter = from_layer(transformer.layers[2].attention)
+        event_filter = (
+            AttentionEvent &
+            lambda ev: torch.isnan(ev.attention_matrix).any()
+        )
+    """
+    def __call__(self, event):
+        raise NotImplementedError()
+
+    def _to_event_filter(self, other):
+        if isinstance(other, EventFilter):
+            return other
+        if isinstance(other, type) and issubclass(other, Event):
+            return event_class(other)
+        if callable(other):
+            return CallableEventFilter(other)
+
+        return NotImplemented
+
+    def __and__(self, other):
+        other = self._to_event_filter(other)
+        if other is NotImplemented:
+            return other
+        return CallableEventFilter(lambda ev: self(ev) and other(ev))
+
+    def __rand__(self, other):
+        other = self._to_event_filter(other)
+        if other is NotImplemented:
+            return other
+        return CallableEventFilter(lambda ev: other(ev) and self(ev))
+
+    def __or__(self, other):
+        other = self._to_event_filter(other)
+        if other is NotImplemented:
+            return other
+        return CallableEventFilter(lambda ev: self(ev) or other(ev))
+
+    def __ror__(self, other):
+        other = self._to_event_filter(other)
+        if other is NotImplemented:
+            return other
+        return CallableEventFilter(lambda ev: other(ev) or self(ev))
+
+    def __invert__(self):
+        return CallableEventFilter(lambda ev: not self(ev))
+
+
+class CallableEventFilter(EventFilter):
+    """Wrap a function with an EventFilter object."""
+    def __init__(self, event_filter):
+        self._event_filter = event_filter
+
+    def __call__(self, event):
+        return self._event_filter(event)
+
+
+class LayerNameEventFilter(EventFilter):
+    """A LayerNameEventFilter allows to filter events based on a human readable
+    name of the layer that emitted them.
+
+    Note that LayerNameEventFilter keeps a weak reference to all modules which
+    means that it cannot be used to prevent modules from being garbage
+    collected.
+
+    Arguments
+    ---------
+        root: torch.nn.Module instance that represents the root container
+        name_filter: callable, that returns true if the name 
+    """
+    def __init__(self, root, name_filter):
+        self._names = {
+            weakref.ref(m): n
+            for n, m in root.named_modules()
+        }
+        self._name_filter = name_filter
+
+    def __call__(self, event):
+        name = self._names.get(weakref.ref(event.source), None)
+        if name is None:
+            return False
+        return self._name_filter(name)
+
+
+def event_class(klass):
+    """Select events that are instances of `klass`.
+
+    Arguments
+    ---------
+        klass: A class to check the event instance against
+
+    Returns
+    -------
+        An instance of EventFilter
+    """
+    return CallableEventFilter(lambda ev: isinstance(ev, klass))
+
+
+def from_layer(layer):
+    """Select events that are dispatched from the `layer`.
+
+    Arguments
+    ---------
+        layer: An instance of torch.nn.Module to check against the event source
+
+    Returns
+    -------
+        An instance of EventFilter
+    """
+    return CallableEventFilter(lambda ev: ev.source is layer)
+
+
+def layer_name_contains(root, name):
+    """Select events that contain `name` in their human readable name.
+
+    We use root.named_modules() to get human readable names for the layers.
+    """
+    return LayerNameEventFilter(root, lambda n: name in n)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..12ebc23497e8f3c25fcd012a8a80178a066f9c19
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/__init__.py
@@ -0,0 +1,12 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Implementations of feature maps to be used with linear attention and causal
+linear attention."""
+
+
+from .base import elu_feature_map, ActivationFunctionFeatureMap
+from .fourier_features import RandomFourierFeatures, Favor, \
+    SmoothedRandomFourierFeatures, GeneralizedRandomFeatures
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/__pycache__/__init__.cpython-310.pyc b/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/__pycache__/__init__.cpython-310.pyc
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/__pycache__/fourier_features.cpython-310.pyc b/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/__pycache__/fourier_features.cpython-310.pyc
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/base.py b/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1a05bcb58e96bbaad0dc46aa2709b4f00beb64c
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/base.py
@@ -0,0 +1,73 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Create the feature map interface and some commonly used feature maps.
+
+All attention implementations that expect a feature map shall receive a factory
+function that returns a feature map instance when called with the query
+dimensions.
+"""
+
+from functools import partial
+
+import torch
+from torch.nn import Module
+
+
+class FeatureMap(Module):
+    """Define the FeatureMap interface."""
+    def __init__(self, query_dims):
+        super().__init__()
+        self.query_dims = query_dims
+
+    def new_feature_map(self, device):
+        """Create a new instance of this feature map. In particular, if it is a
+        random feature map sample new parameters."""
+        raise NotImplementedError()
+
+    def forward_queries(self, x):
+        """Encode the queries `x` using this feature map."""
+        return self(x)
+
+    def forward_keys(self, x):
+        """Encode the keys `x` using this feature map."""
+        return self(x)
+
+    def forward(self, x):
+        """Encode x using this feature map. For symmetric feature maps it
+        suffices to define this function, but for asymmetric feature maps one
+        needs to define the `forward_queries` and `forward_keys` functions."""
+        raise NotImplementedError()
+
+    @classmethod
+    def factory(cls, *args, **kwargs):
+        """Return a function that when called with the query dimensions returns
+        an instance of this feature map.
+
+        It is inherited by the subclasses so it is available in all feature
+        maps.
+        """
+        def inner(query_dims):
+            return cls(query_dims, *args, **kwargs)
+        return inner
+
+
+class ActivationFunctionFeatureMap(FeatureMap):
+    """Define a feature map that is simply an element-wise activation
+    function."""
+    def __init__(self, query_dims, activation_function):
+        super().__init__(query_dims)
+        self.activation_function = activation_function
+
+    def new_feature_map(self, device):
+        return
+
+    def forward(self, x):
+        return self.activation_function(x)
+
+
+elu_feature_map = ActivationFunctionFeatureMap.factory(
+    lambda x: torch.nn.functional.elu(x) + 1
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/fourier_features.py b/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/fourier_features.py
new file mode 100644
index 0000000000000000000000000000000000000000..b95ed4b6e82303f96e0cd6bcc57ae0ffaf8fe65c
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/feature_maps/fourier_features.py
@@ -0,0 +1,287 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Implement the positive orthogonal random features from the paper
+"Rethinking Attention with Performers" https://arxiv.org/pdf/2009.14794.pdf
+and the traditional random Fourier features that approximate the RBF kernel.
+"""
+
+from math import sqrt, log
+import warnings
+
+import torch
+
+from .base import FeatureMap
+
+
+def orthogonal_random_matrix_(w):
+    """Initialize the matrix w in-place to compute orthogonal random features.
+
+    The matrix is initialized such that its columns are orthogonal to each
+    other (in groups of size `rows`) and their norms is drawn from the
+    chi-square distribution with `rows` degrees of freedom (namely the norm of
+    a `rows`-dimensional vector distributed as N(0, I)).
+
+    Arguments
+    ---------
+        w: float tensor of size (rows, columns)
+    """
+    rows, columns = w.shape
+    start = 0
+    while start < columns:
+        end = min(start+rows, columns)
+        block = torch.randn(rows, rows, device=w.device)
+        norms = torch.sqrt(torch.einsum("ab,ab->a", block, block))
+        Q, _ = torch.qr(block)
+        w[:, start:end] = (
+            Q[:, :end-start] * norms[None, :end-start]
+        )
+        start += rows
+
+
+class RandomFourierFeatures(FeatureMap):
+    """Random Fourier Features for the RBF kernel according to [1].
+
+    [1]: "Weighted Sums of Random Kitchen Sinks: Replacing minimization with
+         randomization in learning" by A. Rahimi and Benjamin Recht.
+
+    Arguments
+    ---------
+        query_dimensions: int, The input query dimensions in order to sample
+                          the noise matrix
+        n_dims: int, The size of the feature map (should be divisible by 2)
+                (default: query_dimensions)
+        softmax_temp: float, The temerature for the Gaussian kernel
+                      approximation exp(-t * |x-y|^2)
+                      (default: 1/sqrt(query_dimensions))
+        orthogonal: bool, When True the random matrix is initialized for
+                    orthogonal random features to reduce the approximation
+                    variance (default: False)
+        redraw: int, Redraw the random matrix every 'redraw' times
+                (default: 1)
+        deterministic_eval: bool, Only redraw the random matrix during training
+                            (default: False)
+    """
+    def __init__(self, query_dimensions, n_dims=None, softmax_temp=None,
+                 orthogonal=False, redraw=1, deterministic_eval=False):
+        super(RandomFourierFeatures, self).__init__(query_dimensions)
+
+        self.n_dims = n_dims or query_dimensions
+        self.query_dimensions = query_dimensions
+        self.orthogonal = orthogonal
+        self.softmax_temp = (
+            1/sqrt(query_dimensions) if softmax_temp is None
+            else softmax_temp
+        )
+        self.redraw = redraw
+        self.deterministic_eval = deterministic_eval
+
+        # Make a buffer for storing the sampled omega
+        self.register_buffer(
+            "omega",
+            torch.zeros(self.query_dimensions, self.n_dims//2)
+        )
+        self._calls = -1
+
+    def new_feature_map(self, device):
+        # If we are not training skip the generation of a new feature map
+        if self.deterministic_eval and not self.training:
+            return
+
+        # Only redraw the new feature map every self.redraw times
+        self._calls += 1
+        if (self._calls % self.redraw) != 0:
+            return
+
+        omega = torch.zeros(
+            self.query_dimensions,
+            self.n_dims//2,
+            device=device
+        )
+        if self.orthogonal:
+            orthogonal_random_matrix_(omega)
+        else:
+            omega.normal_()
+        self.register_buffer("omega", omega)
+
+    def forward(self, x):
+        x = x * sqrt(self.softmax_temp)
+        u = x.unsqueeze(-2).matmul(self.omega).squeeze(-2)
+        phi = torch.cat([torch.cos(u), torch.sin(u)], dim=-1)
+        return phi * sqrt(2/self.n_dims)
+
+
+class SmoothedRandomFourierFeatures(RandomFourierFeatures):
+    """Simply add a constant value to the dot product in order to avoid
+    possible numerical instabilities when the feature map is slightly
+    negative.
+
+    Implements K(x, y) = exp(-|x-y|^2) + s.
+
+    Arguments
+    ---------
+        query_dimensions: int, The input query dimensions in order to sample
+                          the noise matrix
+        n_dims: int, The size of the feature map (should be divisible by 2)
+                (default: query_dimensions)
+        softmax_temp: float, The temerature for the Gaussian kernel
+                      approximation exp(-t * |x-y|^2)
+                      (default: 1/sqrt(query_dimensions))
+        orthogonal: bool, When True the random matrix is initialized for
+                    orthogonal random features to reduce the approximation
+                    variance (default: False)
+        smoothing: float, The smoothing parameter to add to the dot product.
+        redraw: int, Redraw the random matrix every 'redraw' times
+                (default: 1)
+        deterministic_eval: bool, Only redraw the random matrix during training
+                            (default: False)
+    """
+    def __init__(self, query_dimensions, n_dims=None, softmax_temp=None,
+                 orthogonal=False, smoothing=1.0, redraw=1,
+                 deterministic_eval=False):
+        super(SmoothedRandomFourierFeatures, self).__init__(
+            query_dimensions,
+            n_dims=query_dimensions-1 if n_dims is None else n_dims-1,
+            softmax_temp=softmax_temp,
+            orthogonal=orthogonal,
+            redraw=redraw,
+            deterministic_eval=deterministic_eval
+        )
+        self.smoothing = smoothing
+
+    def forward(self, x):
+        y = super().forward(x)
+        smoothing = torch.full(
+            y.shape[:-1] + (1,),
+            self.smoothing,
+            dtype=y.dtype,
+            device=y.device
+        )
+        return torch.cat([y, smoothing], dim=-1)
+
+
+class Favor(RandomFourierFeatures):
+    """Positive orthogonal random features that approximate the softmax kernel.
+
+    Basically implementation of Lemma 1 from "Rethinking Attention with
+    Performers".
+
+    Arguments
+    ---------
+        query_dimensions: int, The input query dimensions in order to sample
+                          the noise matrix
+        n_dims: int, The size of the feature map (should be divisible by 2)
+                (default: query_dimensions)
+        softmax_temp: float, The temerature for the softmax approximation
+                     (default: 1/sqrt(query_dimensions))
+        orthogonal: bool, If set to true then the random matrix should be
+                    orthogonal which results in lower approximation variance
+                    (default: True)
+        stabilize: bool, If set to True subtract the max norm from the
+                   exponentials to make sure that there are no infinities. It
+                   is equivalent to a robust implementation of softmax where
+                   the max is subtracted before the exponentiation.
+                   (default: False)
+        redraw: int, Redraw the random matrix every 'redraw' times
+                (default: 1)
+        deterministic_eval: bool, Only redraw the random matrix during training
+                            (default: False)
+    """
+    def __init__(self, query_dimensions, n_dims=None, softmax_temp=None,
+                 orthogonal=True, stabilize=False, redraw=1,
+                 deterministic_eval=False):
+        super(Favor, self).__init__(query_dimensions, n_dims=n_dims,
+                                    softmax_temp=softmax_temp,
+                                    orthogonal=orthogonal, redraw=redraw,
+                                    deterministic_eval=deterministic_eval)
+        self.stabilize = stabilize
+
+    def _check_sequence_length(self, x):
+        """Check that the 2nd dimension is larger than the 3rd as a heuristic
+        that the sequence length will be larger than the number of heads. If
+        not simply warn of a possible bug."""
+        if len(x.shape) != 4:
+            warnings.warn(("Favor.stabilize is set to True but the input "
+                           "feature does not have the shape (N, L, H, D) "
+                           "which may result in unexpected behaviour"))
+
+        if x.shape[1] < x.shape[2]:
+            warnings.warn(("Favor.stabilize is set to True but the 2nd "
+                           "dimension of the input is smaller than the 3rd "
+                           "which could indicate that the sequence length and "
+                           "the heads are flipped. This may result in incorrect "
+                           "behaviour. The shape of the input is "
+                           "{!r}.").format(x.shape))
+
+    def forward(self, x):
+        x = x * sqrt(self.softmax_temp)
+        norm_x_squared = torch.einsum("...d,...d->...", x, x).unsqueeze(-1)
+        u = x.unsqueeze(-2).matmul(self.omega).squeeze(-2)
+
+        # Compute the offset for the exponential such that h(x) is multiplied
+        # in logspace. In particular, we multiply with exp(-norm_x_squared/2)
+        # and 1/sqrt(self.n_dims)
+        offset = norm_x_squared * 0.5 + 0.5 * log(self.n_dims)
+
+        # If stabilize is True then add the max norm per sequence in order to
+        # ensure that exp_u1 and exp_u2 will be <1.
+        #
+        # NOTE: This is the only part of this feature map that assumes the
+        #       2nd dimension is the sequence length. We call the
+        #       _check_sequence_length dimension function to be able to catch
+        #       some possible bugs ahead of time.
+        if self.stabilize:
+            self._check_sequence_length(norm_x_squared)
+            offset = offset + norm_x_squared.max(1, keepdim=True)[0]
+
+        exp_u1 = torch.exp(u - offset)
+        exp_u2 = torch.exp(-u - offset)
+        phi = torch.cat([exp_u1, exp_u2], dim=-1)
+
+        return phi
+
+
+class GeneralizedRandomFeatures(RandomFourierFeatures):
+    """Implements the generalized random Fourier features from Performers.
+
+    It computes φ(χ) = [f(ω_1 χ), f(ω_2 χ), ..., f(ω_n χ)] where f(.) is the
+    passed in `kernel_fn`.
+
+    Arguments
+    ---------
+        query_dimensions: int, The input query dimensions in order to sample
+                          the noise matrix
+        n_dims: int, The size of the feature map (default: query_dimensions)
+        softmax_temp: float, A normalizer for the dot products that is
+                     multiplied to the input features before the feature map
+                     application (default: 1.0)
+        orthogonal: bool, If set to true then the random matrix should be
+                    orthogonal which results in lower approximation variance
+                    (default: True)
+        kernel_fn: callable, defines the f used for the feature map.
+                   (default: relu)
+        redraw: int, Redraw the random matrix every 'redraw' times
+                (default: 1)
+        deterministic_eval: bool, Only redraw the random matrix during training
+                            (default: False)
+    """
+    def __init__(self, query_dimensions, n_dims=None, softmax_temp=1.0,
+                 orthogonal=True, kernel_fn=torch.relu, redraw=1,
+                 deterministic_eval=False):
+        super(GeneralizedRandomFeatures, self).__init__(
+            query_dimensions,
+            n_dims=2*query_dimensions if n_dims is None else 2*n_dims,
+            softmax_temp=softmax_temp,
+            orthogonal=orthogonal,
+            redraw=redraw,
+            deterministic_eval=deterministic_eval
+        )
+        self.kernel_fn = kernel_fn
+
+    def forward(self, x):
+        if self.softmax_temp != 1.0:
+            x = x * sqrt(self.softmax_temp)
+        u = x.unsqueeze(-2).matmul(self.omega).squeeze(-2)
+        return self.kernel_fn(u)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/hashing/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/hashing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2054c4995f23eabcbb4a9aad6ff4e7228a47fe5c
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/hashing/__init__.py
@@ -0,0 +1,31 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+
+import torch
+
+from .hash_cpu import compute_hashes as compute_hashes_cpu
+try:
+    from .hash_cuda import compute_hashes as compute_hashes_cuda
+except ImportError:
+    pass
+
+
+def compute_hashes(X, A, H=None):
+    device = X.device
+    if H is None:
+        H = torch.zeros(len(X), dtype=torch.int64, device=device)
+    else:
+        H.zero_()
+    if A.shape[1] != X.shape[1] + 1:
+        raise ValueError("The hash requires a bias")
+
+    if device.type == "cpu":
+        compute_hashes_cpu(X, A, H)
+    else:
+        compute_hashes_cuda(X, A, H)
+
+    return H
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/hashing/hash_cpu.cpython-39-x86_64-linux-gnu.so b/models/smi_ted/smi_ted_light/fast_transformers/hashing/hash_cpu.cpython-39-x86_64-linux-gnu.so
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/local_product/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/local_product/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..56f69fa0696492730464e19a9ebe0f7c86db2131
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/local_product/__init__.py
@@ -0,0 +1,97 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+import torch
+
+from .local_product_cpu import local_dot_product as local_dot_product_cpu, \
+    local_dot_backward as local_dot_backward_cpu, \
+    local_weighted_average as local_weighted_average_cpu, \
+    local_weighted_average_backward as local_weighted_average_backward_cpu
+
+try:
+    from .local_product_cuda import \
+        local_dot_product as local_dot_product_cuda, \
+        local_dot_backward as local_dot_backward_cuda, \
+        local_weighted_average as local_weighted_average_cuda, \
+        local_weighted_average_backward as local_weighted_average_backward_cuda
+except ImportError:
+    local_dot_product_cuda = None
+    local_dot_backward_cuda = None
+    local_weighted_average_cuda = None
+    local_weighted_average_backward_cuda = None
+
+
+class LocalDotProduct(torch.autograd.Function):
+    """Compute the dot product of the queries and keys but only consider a
+    local neighborhood of each query."""
+    dot = {
+        "cpu": local_dot_product_cpu,
+        "cuda": local_dot_product_cuda
+    }
+    dot_backward = {
+        "cpu": local_dot_backward_cpu,
+        "cuda": local_dot_backward_cuda
+    }
+
+    @staticmethod
+    def forward(ctx, queries, keys, attn_mask, key_lengths, local_context):
+        # Save the inputs for the gradient computation
+        ctx.save_for_backward(queries, keys, key_lengths)
+        ctx.local_context = local_context
+
+        return LocalDotProduct.dot[queries.device.type](
+            queries,
+            keys,
+            attn_mask,
+            key_lengths,
+            local_context
+        )
+
+    @staticmethod
+    def backward(ctx, grad_input):
+        queries, keys, key_lengths = ctx.saved_tensors
+        local_context = ctx.local_context
+
+        grads = LocalDotProduct.dot_backward[queries.device.type](
+            queries,
+            keys,
+            key_lengths,
+            grad_input,
+            local_context
+        )
+
+        # plus 3 None for masks and local_context
+        return grads + (None, None, None)
+
+
+class LocalWeightedAverage(torch.autograd.Function):
+    """Compute the weighted average of the values with the local attention."""
+    avg = {
+        "cpu": local_weighted_average_cpu,
+        "cuda": local_weighted_average_cuda
+    }
+    avg_backward = {
+        "cpu": local_weighted_average_backward_cpu,
+        "cuda": local_weighted_average_backward_cuda
+    }
+
+    @staticmethod
+    def forward(ctx, A, V):
+        # Save the inputs for the gradient computation
+        ctx.save_for_backward(A, V)
+
+        return LocalWeightedAverage.avg[A.device.type](A, V)
+
+    @staticmethod
+    def backward(ctx, grad_input):
+        A, V = ctx.saved_tensors
+        return LocalWeightedAverage.avg_backward[A.device.type](
+            A, V, grad_input
+        )
+
+
+# Alias the autograd functions to python style snake case naming
+local_dot_product = LocalDotProduct.apply
+local_weighted_average = LocalWeightedAverage.apply
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/local_product/local_product_cpu.cpython-39-x86_64-linux-gnu.so b/models/smi_ted/smi_ted_light/fast_transformers/local_product/local_product_cpu.cpython-39-x86_64-linux-gnu.so
new file mode 100644
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/masking.py b/models/smi_ted/smi_ted_light/fast_transformers/masking.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ca90f455fb273eb3f1de974fa13c8a4e1469b54
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/masking.py
@@ -0,0 +1,206 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Create types of masks to be used in various places in transformers.
+
+- Full mask (any key masked for any query)
+- Length mask (masking out everything after a length)
+- Triangular causal mask (mask any key succeeding the query)
+
+All mask implementations should provide a single interface to be used by the
+transformer layers and the attention layers.
+
+NOTE: In all cases the value 1 or True signifies what should be kept and not
+      what should be deleted/masked.
+"""
+
+import torch
+
+
+class BaseMask(object):
+    @property
+    def bool_matrix(self):
+        """Return a bool (uint8) matrix with 1s to all places that should be
+        kept."""
+        raise NotImplementedError()
+
+    @property
+    def float_matrix(self):
+        """Return the bool matrix as a float to be used as a multiplicative
+        mask for non softmax attentions."""
+        if not hasattr(self, "_float_matrix"):
+            with torch.no_grad():
+                self._float_matrix = self.bool_matrix.float()
+        return self._float_matrix
+
+    @property
+    def lengths(self):
+        """If the matrix is of the following form
+
+            1 1 1 0 0 0 0
+            1 0 0 0 0 0 0
+            1 1 0 0 0 0 0
+
+        then return it as a vector of integers
+
+            3 1 2.
+        """
+        if not hasattr(self, "_lengths"):
+            with torch.no_grad():
+                lengths = self.bool_matrix.long().sum(dim=-1)
+                # make sure that the mask starts with 1s and continues with 0s
+                # this should be changed to something more efficient, however,
+                # I chose simplicity over efficiency since the LengthMask class
+                # will be used anyway (and the result is cached)
+                m = self.bool_matrix.view(-1, self.shape[-1])
+                for i, l in enumerate(lengths.view(-1)):
+                    if not torch.all(m[i, :l]):
+                        raise ValueError("The mask is not a length mask")
+                self._lengths = lengths
+        return self._lengths
+
+    @property
+    def shape(self):
+        """Return the shape of the boolean mask."""
+        return self.bool_matrix.shape
+
+    @property
+    def additive_matrix(self):
+        """Return a float matrix to be added to an attention matrix before
+        softmax."""
+        if not hasattr(self, "_additive_matrix"):
+            with torch.no_grad():
+                self._additive_matrix = torch.log(self.bool_matrix.float())
+        return self._additive_matrix
+
+    @property
+    def additive_matrix_finite(self):
+        """Same as additive_matrix but with -1e24 instead of infinity."""
+        if not hasattr(self, "_additive_matrix_finite"):
+            with torch.no_grad():
+                self._additive_matrix_finite = (
+                    (~self.bool_matrix).float() * (-1e24)
+                )
+        return self._additive_matrix_finite
+
+    @property
+    def all_ones(self):
+        """Return true if the mask is all ones."""
+        if not hasattr(self, "_all_ones"):
+            with torch.no_grad():
+                self._all_ones = torch.all(self.bool_matrix)
+        return self._all_ones
+
+    @property
+    def lower_triangular(self):
+        """Return true if the attention is a triangular causal mask."""
+        if not hasattr(self, "_lower_triangular"):
+            self._lower_triangular = False
+            with torch.no_grad():
+                try:
+                    lengths = self.lengths
+                    if len(lengths.shape) == 1:
+                        target = torch.arange(
+                            1,
+                            len(lengths)+1,
+                            device=lengths.device
+                        )
+                        self._lower_triangular = torch.all(lengths == target)
+                except ValueError:
+                    pass
+        return self._lower_triangular
+
+
+class FullMask(BaseMask):
+    """Thin wrapper over a pytorch tensor that provides the BaseMask
+    interface.
+
+    The arguments can be given both by keyword arguments and positional
+    arguments. To imitate function overloading, the constructor checks the type
+    of the first argument and if it is a tensor it treats it as the mask.
+    otherwise it assumes that it was the N argument.
+
+    Arguments
+    ---------
+        mask: The mask as a PyTorch tensor.
+        N: The rows of the all True mask to be created if the mask argument is
+           not provided.
+        M: The columns of the all True mask to be created if the mask argument
+           is not provided. If N is given M defaults to N.
+        device: The device to create the mask in (defaults to cpu)
+    """
+    def __init__(self, mask=None, N=None, M=None, device="cpu"):
+        # mask is a tensor so we ignore N and M
+        if mask is not None and isinstance(mask, torch.Tensor):
+            if mask.dtype != torch.bool:
+                raise ValueError("FullMask expects the mask to be bool")
+            with torch.no_grad():
+                self._mask = mask.clone()
+            return
+
+        # mask is an integer, N is an integer and M is None so assume they were
+        # passed as N, M
+        if mask is not None and M is None and isinstance(mask, int):
+            M = N
+            N = mask
+
+        if N is not None:
+            M = M or N
+            with torch.no_grad():
+                self._mask = torch.ones(N, M, dtype=torch.bool, device=device)
+            self._all_ones = True
+            return
+
+        raise ValueError("Either mask or N should be provided")
+
+    @property
+    def bool_matrix(self):
+        return self._mask
+
+
+class LengthMask(BaseMask):
+    """Provide a BaseMask interface for lengths. Mostly to be used with
+    sequences of different lengths.
+
+    Arguments
+    ---------
+        lengths: The lengths as a PyTorch long tensor
+        max_len: The maximum length for the mask (defaults to lengths.max())
+        device: The device to be used for creating the masks (defaults to
+                lengths.device)
+    """
+    def __init__(self, lengths, max_len=None, device=None):
+        self._device = device or lengths.device
+        with torch.no_grad():
+            self._lengths = lengths.clone().to(self._device)
+        self._max_len = max_len or self._lengths.max()
+
+        self._bool_matrix = None
+        self._all_ones = torch.all(self._lengths == self._max_len).item()
+
+    @property
+    def bool_matrix(self):
+        if self._bool_matrix is None:
+            with torch.no_grad():
+                indices = torch.arange(self._max_len, device=self._device)
+                self._bool_matrix = (
+                    indices.view(1, -1) < self._lengths.view(-1, 1)
+                )
+        return self._bool_matrix
+
+
+class TriangularCausalMask(LengthMask):
+    """A square matrix with everything masked out above the diagonal.
+
+    Arguments
+    ---------
+        N: The size of the matrix
+        device: The device to create the mask in (defaults to cpu)
+    """
+    def __init__(self, N, device="cpu"):
+        lengths = torch.arange(1, N+1, device=device)
+        super(TriangularCausalMask, self).__init__(lengths, N, device)
+        self._lower_triangular = True
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/.DS_Store b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/.DS_Store
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new file mode 100644
index 0000000000000000000000000000000000000000..f507c11696f7f9fc77da87efbf171db22514304e
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/__init__.py
@@ -0,0 +1,7 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implementations of transformers as recurrent functions."""
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/__pycache__/__init__.cpython-310.pyc b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/__pycache__/__init__.cpython-310.pyc
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/_utils.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b88f9836d9c43acea90c2eb7b109e553ce768b43
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/_utils.py
@@ -0,0 +1,16 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+import warnings
+
+
+def check_state(state=None, memory=None):
+    if memory is not None:
+        warnings.warn(("'memory' is deprecated for recurrent transformers "
+                       " and will be removed in the future, use 'state' "
+                       "instead"), DeprecationWarning)
+    if state is None:
+        state = memory
+    return state
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/.DS_Store b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/.DS_Store
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e892c7e543f9421d361b724858a1e6e740d3ace6
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/__init__.py
@@ -0,0 +1,16 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implementations of different types of autoregressive attention
+mechanisms for self attention and cross attention."""
+
+from .self_attention.attention_layer import RecurrentAttentionLayer
+from .self_attention.full_attention import RecurrentFullAttention
+from .self_attention.linear_attention import RecurrentLinearAttention
+
+from .cross_attention.attention_layer import RecurrentCrossAttentionLayer
+from .cross_attention.full_attention import RecurrentCrossFullAttention
+from .cross_attention.linear_attention import RecurrentCrossLinearAttention
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/__pycache__/__init__.cpython-310.pyc b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/__pycache__/__init__.cpython-310.pyc
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee0933dec9c90d7613757dc083644f1817c593de
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/__init__.py
@@ -0,0 +1,30 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Autoregressive implementations for cross attention as a recurrent module.
+
+The attention implementations in this module expect one input for query and a
+sequence of inputs for keys and values. The sequence for the keys and values is
+fixed for all queries.
+
+Example
+--------
+
+    import torch
+
+    from fast_transformers.recurrent.attention import \
+        RecurrentCrossAttentionLayer, RecurrentCrossFullAttention
+
+    att = RecurrentCrossAttentionLayer(RecurrentCrossFullAttention(), 16, 4)
+    state = None
+    x = torch.rand(8, 16)
+    memory = torch.rand(8, 64, 16)
+    for i in range(10):
+        x, state = att(x, memory, memory, state=state)
+"""
+
+from .attention_layer import RecurrentCrossAttentionLayer
+from .full_attention import RecurrentCrossFullAttention
+from .linear_attention import RecurrentCrossLinearAttention
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/__pycache__/__init__.cpython-310.pyc b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/__pycache__/__init__.cpython-310.pyc
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/attention_layer.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/attention_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6718a2455d9640be35da6c9ce0872804a09c9a3b
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/attention_layer.py
@@ -0,0 +1,105 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Similar to the corresponding module in fast_transformers.attention, this
+module performs all the query, key, value projections and output projections
+leaving the implementation of the attention to the inner attention module.
+
+The crucial difference with respect to the self attention recurrent module
+(fast_transformers.recurrent.attention.RecurrentAttentionLayer) is that it
+doesn't recompute the projections for the keys and values if the state is not
+None.
+"""
+
+from torch.nn import Linear, Module
+
+from ....events import EventDispatcher
+
+
+class RecurrentCrossAttentionLayer(Module):
+    """See fast_transformers.attention.attention_layer.AttentionLayer .
+
+    The differences with the aforementioned module as well as the
+    RecurrentAttentionLayer are that this module projects the query every time
+    and the keys and values only the first time they are provided.
+
+    Arguments
+    ---------
+        attention: Specific inner attention implementation that just computes a
+                   weighted average of values given a similarity of queries and
+                   keys.
+        d_model: The input feature dimensionality
+        n_heads: The number of heads for the multi head attention
+        d_keys: The dimensionality of the keys/queries
+                (default: d_model/n_heads)
+        d_values: The dimensionality of the values (default: d_model/n_heads)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, attention, d_model, n_heads, d_keys=None,
+                 d_values=None, event_dispatcher=""):
+        super(RecurrentCrossAttentionLayer, self).__init__()
+
+        # Fill d_keys and d_values
+        d_keys = d_keys or (d_model//n_heads)
+        d_values = d_values or (d_model//n_heads)
+
+        self.inner_attention = attention
+        self.query_projection = Linear(d_model, d_keys * n_heads)
+        self.key_projection = Linear(d_model, d_keys * n_heads)
+        self.value_projection = Linear(d_model, d_values * n_heads)
+        self.out_projection = Linear(d_values * n_heads, d_model)
+        self.n_heads = n_heads
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, query, keys, values, key_lengths, state=None):
+        """Attend to the keys and values based on the passed in query.
+
+        In the argument description we make use of the following sizes
+
+            - N: the batch size
+            - S: the sequence length of the keys and values
+            - D: The input feature dimensionality passed in the constructor as
+              'd_model'
+
+        Argument
+        --------
+            query: (N, D) The tensor containing the queries
+            keys: (N, S, D) The tensor containing the keys
+            values: (N, S, D) The tensor containing the values
+            key_lengths: A fast_transformers.masking.BaseMask implementation
+                         that defines the length of each key/value sequence
+            state: The state varies depending on the inner attention
+                   implementation, but if it is not None then the keys and
+                   values are ignored
+        """
+        #Extract some shapes
+        N, _ = query.shape
+        H = self.n_heads
+
+        # Project the query
+        query = self.query_projection(query).view(N, H, -1)
+
+        # Project the keys and values if there is no state
+        if state is None:
+            _, S, _ = keys.shape
+            keys = self.key_projection(keys).view(N, S, H, -1)
+            values = self.value_projection(values).view(N, S, H, -1)
+        else:
+            keys = None
+            values = None
+
+        new_value, state = self.inner_attention(
+            query,
+            keys,
+            values,
+            key_lengths,
+            state=state
+        )
+        new_value = new_value.view(N, -1)
+
+        # Project the output and return
+        return self.out_projection(new_value), state
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/full_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/full_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..b446d917b74baf1eea56a641d062adee844ba3c3
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/full_attention.py
@@ -0,0 +1,75 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Implement the typical softmax attention as a recurrent cross attention
+module to speed up autoregressive decoding."""
+
+from math import sqrt
+
+import torch
+from torch.nn import Dropout, Module
+
+from ....attention_registry import RecurrentCrossAttentionRegistry, Optional, \
+    Float, EventDispatcherInstance
+from ....events import EventDispatcher, AttentionEvent
+
+
+class RecurrentCrossFullAttention(Module):
+    """Implement autoregressive softmax cross attention as a recurrent
+    module.
+
+    Arguments
+    ---------
+        softmax_temp: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.1)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+
+    def __init__(self, softmax_temp=None, attention_dropout=0.1,
+                 event_dispatcher=""):
+        super(RecurrentCrossFullAttention, self).__init__()
+        self.softmax_temp = softmax_temp
+        self.dropout = Dropout(attention_dropout)
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, query, keys, values, key_lengths, state=None):
+        # Extract some shapes and compute the temperature
+        N, H, E = query.shape
+        softmax_temp = self.softmax_temp or 1. / sqrt(E)
+
+        # Extract the keys and values either from the arguments or the state
+        if state is not None:
+            keys, values = state
+
+        # Compute the unnormalized attention and apply the key length mask
+        QK = torch.einsum("nhe,nshe->nsh", query, keys)
+        QK = QK + key_lengths.additive_matrix[:, :, None]
+
+        # Compute the attention and the weighted average
+        A = self.dropout(torch.softmax(softmax_temp * QK, dim=1))
+        V = torch.einsum("nsh,nshd->nhd", A, values)
+
+        # Let the world know of the attention matrix
+        self.event_dispatcher.dispatch(AttentionEvent(self, A))
+
+        # Make sure that we return a contiguous value
+        return V.contiguous(), [keys, values]
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+RecurrentCrossAttentionRegistry.register(
+    "full", RecurrentCrossFullAttention,
+    [
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/linear_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/linear_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca7cf99be3d5116a79cab35a56c8b64a3cbfbda7
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/cross_attention/linear_attention.py
@@ -0,0 +1,79 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Implement unmasked linear attention as a recurrent cross attention module to
+speed up autoregressive decoding."""
+
+import torch
+from torch.nn import Module
+
+from ....attention_registry import RecurrentCrossAttentionRegistry, Optional, Int, \
+    Callable, EventDispatcherInstance
+from ....events import EventDispatcher
+from ....feature_maps import elu_feature_map
+
+
+class RecurrentCrossLinearAttention(Module):
+    """Implement autoregressive linear cross attention as a recurrent
+    module.
+
+    See fast_transformers.attention.linear_attention.LinearAttention .
+
+    Arguments
+    ---------
+        feature_map: callable, a callable that applies the feature map to the
+                     last dimension of a tensor (default: elu(x)+1)
+        eps: float, a small number to ensure the numerical stability of the
+             denominator (default: 1e-6)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, query_dimensions, feature_map=None, eps=1e-6,
+                 event_dispatcher=""):
+        super(RecurrentCrossLinearAttention, self).__init__()
+        self.feature_map = (
+            feature_map(query_dimensions) if feature_map else
+            elu_feature_map(query_dimensions)
+        )
+        self.eps = eps
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, query, keys, values, key_lengths, state=None):
+        # If this is a new sequence re initialize the feature map
+        if state is None:
+            self.feature_map.new_feature_map(query.device)
+
+        # Compute the feature representation of the query
+        Q = self.feature_map.forward_queries(query)
+
+        # If the state is not given compute the key-value matrix and the
+        # normalizers, namely compute whatever is needed in order to attend to
+        # keys and values with a given query.
+        if state is None:
+            K = self.feature_map.forward_keys(keys)
+            K = K * key_lengths.float_matrix[:, :, None, None]
+            S = torch.einsum("nshd,nshm->nhmd", K, values)
+            Z = K.sum(dim=1)
+        else:
+            S, Z = state
+
+        # Given S and Z now we can efficiently compute the new value
+        QZ = 1/(torch.einsum("nhd,nhd->nh", Q, Z)+self.eps)
+        V = torch.einsum("nhd,nhmd,nh->nhm", Q, S, QZ)
+
+        return V.contiguous(), [S, Z]
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+RecurrentCrossAttentionRegistry.register(
+    "linear", RecurrentCrossLinearAttention,
+    [
+        ("query_dimensions", Int),
+        ("feature_map", Optional(Callable)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0251189c4d752397e0354a692790a58b05e5aed
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/__init__.py
@@ -0,0 +1,30 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Autoregressive implementations for self attention as a recurrent module.
+
+The attention implementations in this module expect one input for query, one
+for key and one for value and attend to all the keys and values seen so far. No
+masking is necessary as an implicit lower triangular attention mask is assumed
+in all cases.
+
+Example
+-------
+
+    import torch
+
+    from fast_transformers.recurrent.attention import \
+        RecurrentAttentionLayer, RecurrentFullAttention
+
+    att = RecurrentAttentionLayer(RecurrentFullAttention(), 16, 4)
+    state = None
+    x = torch.rand(8, 16)
+    for i in range(10):
+        x, state = att(x, x, x, state=state)
+"""
+
+from .attention_layer import RecurrentAttentionLayer
+from .full_attention import RecurrentFullAttention
+from .linear_attention import RecurrentLinearAttention
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/attention_layer.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/attention_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..671db6400d6cb4450af083843329792583bf9509
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/attention_layer.py
@@ -0,0 +1,96 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Similar to the corresponding module in fast_transformers.attention, this
+module performs all the query, key, value projections and output projections
+leaving the implementation of the attention to the inner attention module."""
+
+from torch.nn import Linear, Module
+
+from ....events import EventDispatcher
+from ..._utils import check_state
+
+
+class RecurrentAttentionLayer(Module):
+    """See fast_transformers.attention.attention_layer.AttentionLayer.
+
+    The only difference with the corresponding module is that this projects
+    only one input and then calls the inner attention with the provided
+    previous state.
+
+    Arguments
+    ---------
+        attention: Specific inner attention implementation that just computes a
+                   weighted average of values given a similarity of queries and
+                   keys.
+        d_model: The input feature dimensionality
+        n_heads: The number of heads for the multi head attention
+        d_keys: The dimensionality of the keys/queries
+                (default: d_model/n_heads)
+        d_values: The dimensionality of the values (default: d_model/n_heads)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, attention, d_model, n_heads, d_keys=None,
+                 d_values=None, event_dispatcher=""):
+        super(RecurrentAttentionLayer, self).__init__()
+
+        # Fill d_keys and d_values
+        d_keys = d_keys or (d_model//n_heads)
+        d_values = d_values or (d_model//n_heads)
+
+        self.inner_attention = attention
+        self.query_projection = Linear(d_model, d_keys * n_heads)
+        self.key_projection = Linear(d_model, d_keys * n_heads)
+        self.value_projection = Linear(d_model, d_values * n_heads)
+        self.out_projection = Linear(d_values * n_heads, d_model)
+        self.n_heads = n_heads
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, query, key, value, state=None, memory=None):
+        """Apply attention to the passed in query/key/value after projecting
+        them to multiple heads.
+
+        In the argument description we make use of the following sizes
+
+            - N: the batch size
+            - D: The input feature dimensionality passed in the constructor as
+              'd_model'
+
+        Arguments
+        ---------
+            query: (N, D) The tensor containing the queries
+            key: (N, D) The tensor containing the keys
+            value: (N, D) The tensor containing the values
+            state: The state varies depending on the inner attention implementation
+            memory: **Deprecated** and replaced by state
+
+        Returns
+        -------
+            The new value for each query as a tensor of shape (N, D).
+        """
+        # Normalize the state/memory
+        state = check_state(state, memory)
+
+        # Project the queries/keys/values
+        query = self.query_projection(query)
+        key = self.key_projection(key)
+        value = self.value_projection(value)
+
+        # Reshape them into many heads and compute the attention
+        N, D = query.shape
+        H = self.n_heads
+        new_value, state = self.inner_attention(
+            query.view(N, H, -1),
+            key.view(N, H, -1),
+            value.view(N, H, -1),
+            state
+        )
+        new_value = new_value.view(N, -1)
+
+        # Project the output and return
+        return self.out_projection(new_value), state
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/full_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/full_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..c65598bdb6faeceac9e80efba3100cc56f0832f0
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/full_attention.py
@@ -0,0 +1,83 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement the typical softmax attention as a recurrent module to speed up
+autoregressive inference. See fast_transformers.attention.full_attention ."""
+
+from math import sqrt
+
+import torch
+from torch.nn import Dropout, Module
+
+from ....attention_registry import RecurrentAttentionRegistry, Optional, \
+    Float, EventDispatcherInstance
+from ....events import EventDispatcher, AttentionEvent
+from ..._utils import check_state
+
+
+class RecurrentFullAttention(Module):
+    """Implement the full softmax attention as a recurrent module.
+
+    Arguments
+    ---------
+        softmax_temp: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.1)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, softmax_temp=None, attention_dropout=0.1,
+                 event_dispatcher=""):
+        super(RecurrentFullAttention, self).__init__()
+        self.softmax_temp = softmax_temp
+        self.dropout = Dropout(attention_dropout)
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, query, key, value, state=None, memory=None):
+        # Normalize state/memory
+        state = check_state(state, memory)
+
+        # Extract some shapes and compute the temperature
+        N, H, E = query.shape
+        _, _, D = value.shape
+        softmax_temp = self.softmax_temp or 1./sqrt(E)
+
+        # Aggregate the list of keys and values
+        if state is not None:
+            keys, values = state
+            keys = torch.cat([keys, key[:, :, None]], dim=2)
+            values = torch.cat([values, value[:, :, None]], dim=2)
+        else:
+            keys = key[:, :, None]
+            values = value[:, :, None]
+
+        # Compute the unnormalized attention
+        QK = torch.einsum("nhe,nhse->nhs", query, keys)
+
+        # Compute the attention and the weighted average
+        A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1))
+        V = torch.einsum("nhs,nhsd->nhd", A, values).contiguous()
+
+        # Let the world know of the attention matrix
+        self.event_dispatcher.dispatch(AttentionEvent(self, A))
+
+        # Make sure that what we return is contiguous
+        return V, [keys, values]
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+RecurrentAttentionRegistry.register(
+    "full", RecurrentFullAttention,
+    [
+        ("softmax_temp", Optional(Float)),
+        ("attention_dropout", Optional(Float, 0.1)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/linear_attention.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/linear_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..b429cc555d63c0c5b3bd139920b66037b40f2727
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/attention/self_attention/linear_attention.py
@@ -0,0 +1,110 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement the causally masked linear attention as a recurrent model."""
+
+import torch
+from torch.nn import Module
+
+from ....attention_registry import RecurrentAttentionRegistry, Optional, Int, \
+    Callable, EventDispatcherInstance
+from ....events import EventDispatcher
+from ....feature_maps import elu_feature_map
+from ..._utils import check_state
+
+
+class RecurrentLinearAttention(Module):
+    """Implement fast_transformers.attention.causal_linear_attention as a
+    fixed-dimensional state recurrent model.
+
+    See fast_transformers.attention.linear_attention and
+    fast_transformers.attention.causal_linear_attention for the general concept
+    of replacing the softmax with feature maps.
+
+    Arguments
+    ---------
+        feature_map: callable, a callable that applies the feature map to the
+                     last dimension of a tensor (default: elu(x)+1)
+        eps: float, a small number to ensure the numerical stability of the
+             denominator (default: 1e-6)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, query_dimensions, feature_map=None, eps=1e-6,
+                 event_dispatcher=""):
+        super(RecurrentLinearAttention, self).__init__()
+        self.feature_map = (
+            feature_map(query_dimensions) if feature_map else
+            elu_feature_map(query_dimensions)
+        )
+        self.eps = eps
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, query, key, value, state=None, memory=None):
+        # Normalize state/memory
+        state = check_state(state, memory)
+
+        # If this is a new sequence reinitialize the feature map
+        if state is None:
+            self.feature_map.new_feature_map(query.device)
+
+        # Apply the feature map to the query and key
+        Q = self.feature_map.forward_queries(query)
+        K = self.feature_map.forward_keys(key)
+
+        # Extract some shapes
+        N, H, D = Q.shape
+        _, _, M = value.shape
+
+        # Extract the memory or initialize it
+        if state is None:
+            Si = query.new_zeros((N, H, D, M))
+            Zi = query.new_zeros((N, H, D))
+        else:
+            Si, Zi = state
+
+        # Ensure the batch size did not change
+        if len(Si) != N:
+            raise ValueError("The batch size changed during iteration")
+
+        # Update the internal state
+        #
+        # NOTE: The if clause is added due to GitHub PR #10. Simply using the
+        # following two lines does not perform the operation in place which
+        # means it is slower for inference.
+        if K.grad_fn is not None or value.grad_fn is not None:
+            Zi = Zi + K
+            Si = Si + torch.einsum("nhd,nhm->nhdm", K, value)
+        else:
+            Zi += K
+            Si += torch.einsum("nhd,nhm->nhdm", K, value)
+
+        # Compute the output
+        Z = 1. / (torch.einsum("nhd,nhd->nh", Q, Zi) + self.eps)
+        V = torch.einsum("nhd,nhdm,nh->nhm", Q, Si, Z)
+
+        return V, [Si, Zi]
+
+
+# Register the attention implementation so that it becomes available in our
+# builders
+RecurrentAttentionRegistry.register(
+    "linear", RecurrentLinearAttention,
+    [
+        ("query_dimensions", Int),
+        ("feature_map", Optional(Callable)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
+RecurrentAttentionRegistry.register(
+    "causal-linear", RecurrentLinearAttention,
+    [
+        ("query_dimensions", Int),
+        ("feature_map", Optional(Callable)),
+        ("event_dispatcher", Optional(EventDispatcherInstance, ""))
+    ]
+)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/recurrent/transformers.py b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/transformers.py
new file mode 100644
index 0000000000000000000000000000000000000000..1322f14e06553e0860472f4eaf0d0352e9b6537b
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/recurrent/transformers.py
@@ -0,0 +1,279 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement transformer encoders and decoders as RNNs that will be used with
+different recurrent attention mechanisms.
+
+In all cases there exists no sequence dimension and the shapes are batch x
+heads x dims.
+
+This module's interface is designed with the linear attention in mind. The
+interface is subject to change given the implementation of other recurrent
+attentions.
+"""
+
+import warnings
+
+import torch
+from torch.nn import Dropout, LayerNorm, Linear, Module, ModuleList
+import torch.nn.functional as F
+
+from ..events import EventDispatcher
+from ..masking import LengthMask
+from ._utils import check_state
+
+
+class RecurrentTransformerEncoderLayer(Module):
+    """Attention to the previous inputs and feed forward with skip connections.
+
+    This transformer encoder layer is the recurrent dual of
+    fast_transformers.transformers.TransformerEncoderLayer . The results should
+    be identical given the same inputs and a lower triangular mask.
+
+    Arguments
+    ---------
+        attention: The attention implementation to use given as a nn.Module
+        d_model: The input feature dimensionality
+        d_ff: The dimensionality of the intermediate features after the
+              attention (default: d_model*4)
+        dropout: The dropout rate to apply to the intermediate features
+                 (default: 0.1)
+        activation: {'relu', 'gelu'} Which activation to use for the feed
+                    forward part of the layer (default: relu)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, attention, d_model, d_ff=None, dropout=0.1,
+                 activation="relu", event_dispatcher=""):
+        super(RecurrentTransformerEncoderLayer, self).__init__()
+        d_ff = d_ff or 4*d_model
+        self.attention = attention
+        self.linear1 = Linear(d_model, d_ff)
+        self.linear2 = Linear(d_ff, d_model)
+        self.norm1 = LayerNorm(d_model)
+        self.norm2 = LayerNorm(d_model)
+        self.dropout = Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, x, state=None, memory=None):
+        """Apply the transformer encoder to the input x using the provided
+        memory.
+
+        Arguments
+        ---------
+            x: The input features of shape (N, E) where N is the batch size and
+               E is d_model passed in the constructor
+            state: The state can vary depending on the attention implementation
+            memory: **Deprecated** name for the state argument
+        """
+        # Normalize the state name
+        state = check_state(state, memory)
+
+        # Run the self attention and add it to the input
+        x2, state = self.attention(x, x, x, state)
+        x = x + self.dropout(x2)
+
+        # Run the fully connected part of the layer
+        y = x = self.norm1(x)
+        y = self.dropout(self.activation(self.linear1(y)))
+        y = self.dropout(self.linear2(y))
+
+        return self.norm2(x+y), state
+
+
+class RecurrentTransformerEncoder(Module):
+    """RecurrentTransformerEncoder is a sequence of
+    RecurrentTransformerEncoderLayer instances.
+
+    RecurrentTransformerEncoder keeps a separate state per
+    RecurrentTransformerEncoderLayer.
+
+    Arguments
+    ---------
+        layers: list, RecurrentTransformerEncoderLayer instances or instances
+                that implement the same interface
+        norm_layer: A normalization layer to be applied to the final output
+                    (default: None which means no normalization)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, layers, norm_layer=None, event_dispatcher=""):
+        super(RecurrentTransformerEncoder, self).__init__()
+        self.layers = ModuleList(layers)
+        self.norm = norm_layer
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, x, state=None, memory=None):
+        """Apply all recurrent transformer layers to the input x using the
+        provided state.
+
+        Arguments
+        ---------
+            x: The input features of shape (N, E) where N is the batch size and
+               E is d_model passed in the constructor of each recurrent
+               transformer encoder layer
+            state: A list of objects to be passed to each recurrent
+                   transformer encoder layer
+            memory: **Deprecated** name for the state argument
+        """
+        # Initialize the memory to None if not given
+        state = check_state(state, memory)
+        if state is None:
+            state = [None]*len(self.layers)
+
+        # Apply all the transformers
+        for i, layer in enumerate(self.layers):
+            x, s = layer(x, state[i])
+            state[i] = s
+
+        # Apply the normalization if needed
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, state
+
+
+class RecurrentTransformerDecoderLayer(Module):
+    """Attention to the previous inputs and a preprocessed memory.
+
+    This transformer decoder layer is the recurrent dual of
+    fast_transformers.transformers.TransformerDecoderLayer . The results should
+    be identical given the same inputs and a lower triangular mask for x_mask.
+
+    Arguments
+    ---------
+        self_attention: The attention implementation to use for self attention
+                        given as a nn.Module
+        cross_attention: The attention implementation to use for cross
+                         attention given as a nn.Module
+        d_model: The input feature dimensionality
+        d_ff: The dimensionality of the intermediate features after the
+              attention (default: d_model*4)
+        dropout: The dropout rate to apply to the intermediate features
+                 (default: 0.1)
+        activation: {'relu', 'gelu'} Which activation to use for the feed
+                    forward part of the layer (default: relu)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
+                 dropout=0.1, activation="relu", event_dispatcher=""):
+        super(RecurrentTransformerDecoderLayer, self).__init__()
+        d_ff = d_ff or 4*d_model
+        self.self_attention = self_attention
+        self.cross_attention = cross_attention
+        self.linear1 = Linear(d_model, d_ff)
+        self.linear2 = Linear(d_ff, d_model)
+        self.norm1 = LayerNorm(d_model)
+        self.norm2 = LayerNorm(d_model)
+        self.norm3 = LayerNorm(d_model)
+        self.dropout = Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, x, memory, memory_length_mask=None, state=None):
+        """Apply the transformer decoder to the input x and also attend to
+        memory.
+
+        Note the memory mask is assumed to be a full mask.
+
+        Arguments
+        ---------
+            x: The input features of shape (N, E) where N is the batch size and
+               E is d_model passed in the constructor
+            memory: A sequence of features (N, S, E) that the input will attend
+                    to. S is the sequence length and E is the same as for x.
+            memory_length_mask: An implementation of a BaseMask that encodes
+                                how many elements each memory sequence in the
+                                batch consists of.
+            state: The state varies depending on the attention implementations
+                   but it allows for recurrent implementation.
+        """
+        # Normalize the mask
+        N = x.shape[0]
+        L = memory.shape[1]
+        memory_length_mask = memory_length_mask or \
+            LengthMask(x.new_full((N,), L, dtype=torch.int64))
+
+        # Extract the individual states for the self attention and the cross
+        # attention
+        self_state, cross_state = state or [None, None]
+
+        # First apply the self attention and add it to the input
+        x2, self_state = self.self_attention(x, x, x, state=self_state)
+        x = self.norm1(x + self.dropout(x2))
+
+        # Secondly apply the cross attention and add it to the previous output
+        x2, cross_state = self.cross_attention(
+            x, memory, memory, memory_length_mask, state=cross_state
+        )
+        x = self.norm2(x + self.dropout(x2))
+
+        # Finally run the fully connected part of the layer
+        y = x
+        y = self.dropout(self.activation(self.linear1(y)))
+        y = self.dropout(self.linear2(y))
+
+        return self.norm3(x+y), [self_state, cross_state]
+
+
+class RecurrentTransformerDecoder(Module):
+    """RecurrentTransformerDecoder is little more than a sequence of
+    RecurrentTransformerDecoderLayer instances.
+
+    Simlar to the recurrent encoder a separate state is kept per decoder layer.
+
+    Arguments
+    ---------
+        layers: list, RecurrentTransformerDecoderLayer instances or instances
+                that implement the same interface
+        norm_layer: A normalization layer to be applied to the final output
+                    (default: None which means no normalization)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, layers, norm_layer=None, event_dispatcher=""):
+        super(RecurrentTransformerDecoder, self).__init__()
+        self.layers = ModuleList(layers)
+        self.norm = norm_layer
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, x, memory, memory_length_mask=None, state=None):
+        """Apply all recurrent transformer layers to the input x using the
+        provided state.
+
+        Arguments
+        ---------
+            x: The input features of shape (N, E) where N is the batch size and
+               E is d_model passed in the constructor
+            memory: A sequence of features (N, S, E) that the input will attend
+                    to. S is the sequence length and E is the same as for x.
+            memory_length_mask: An implementation of a BaseMask that encodes
+                                how many elements each memory sequence in the
+                                batch consists of
+            state: A list of objects to be passed to each recurrent
+                   transformer decoder layer
+        """
+        # Initialize the state to None if not given
+        if state is None:
+            state = [None]*len(self.layers)
+
+        # Apply all the transformers
+        for i, layer in enumerate(self.layers):
+            x, s = layer(x, memory, memory_length_mask=memory_length_mask,
+                         state=state[i])
+            state[i] = s
+
+        # Apply the normalization if needed
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, state
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/sparse_product/__init__.py b/models/smi_ted/smi_ted_light/fast_transformers/sparse_product/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8964e3342a36b3ef87f9d20d4939c8a7ad2538d
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/sparse_product/__init__.py
@@ -0,0 +1,399 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+
+import torch
+
+from .sparse_product_cpu import \
+    sparse_dot_product as sparse_dot_product_cpu, \
+    sparse_dot_backward as sparse_dot_backward_cpu, \
+    sparse_weighted_average as sparse_weighted_average_cpu, \
+    sparse_weighted_average_backward as sparse_weighted_average_backward_cpu
+try:
+    from .sparse_product_cuda import \
+        sparse_dot_product as sparse_dot_product_cuda, \
+        sparse_dot_backward as sparse_dot_backward_cuda, \
+        sparse_weighted_average as sparse_weighted_average_cuda, \
+        sparse_weighted_average_backward as \
+            sparse_weighted_average_backward_cuda
+except ImportError:
+    sparse_dot_product_cuda = None
+    sparse_dot_backward_cuda = None
+    sparse_weighted_average_cuda = None
+    sparse_weighted_average_backward_cuda = None
+
+from .clustered_sparse_product_cpu import \
+    clustered_sparse_dot_product as clustered_sparse_dot_product_cpu, \
+    clustered_sparse_dot_backward as clustered_sparse_dot_backward_cpu, \
+    clustered_sparse_weighted_average as \
+        clustered_sparse_weighted_average_cpu, \
+    clustered_sparse_weighted_average_backward as \
+        clustered_sparse_weighted_average_backward_cpu
+
+try:
+    from .clustered_sparse_product_cuda import \
+        clustered_sparse_dot_product as clustered_sparse_dot_product_cuda, \
+        clustered_sparse_dot_backward as clustered_sparse_dot_backward_cuda, \
+        clustered_sparse_weighted_average as \
+            clustered_sparse_weighted_average_cuda, \
+        clustered_sparse_weighted_average_backward as \
+            clustered_sparse_weighted_average_backward_cuda
+except ImportError:
+    clustered_sparse_dot_product_cuda = None
+    clustered_sparse_dot_backward_cuda = None
+    clustered_sparse_weighted_average_cuda = None
+    clustered_sparse_weighted_average_backward_cuda = None
+
+
+class SparseDotProduct(torch.autograd.Function):
+    """Compute the dot products only at the positions specified by topk."""
+    dot = {
+        "cpu": sparse_dot_product_cpu,
+        "cuda": sparse_dot_product_cuda
+    }
+    dot_backward = {
+        "cpu": sparse_dot_backward_cpu,
+        "cuda": sparse_dot_backward_cuda
+    }
+
+    @staticmethod
+    def forward(ctx, Q, K, topk):
+        # Save the inputs to compute the gradient
+        ctx.save_for_backward(Q, K, topk)
+
+        # Create the output tensor
+        device = Q.device
+        N, H, L, E = Q.shape
+        _, _, _, k = topk.shape
+        product = torch.empty((N, H, L, k), device=device)
+
+        # Actually perform the dot product
+        SparseDotProduct.dot[device.type](Q, K, topk, product)
+
+        return product
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        # Extract the saved tensors and allocate memory for the gradients
+        Q, K, topk = ctx.saved_tensors
+        grad_Q = torch.zeros_like(Q)
+        grad_K = torch.zeros_like(K)
+
+        SparseDotProduct.dot_backward[Q.device.type](
+            Q,
+            K,
+            topk,
+            grad_output,
+            grad_Q,
+            grad_K
+        )
+
+        return grad_Q, grad_K, None
+
+
+class SparseWeightedAverage(torch.autograd.Function):
+    """Compute the weighted average only for the topk values."""
+    avg = {
+        "cpu": sparse_weighted_average_cpu,
+        "cuda": sparse_weighted_average_cuda
+    }
+    avg_backward = {
+        "cpu": sparse_weighted_average_backward_cpu,
+        "cuda": sparse_weighted_average_backward_cuda
+    }
+
+    @staticmethod
+    def forward(ctx, weights, values, topk):
+        # Save the tensors to compute the gradient
+        ctx.save_for_backward(weights, values, topk)
+
+        # Allocate the output tensor
+        N, H, L, _ = weights.shape
+        _, _, _, E = values.shape
+        output = values.new_zeros(N, H, L, E)
+
+        # Compute the average
+        SparseWeightedAverage.avg[weights.device.type](
+            weights,
+            values,
+            topk,
+            output
+        )
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        # Extract the saved tensors and allocate memory for the gradients
+        weights, values, topk = ctx.saved_tensors
+        grad_weights = torch.zeros_like(weights)
+        grad_values = torch.zeros_like(values)
+
+        if grad_output.stride()[-1] != 1:
+            grad_output = grad_output.contiguous()
+
+        SparseWeightedAverage.avg_backward[weights.device.type](
+            weights,
+            values,
+            topk,
+            grad_output,
+            grad_weights,
+            grad_values
+        )
+
+        return grad_weights, grad_values, None
+
+
+class ClusteredSparseDotProduct(torch.autograd.Function):
+    """Compute the dot products only at the positions specified by topk."""
+    dot = {
+        "cpu": clustered_sparse_dot_product_cpu,
+        "cuda": clustered_sparse_dot_product_cuda
+    }
+    dot_backward = {
+        "cpu": clustered_sparse_dot_backward_cpu,
+        "cuda": clustered_sparse_dot_backward_cuda
+    }
+
+    @staticmethod
+    def forward(ctx, Q, K, topk, groups, counts, lengths):
+        # Save the inputs to compute the gradient
+        ctx.save_for_backward(Q, K, topk, groups, counts)
+
+        device = Q.device
+        N, H, L, E = Q.shape
+        _, _, C, k = topk.shape
+
+        # Create the output tensor
+        product = torch.zeros((N, H, L, k), device=device)
+
+        # Unfortunately the cpu and gpu interfaces are different so
+        # the entire call is surrounded by if-else block
+        if device.type == "cpu":
+            ClusteredSparseDotProduct.dot[device.type](
+                Q,
+                K,
+                groups,
+                topk,
+                product
+            )
+
+        else:
+            # Allocate bookkeeping parameters to facilitate the kernel
+            with torch.no_grad():
+                Q_pb = 16
+                block_counts = (counts + Q_pb - 1) // Q_pb
+                block_counts = block_counts.int()
+                block_counts_cumsum = block_counts.view(-1).cumsum(-1).view(N, H, C).int()
+                indx_maps = torch.ones(
+                    (block_counts.sum(), 4),
+                    device=Q.device,
+                    dtype=torch.int32
+                )
+                counts_cumsum = counts.cumsum(-1).int()
+                total_blocks = block_counts.sum().item()
+
+            # Actually perform the dot product
+            ClusteredSparseDotProduct.dot[device.type](
+                Q,
+                K,
+                topk.int(),
+                counts_cumsum - counts,
+                counts_cumsum,
+                block_counts,
+                block_counts_cumsum,
+                total_blocks,
+                indx_maps,
+                product
+            )
+
+        return product
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        Q, K, topk, groups, counts = ctx.saved_tensors
+        device = Q.device
+        # Extract the saved tensors and allocate memory for the gradients
+        grad_Q = torch.zeros_like(Q)
+        grad_K = torch.zeros_like(K)
+
+        # Unfortunately the cpu and gpu interfaces are different so
+        # the entire call is surrounded by if-else block
+        if device.type == "cpu":
+            ClusteredSparseDotProduct.dot_backward[Q.device.type](
+                Q,
+                K,
+                groups,
+                topk,
+                grad_output,
+                grad_Q,
+                grad_K
+            )
+
+        else:
+            N, H, L, E = Q.shape
+            _, _, C, k = topk.shape
+            # Allocate bookkeeping parameters to facilitate the kernel
+            with torch.no_grad():
+                Q_pb = 16
+                block_counts = (counts + Q_pb - 1) // Q_pb
+                block_counts = block_counts.int()
+                block_counts_cumsum = block_counts.view(-1).cumsum(-1).view(N, H, C).int()
+                indx_maps = torch.ones(
+                    (block_counts.sum(), 4),
+                    device=Q.device,
+                    dtype=torch.int32
+                )
+
+                counts_cumsum = counts.cumsum(-1).int()
+                total_blocks = block_counts.sum().item()
+
+            # Actually perform the backward pass
+            ClusteredSparseDotProduct.dot_backward[Q.device.type](
+                Q,
+                K,
+                groups.int(),
+                topk.int(),
+                grad_output,
+                grad_Q,
+                grad_K,
+                counts_cumsum - counts,
+                counts_cumsum,
+                block_counts,
+                block_counts_cumsum,
+                total_blocks,
+                indx_maps
+            )
+
+        return grad_Q, grad_K, None, None, None, None, None
+
+
+class ClusteredSparseWeightedAverage(torch.autograd.Function):
+    """Compute the weighted average only for the topk values."""
+    avg = {
+        "cpu": clustered_sparse_weighted_average_cpu,
+        "cuda": clustered_sparse_weighted_average_cuda
+    }
+    avg_backward = {
+        "cpu": clustered_sparse_weighted_average_backward_cpu,
+        "cuda": clustered_sparse_weighted_average_backward_cuda
+    }
+
+    @staticmethod
+    def forward(ctx, weights, values, topk, groups, counts):
+        # Save the tensors to compute the gradient
+        ctx.save_for_backward(weights, values, topk, groups, counts)
+
+        # Allocate the output tensor
+        N, H, L, _ = weights.shape
+        _, _, _, E = values.shape
+        _, _, C, _ = topk.shape
+        output = values.new_zeros(N, H, L, E)
+        device = weights.device
+
+        if device.type == "cpu":
+            # Compute the average
+            ClusteredSparseWeightedAverage.avg[weights.device.type](
+                weights,
+                values,
+                groups,
+                topk,
+                output
+            )
+        else:
+            # Bookkeeping parameters to facilitate the GPU cuda kernel
+            with torch.no_grad():
+                Q_pb = 16
+                block_counts = (counts + Q_pb - 1) // Q_pb
+                block_counts = block_counts.int()
+                block_counts_cumsum = block_counts.view(-1).cumsum(-1).view(N, H, C).int()
+                indx_maps = torch.ones(
+                    (block_counts.sum(), 4),
+                    device=weights.device,
+                    dtype=torch.int32
+                )
+                counts_cumsum = counts.cumsum(-1).int()
+                total_blocks = block_counts.sum().item()
+
+            # Compute the average
+            ClusteredSparseWeightedAverage.avg[device.type](
+                weights,
+                values,
+                topk.int(),
+                output,
+                counts_cumsum - counts,
+                counts_cumsum,
+                block_counts,
+                block_counts_cumsum,
+                total_blocks,
+                indx_maps
+            )
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        # Extract the saved tensors and allocate memory for the gradients
+        weights, values, topk, groups, counts = ctx.saved_tensors
+        grad_weights = torch.zeros_like(weights)
+        grad_values = torch.zeros_like(values)
+
+        if grad_output.stride()[-1] != 1:
+            grad_output = grad_output.contiguous()
+
+        device = weights.device
+        if device.type == "cpu":
+            ClusteredSparseWeightedAverage.avg_backward[weights.device.type](
+                weights,
+                values,
+                groups,
+                topk,
+                grad_output,
+                grad_weights,
+                grad_values
+            )
+        else:
+            # Bookkeeping parameters to facilitate the cuda kernel
+            with torch.no_grad():
+                N, H, C = counts.shape
+                Q_pb = 16
+                block_counts = (counts + Q_pb - 1) // Q_pb
+                block_counts = block_counts.int()
+                block_counts_cumsum = block_counts.view(-1).cumsum(-1).view(N, H, C).int()
+
+                indx_maps = torch.ones(
+                    (block_counts.sum(), 4),
+                    device=weights.device,
+                    dtype=torch.int32
+                )
+                counts_cumsum = counts.cumsum(-1).int()
+                total_blocks = block_counts.sum().item()
+
+            # Do sparse weighted average backward pass
+            ClusteredSparseWeightedAverage.avg_backward[device.type](
+                weights,
+                values,
+                topk.int(),
+                grad_output,
+                grad_weights,
+                grad_values,
+                counts_cumsum - counts,
+                counts_cumsum,
+                block_counts,
+                block_counts_cumsum,
+                total_blocks,
+                indx_maps
+            )
+        return grad_weights, grad_values, None, None, None, None
+
+
+# Alias the autograd functions to python style snake case naming
+clustered_sparse_dot_product = ClusteredSparseDotProduct.apply
+clustered_sparse_weighted_average = ClusteredSparseWeightedAverage.apply
+
+# Alias the autograd functions to python style snake case naming
+sparse_dot_product = SparseDotProduct.apply
+sparse_weighted_average = SparseWeightedAverage.apply
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/sparse_product/clustered_sparse_product_cpu.cpython-39-x86_64-linux-gnu.so b/models/smi_ted/smi_ted_light/fast_transformers/sparse_product/clustered_sparse_product_cpu.cpython-39-x86_64-linux-gnu.so
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diff --git a/models/smi_ted/smi_ted_light/fast_transformers/transformers.py b/models/smi_ted/smi_ted_light/fast_transformers/transformers.py
new file mode 100644
index 0000000000000000000000000000000000000000..744996c38623dd14506db3c11f71f07f118caa62
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/transformers.py
@@ -0,0 +1,294 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""Implement transformer encoders and decoders that are going to be used with
+different attention mechanisms.
+
+In all cases the batch dimension is first and the sequence dimension is second.
+"""
+
+import torch
+from torch.nn import Dropout, LayerNorm, Linear, Module, ModuleList
+import torch.nn.functional as F
+
+from .events import EventDispatcher
+from .masking import FullMask, LengthMask
+
+
+class TransformerEncoderLayer(Module):
+    """Self attention and feed forward network with skip connections.
+
+    This transformer encoder layer implements the same encoder layer as
+    PyTorch but is a bit more open for extension by receiving the attention
+    implementation as a constructor argument.
+
+    Arguments
+    ---------
+        attention: The attention implementation to use given as a nn.Module
+        d_model: The input feature dimensionality
+        d_ff: The dimensionality of the intermediate features after the
+              attention (default: d_model*4)
+        dropout: The dropout rate to apply to the intermediate features
+                 (default: 0.1)
+        activation: {'relu', 'gelu'} Which activation to use for the feed
+                    forward part of the layer (default: relu)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, attention, d_model, d_ff=None, dropout=0.1,
+                 activation="relu", event_dispatcher=""):
+        super(TransformerEncoderLayer, self).__init__()
+        d_ff = d_ff or 4*d_model
+        self.attention = attention
+        self.linear1 = Linear(d_model, d_ff)
+        self.linear2 = Linear(d_ff, d_model)
+        self.norm1 = LayerNorm(d_model)
+        self.norm2 = LayerNorm(d_model)
+        self.dropout = Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, x, attn_mask=None, length_mask=None):
+        """Apply the transformer encoder to the input x.
+
+        Arguments
+        ---------
+            x: The input features of shape (N, L, E) where N is the batch size,
+               L is the sequence length (padded) and E is d_model passed in the
+               constructor.
+            attn_mask: An implementation of fast_transformers.masking.BaseMask
+                       that encodes where each element of x can attend to.
+            length_mask: An implementation of
+                         fast_transformers.masking.BaseMask that encodes how
+                         many elements each sequence in the batch consists of.
+        """
+        # Normalize the masks
+        N = x.shape[0]
+        L = x.shape[1]
+        attn_mask = attn_mask or FullMask(L, device=x.device)
+        length_mask = length_mask or \
+            LengthMask(x.new_full((N,), L, dtype=torch.int64))
+
+        # Run self attention and add it to the input
+        x = x + self.dropout(self.attention(
+            x, x, x,
+            attn_mask=attn_mask,
+            query_lengths=length_mask,
+            key_lengths=length_mask
+        ))
+
+        # Run the fully connected part of the layer
+        y = x = self.norm1(x)
+        y = self.dropout(self.activation(self.linear1(y)))
+        y = self.dropout(self.linear2(y))
+
+        return self.norm2(x+y)
+
+
+class TransformerEncoder(Module):
+    """TransformerEncoder is little more than a sequence of transformer encoder
+    layers.
+
+    It contains an optional final normalization layer as well as the ability to
+    create the masks once and save some computation.
+
+    Arguments
+    ---------
+        layers: list, TransformerEncoderLayer instances or instances that
+                implement the same interface.
+        norm_layer: A normalization layer to be applied to the final output
+                    (default: None which means no normalization)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, layers, norm_layer=None, event_dispatcher=""):
+        super(TransformerEncoder, self).__init__()
+        self.layers = ModuleList(layers)
+        self.norm = norm_layer
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, x, attn_mask=None, length_mask=None):
+        """Apply all transformer encoder layers to the input x.
+
+        Arguments
+        ---------
+            x: The input features of shape (N, L, E) where N is the batch size,
+               L is the sequence length (padded) and E is d_model passed in the
+               constructor of each transformer encoder layer.
+            attn_mask: An implementation of fast_transformers.masking.BaseMask
+                       that encodes where each element of x can attend to.
+            length_mask: An implementation of
+                         fast_transformers.masking.BaseMask that encodes how
+                         many elements each sequence in the batch consists of.
+        """
+        # Normalize the masks
+        N = x.shape[0]
+        L = x.shape[1]
+        attn_mask = attn_mask or FullMask(L, device=x.device)
+        length_mask = length_mask or \
+            LengthMask(x.new_full((N,), L, dtype=torch.int64))
+
+        # Apply all the transformers
+        for layer in self.layers:
+            x = layer(x, attn_mask=attn_mask, length_mask=length_mask)
+
+        # Apply the normalization if needed
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x
+
+
+class TransformerDecoderLayer(Module):
+    """The decoder layer from "Attention Is All You Need".
+
+    Similar to the encoder layer, this layer implements the decoder that
+    PyTorch implements but can be used with any attention implementation
+    because it receives the attention layers as constructor arguments.
+
+    Arguments
+    ---------
+        self_attention: The attention implementation to use for self attention
+                        given as a nn.Module
+        cross_attention: The attention implementation to use for cross
+                         attention given as a nn.Module
+        d_model: The input feature dimensionality
+        d_ff: The dimensionality of the intermediate features after the
+              attention (default: d_model*4)
+        dropout: The dropout rate to apply to the intermediate features
+                 (default: 0.1)
+        activation: {'relu', 'gelu'} Which activation to use for the feed
+                    forward part of the layer (default: relu)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
+                 dropout=0.1, activation="relu", event_dispatcher=""):
+        super(TransformerDecoderLayer, self).__init__()
+        d_ff = d_ff or 4*d_model
+        self.self_attention = self_attention
+        self.cross_attention = cross_attention
+        self.linear1 = Linear(d_model, d_ff)
+        self.linear2 = Linear(d_ff, d_model)
+        self.norm1 = LayerNorm(d_model)
+        self.norm2 = LayerNorm(d_model)
+        self.norm3 = LayerNorm(d_model)
+        self.dropout = Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, x, memory, x_mask=None, x_length_mask=None,
+                memory_mask=None, memory_length_mask=None):
+        """Apply the transformer decoder to the input x using the memory
+        `memory`.
+
+        Arguments
+        ---------
+            x: The input features of shape (N, L, E) where N is the batch size,
+               L is the sequence length (padded) and E should be the same as
+               the d_model passed in the constructor.
+            memory: The memory features of shape (N, L', E) where N is the
+                    batch size, L' is the memory's sequence length (padded) and
+                    E should be the same as the d_model.
+            x_mask: An implementation of fast_transformers.masking.BaseMask
+                    that encodes where each element of x can attend to in x.
+                    Namely the self attention mask.
+            x_length_mask: An implementation of a BaseMask that encodes how
+                           many elements each sequence in the batch consists
+                           of.
+            memory_mask: An implementation of BaseMask that encodes where each
+                         element of x can attend to in the memory. Namely the
+                         cross attention mask.
+            memory_length_mask: An implementation of a BaseMask that encodes how
+                                many elements each memory sequence in the batch
+                                consists of.
+        """
+        # Normalize the masks
+        N = x.shape[0]
+        L = x.shape[1]
+        L_prime = memory.shape[1]
+        x_mask = x_mask or FullMask(L, device=x.device)
+        x_length_mask = x_length_mask  or \
+            LengthMask(x.new_full((N,), L, dtype=torch.int64))
+        memory_mask = memory_mask or FullMask(L, L_prime, device=x.device)
+        memory_length_mask = memory_length_mask or \
+            LengthMask(x.new_full((N,), L_prime, dtype=torch.int64))
+
+        # First apply the self attention and add it to the input
+        x = x + self.dropout(self.self_attention(
+            x, x, x,
+            attn_mask=x_mask,
+            query_lengths=x_length_mask,
+            key_lengths=x_length_mask
+        ))
+        x = self.norm1(x)
+
+        # Secondly apply the cross attention and add it to the previous output
+        x = x + self.dropout(self.cross_attention(
+            x, memory, memory,
+            attn_mask=memory_mask,
+            query_lengths=x_length_mask,
+            key_lengths=memory_length_mask
+        ))
+
+        # Finally run the fully connected part of the layer
+        y = x = self.norm2(x)
+        y = self.dropout(self.activation(self.linear1(y)))
+        y = self.dropout(self.linear2(y))
+
+        return self.norm3(x+y)
+
+
+class TransformerDecoder(Module):
+    """TransformerDecoder is little more than a sequence of transformer decoder
+    layers.
+
+    It contains an optional final normalization layer as well as the ability to
+    create the masks once and save some computation.
+
+    Arguments
+    ----------
+        layers: list, TransformerDecoderLayer instances or instances that
+                implement the same interface
+        norm_layer: A normalization layer to be applied to the final output
+                    (default: None which means no normalization)
+        event_dispatcher: str or EventDispatcher instance to be used by this
+                          module for dispatching events (default: the default
+                          global dispatcher)
+    """
+    def __init__(self, layers, norm_layer=None, event_dispatcher=""):
+        super(TransformerDecoder, self).__init__()
+        self.layers = ModuleList(layers)
+        self.norm = norm_layer
+        self.event_dispatcher = EventDispatcher.get(event_dispatcher)
+
+    def forward(self, x, memory, x_mask=None, x_length_mask=None,
+                memory_mask=None, memory_length_mask=None):
+        # Normalize the masks
+        N = x.shape[0]
+        L = x.shape[1]
+        L_prime = memory.shape[1]
+        x_mask = x_mask or FullMask(L, device=x.device)
+        x_length_mask = x_length_mask  or \
+            LengthMask(x.new_full((N,), L, dtype=torch.int64))
+        memory_mask = memory_mask or FullMask(L, L_prime, device=x.device)
+        memory_length_mask = memory_length_mask or \
+            LengthMask(x.new_full((N,), L_prime, dtype=torch.int64))
+
+        # Apply all the transformer decoders
+        for layer in self.layers:
+            x = layer(x, memory, x_mask=x_mask, x_length_mask=x_length_mask,
+                      memory_mask=memory_mask,
+                      memory_length_mask=memory_length_mask)
+
+        # Apply the normalization if needed
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/utils.py b/models/smi_ted/smi_ted_light/fast_transformers/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2965e8d1de37797df83be134a59417a863bb90a6
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/utils.py
@@ -0,0 +1,33 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>
+#
+
+"""Boilerplate code for dealing with fast_transformers modules."""
+
+
+def make_mirror(src_module, dst_module):
+    """Sets the parameters of src_module to dst_module so that they share the
+    same parameters.
+
+    Most noteable usecase is to make a recurrent transformer mirror of a batch
+    transformer for fast inference.
+
+    Arguments
+    ---------
+        src_module: Module to take the parameters from
+        dst_module: Module to set the parameters to
+
+    Returns
+    -------
+        None, it changes dst_module in place
+    """
+    def setattr_recursive(mod, key, value):
+        key, *next_key = key.split(".", maxsplit=1)
+        if not next_key:
+            setattr(mod, key, value)
+        else:
+            setattr_recursive(getattr(mod, key), next_key[0], value)
+
+    for name, param in src_module.named_parameters():
+        setattr_recursive(dst_module, name, param)
diff --git a/models/smi_ted/smi_ted_light/fast_transformers/weight_mapper.py b/models/smi_ted/smi_ted_light/fast_transformers/weight_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9e6c48d9c6e86d487616bea4709abbcc3201346
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/fast_transformers/weight_mapper.py
@@ -0,0 +1,273 @@
+#
+# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
+# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
+# Apoorv Vyas <avyas@idiap.ch>
+#
+
+"""The weight mapper module provides a utility to load transformer model
+weights from other implementations to a fast_transformers model.
+
+NOTE: This API is lkely to change in the future as we collect more information
+      regarding how people use it.
+"""
+
+import re
+
+
+class MappingRule(object):
+    """A mapping rule can be applied to a key and value and it returns new keys
+    and values to be added in the state dict."""
+    def matches(self, key):
+        """Check whether this mapping rule should be applied to this key."""
+        raise NotImplementedError()
+
+    def apply(self, key, value):
+        """Apply the rule and map the key to a new one."""
+        raise NotImplementedError()
+
+
+class IdentityRule(MappingRule):
+    """The identity rule matches all keys and returns them as is."""
+    def matches(self, key):
+        return True
+
+    def apply(self, key, value):
+        return [(key, value)]
+
+
+class NotRule(MappingRule):
+    """Decorate a MappingRule by using a logical not for the matches function
+    and identity for the apply."""
+    def __init__(self, rule):
+        self.rule = rule
+
+    def matches(self, key):
+        return not self.rule.matches(key)
+
+    def apply(self, key, value):
+        return [(key, value)]
+
+class OrRule(MappingRule):
+    """Decorate some MappingRules using the logical or to create a matches
+    function that returns True if any of the rules matches. In case of a match
+    apply all of the rules."""
+    def __init__(self, *rules):
+        self.rules = rules
+
+    def matches(self, key):
+        return any(r.matches(key) for r in self.rules)
+
+    def apply(self, key, value):
+        items = [(key, value)]
+        for r in self.rules:
+            items = [
+                r.apply(k, v)
+                for k, v in items
+            ]
+        return items
+
+
+class RegexRule(MappingRule):
+    """Apply a regex search and replace on a key.
+
+    Arguments
+    ---------
+        search: str, the regex pattern to search and replace
+        replace: str or callable, the replacement for every occurence of the
+                 search pattern. If it is a callable it should follow the rules
+                 of python re.sub().
+    """
+    def __init__(self, search, replace):
+        self.pattern = re.compile(search)
+        self.replace = replace
+
+    def matches(self, key):
+        return self.pattern.search(key) is not None
+
+    def apply(self, key, value):
+        return [(self.pattern.sub(self.replace, key), value)]
+
+
+class PytorchAttentionWeightsRule(MappingRule):
+    """Map the merged MultiheadAttention weights to the corresponding keys and
+    values."""
+    def __init__(self):
+        self.weight_pattern = "self_attn.in_proj_weight"
+        self.bias_pattern = "self_attn.in_proj_bias"
+
+    def matches(self, key):
+        return (
+            self.weight_pattern in key or
+            self.bias_pattern in key
+        )
+
+    def apply(self, key, value):
+        N = value.shape[0]
+        if self.weight_pattern in key:
+            return [
+                (
+                    key.replace(
+                        self.weight_pattern,
+                        "attention.query_projection.weight"
+                    ),
+                    value[:N//3]
+                ),
+                (
+                    key.replace(
+                        self.weight_pattern,
+                        "attention.key_projection.weight"
+                    ),
+                    value[N//3:2*N//3]
+                ),
+                (
+                    key.replace(
+                        self.weight_pattern,
+                        "attention.value_projection.weight"
+                    ),
+                    value[2*N//3:]
+                )
+            ]
+        if self.bias_pattern in key:
+                return [
+                    (
+                        key.replace(
+                            self.bias_pattern,
+                            "attention.query_projection.bias"
+                        ),
+                        value[:N//3]
+                    ),
+                    (
+                        key.replace(
+                            self.bias_pattern,
+                            "attention.key_projection.bias"
+                        ),
+                        value[N//3:2*N//3]
+                    ),
+                    (
+                        key.replace(
+                            self.bias_pattern,
+                            "attention.value_projection.bias"
+                        ),
+                        value[2*N//3:]
+                    )
+                ]
+
+
+class SimpleMapper(object):
+    """Map keys of a state dict to other keys.
+
+    Arguments
+    ---------
+        rules: A list of mapping rules to apply to the keys (default: []).
+        add_identity: bool, if set to True add a catch all identity rule as the
+                      final rule (default: True).
+    """
+    def __init__(self, rules=[], add_identity=True):
+        self._rules = rules
+        if add_identity:
+            self._rules.append(IdentityRule())
+
+    def map(self, state_dict):
+        new_state = {}
+        for k, v in state_dict.items():
+            for rule in self._rules:
+                if rule.matches(k):
+                    for nk, nv in rule.apply(k, v):
+                        new_state[nk] = nv
+                    break
+        return new_state
+
+    @classmethod
+    def load_file(cls, filepath, model_root=None, map_location=None,
+                  **other_args):
+        """Load the file and apply the weight map.
+
+        The model root the key that contains the state dict to be mapped.
+
+        Arguments
+        ---------
+            filepath: The file containing the saved state.
+            model_root: The key for the state dict to be mapped, if None assume
+                        it is the top level dictionary (default: None).
+            map_location: The parameter is passed to torch.load .
+            other_args: The parameter dict is passed to torch.load because it
+                        expects a similar dictionary of arguments to pass to
+                        pickle.load.
+        """
+        state = torch.load(filepath, map_location=map_location, **other_args)
+        if model_root is None:
+            state = cls().map(state)
+        else:
+            state[model_root] = cls().map(state[model_root])
+
+        return state
+
+
+class PytorchMapper(SimpleMapper):
+    """Map a Pytorch transformer encoder state dict to a fast transformers
+    one."""
+    def __init__(self):
+        super(PytorchMapper, self).__init__([
+            PytorchAttentionWeightsRule(),
+            RegexRule(
+                r"layers\.(\d+)\.self_attn\.([a-z]+)_proj(ection)?\.",
+                r"layers.\1.attention.\2_projection."
+            ),
+            NotRule(OrRule(
+                RegexRule(
+                    r"\.softmax_temp$",
+                    r""
+                )
+            ))
+        ], add_identity=False)
+
+
+class HugginfaceBertEncoderMapper(SimpleMapper):
+    """Map the weights of a model that uses a BertEncoder to our fast
+    transformers."""
+    RULES = [
+        RegexRule(
+            r"layer\.(\d+)\.attention\.self\.(query|key|value)",
+            r"layers.\1.attention.\2_projection"
+        ),
+        RegexRule(
+            r"layer\.(\d+)\.attention\.output\.dense",
+            r"layers.\1.attention.out_projection"
+        ),
+        RegexRule(
+            r"layer\.(\d+)\.attention\.output\.LayerNorm",
+            r"layers.\1.norm1"
+        ),
+        RegexRule(
+            r"layer\.(\d+)\.intermediate\.dense",
+            r"layers.\1.linear1"
+        ),
+        RegexRule(
+            r"layer\.(\d+)\.output\.dense",
+            r"layers.\1.linear2"
+        ),
+        RegexRule(
+            r"layer\.(\d+)\.output\.LayerNorm",
+            r"layers.\1.norm2"
+        )
+    ]
+
+    def __init__(self):
+        super(HugginfaceBertEncoderMapper, self).__init__(self.RULES)
+
+
+class LongformerMapper(SimpleMapper):
+    """Map the longformer weights to our fast transformers.
+
+    NOTE: The projections for the global attention are ignored.
+    """
+    def __init__(self):
+        super(LongformerMapper, self).__init__(
+            HugginfaceBertEncoderMapper.RULES + [
+                NotRule(RegexRule(
+                    r"layer\.(\d+)\.attention\.self\.(query|key|value)_global",
+                    ""
+                ))
+            ],
+            add_identity=False
+        )
diff --git a/models/smi_ted/smi_ted_light/load.py b/models/smi_ted/smi_ted_light/load.py
new file mode 100644
index 0000000000000000000000000000000000000000..e10cf1524dcd7f4c9a08460fc7960f2428fd654b
--- /dev/null
+++ b/models/smi_ted/smi_ted_light/load.py
@@ -0,0 +1,619 @@
+PATTERN = "(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\\\|\/|:|~|@|\?|>|\*|\$|\%[0-9]{2}|[0-9])"
+# Deep learning
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.backends.cudnn as cudnn
+
+# Transformers
+from .fast_transformers.attention import AttentionLayer
+from .fast_transformers.events import QKVEvent
+from .fast_transformers.transformers import TransformerEncoder, TransformerEncoderLayer
+from .fast_transformers.builders.transformer_builders import BaseTransformerEncoderBuilder
+from .fast_transformers.builders.attention_builders import AttentionBuilder
+from .fast_transformers.feature_maps import GeneralizedRandomFeatures
+from .fast_transformers.masking import LengthMask
+from transformers import BertTokenizer
+from huggingface_hub import hf_hub_download
+
+# Data
+import numpy as np
+import pandas as pd
+
+# Standard library
+from functools import partial
+import regex as re
+import random
+import os
+import gc
+from tqdm import tqdm
+tqdm.pandas()
+
+
+class MolTranBertTokenizer(BertTokenizer):
+    def __init__(self, vocab_file: str = '',
+                 do_lower_case=False,
+                 unk_token='<pad>',
+                 sep_token='<eos>',
+                 pad_token='<pad>',
+                 cls_token='<bos>',
+                 mask_token='<mask>',
+                 **kwargs):
+        super().__init__(vocab_file,
+                         unk_token=unk_token,
+                         sep_token=sep_token,
+                         pad_token=pad_token,
+                         cls_token=cls_token,
+                         mask_token=mask_token,
+                         **kwargs)
+
+        self.regex_tokenizer = re.compile(PATTERN)
+        self.wordpiece_tokenizer = None
+        self.basic_tokenizer = None
+        with open(vocab_file) as f:
+            self.padding_idx = f.readlines().index(pad_token+'\n')
+
+    def _tokenize(self, text):
+        split_tokens = self.regex_tokenizer.findall(text)
+        return split_tokens
+    
+    def convert_idx_to_tokens(self, idx_tensor):
+        tokens = [self.convert_ids_to_tokens(idx) for idx in idx_tensor.tolist()]
+        return tokens
+
+    def convert_tokens_to_string(self, tokens):
+        stopwords = ['<bos>', '<eos>']
+        clean_tokens = [word for word in tokens if word not in stopwords]
+        out_string = ''.join(clean_tokens)
+        return out_string
+    
+    def get_padding_idx(self):
+        return self.padding_idx
+    
+    def idx_to_smiles(self, torch_model, idx):
+        '''Convert tokens idx back to SMILES text'''
+        rev_tokens = torch_model.tokenizer.convert_idx_to_tokens(idx)
+        flat_list_tokens = [item for sublist in rev_tokens for item in sublist]
+        decoded_smiles = torch_model.tokenizer.convert_tokens_to_string(flat_list_tokens)
+        return decoded_smiles
+
+
+## Transformer layers
+class RotaryEmbedding(torch.nn.Module):
+    
+    def __init__(self, dim, base=10000):
+        super().__init__()
+        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+        self.seq_len_cached = 0 
+        self.cos_cached = None
+        self.sin_cached = None
+
+    def forward(self, x, seq_dim=1):
+        seq_len = x.shape[seq_dim]
+        if seq_len != self.seq_len_cached:
+            self.seq_len_cached = seq_len
+            
+            t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq)
+            freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+            
+            self.cos_cached = emb.cos()[None,:, None, :]
+            self.sin_cached = emb.sin()[None,:, None, :]
+                
+        return self.cos_cached, self.sin_cached
+
+def rotate_half(x):
+    x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions
+
+@torch.jit.script
+def apply_rotary_pos_emb(q, k, cos, sin):
+    return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
+
+class RotateAttentionLayer(AttentionLayer):
+    """Rotate attention layer inherits from fast_transformer attention layer. 
+        The only thing added is an Embedding encoding, for more information
+        on the attention layer see the fast_transformers code
+    """
+    def __init__(self, attention, d_model, n_heads, d_keys=None,
+                 d_values=None, event_dispatcher=""):
+        super(RotateAttentionLayer, self).__init__(attention,d_model, n_heads, d_keys=d_keys,
+                 d_values=d_values, event_dispatcher=event_dispatcher)
+
+        self.rotaryemb = RotaryEmbedding(d_keys)
+        print('Using Rotation Embedding')
+
+    def forward(self, queries, keys, values, attn_mask, query_lengths,
+                key_lengths):
+        """
+        Using the same frame work as the fast_Transformers attention layer
+        but injecting rotary information to the queries and the keys
+        after the keys and queries are projected. 
+        In the argument description we make use of the following sizes
+            - N: the batch size
+            - L: The maximum length of the queries
+            - S: The maximum length of the keys (the actual length per sequence
+              is given by the length mask)
+            - D: The input feature dimensionality passed in the constructor as
+              'd_model'
+        Arguments
+        ---------
+            queries: (N, L, D) The tensor containing the queries
+            keys: (N, S, D) The tensor containing the keys
+            values: (N, S, D) The tensor containing the values
+            attn_mask: An implementation of BaseMask that encodes where each
+                       query can attend to
+            query_lengths: An implementation of  BaseMask that encodes how
+                           many queries each sequence in the batch consists of
+            key_lengths: An implementation of BaseMask that encodes how
+                         many queries each sequence in the batch consists of
+        Returns
+        -------
+            The new value for each query as a tensor of shape (N, L, D).
+        """
+        # Extract the dimensions into local variables
+        N, L, _ = queries.shape
+        _, S, _ = keys.shape
+        H = self.n_heads
+
+        # Project the queries/keys/values
+        queries = self.query_projection(queries).view(N, L, H, -1)
+        keys = self.key_projection(keys).view(N, S, H, -1)
+        cos, sin = self.rotaryemb(queries)
+        queries, keys = apply_rotary_pos_emb(queries, keys, cos, sin)
+        values = self.value_projection(values).view(N, S, H, -1)
+        # Let the world know of the qkv
+        self.event_dispatcher.dispatch(QKVEvent(self, queries, keys, values))
+
+
+        # Compute the attention
+        new_values = self.inner_attention(
+            queries,
+            keys,
+            values,
+            attn_mask,
+            query_lengths,
+            key_lengths
+        ).view(N, L, -1)
+
+        # Project the output and return
+        return self.out_projection(new_values)
+
+class RotateEncoderBuilder(BaseTransformerEncoderBuilder):
+    """Build a batch transformer encoder with Relative Rotary embeddings
+    for training or processing of sequences all elements at a time.
+    Example usage:
+        builder = RotateEncoderBuilder()
+        builder.n_layers = 12
+        builder.n_heads = 8
+        builder.feed_forward_dimensions = 1024
+        builder.query_dimensions = 64
+        builder.value_dimensions = 64
+        builder.dropout = 0.1
+        builder.attention_dropout = 0.1
+        builder.attention_type = "linear"
+        transformer = builder.get()
+    """
+    def _get_attention_builder(self):
+        """Return an instance of the appropriate attention builder."""
+        return AttentionBuilder()
+
+    def _get_attention_layer_class(self):
+        """Return the class for the layer that projects queries keys and
+        values."""
+        return RotateAttentionLayer
+
+    def _get_encoder_class(self):
+        """Return the class for the transformer encoder."""
+        return TransformerEncoder
+
+    def _get_encoder_layer_class(self):
+        """Return the class for the transformer encoder layer."""
+        return TransformerEncoderLayer
+
+
+class AutoEncoderLayer(nn.Module):
+
+    def __init__(self, feature_size, latent_size):
+        super().__init__()
+        self.encoder = self.Encoder(feature_size, latent_size)
+        self.decoder = self.Decoder(feature_size, latent_size)
+    
+    class Encoder(nn.Module):
+
+        def __init__(self, feature_size, latent_size):
+            super().__init__()
+            self.is_cuda_available = torch.cuda.is_available()
+            self.fc1 = nn.Linear(feature_size, latent_size)
+            self.ln_f = nn.LayerNorm(latent_size)
+            self.lat = nn.Linear(latent_size, latent_size, bias=False)
+        
+        def forward(self, x):
+            if self.is_cuda_available:
+                self.fc1.cuda()
+                self.ln_f.cuda()
+                self.lat.cuda()
+                x = x.cuda()
+            x = F.gelu(self.fc1(x))
+            x = self.ln_f(x)
+            x = self.lat(x)
+            return x # -> (N, D)
+    
+    class Decoder(nn.Module):
+
+        def __init__(self, feature_size, latent_size):
+            super().__init__()
+            self.is_cuda_available = torch.cuda.is_available()
+            self.fc1 = nn.Linear(latent_size, latent_size)
+            self.ln_f = nn.LayerNorm(latent_size)
+            self.rec = nn.Linear(latent_size, feature_size, bias=False)
+        
+        def forward(self, x):
+            if self.is_cuda_available:
+                self.fc1.cuda()
+                self.ln_f.cuda()
+                self.rec.cuda()
+                x = x.cuda()
+            x = F.gelu(self.fc1(x))
+            x = self.ln_f(x)
+            x = self.rec(x)
+            return x # -> (N, L*D)
+
+
+class LangLayer(nn.Module):
+
+    def __init__(self, n_embd, n_vocab):
+        super().__init__()
+        self.is_cuda_available = torch.cuda.is_available()
+        self.embed = nn.Linear(n_embd, n_embd)
+        self.ln_f = nn.LayerNorm(n_embd)
+        self.head = nn.Linear(n_embd, n_vocab, bias=False)
+    
+    def forward(self, tensor):
+        if self.is_cuda_available:
+            self.embed.cuda()
+            self.ln_f.cuda()
+            self.head.cuda()
+            tensor = tensor.cuda()
+        tensor = self.embed(tensor)
+        tensor = F.gelu(tensor)
+        tensor = self.ln_f(tensor)
+        tensor = self.head(tensor)
+        return tensor
+    
+
+class Net(nn.Module):
+    
+    def __init__(self, smiles_embed_dim, n_output=1, dropout=0.2):
+        super().__init__()
+        self.desc_skip_connection = True
+        self.fc1 = nn.Linear(smiles_embed_dim, smiles_embed_dim)
+        self.dropout1 = nn.Dropout(dropout)
+        self.relu1 = nn.GELU()
+        self.fc2 = nn.Linear(smiles_embed_dim, smiles_embed_dim)
+        self.dropout2 = nn.Dropout(dropout)
+        self.relu2 = nn.GELU()
+        self.final = nn.Linear(smiles_embed_dim, n_output)
+
+    def forward(self, smiles_emb, multitask=False):
+        x_out = self.fc1(smiles_emb)
+        x_out = self.dropout1(x_out)
+        x_out = self.relu1(x_out)
+
+        if self.desc_skip_connection is True:
+            x_out = x_out + smiles_emb
+
+        z = self.fc2(x_out)
+        z = self.dropout2(z)
+        z = self.relu2(z)
+        if self.desc_skip_connection is True:
+            z = self.final(z + x_out)
+        else:
+            z = self.final(z)
+
+        if multitask:
+            return F.sigmoid(z)
+        return z
+
+
+class MoLEncoder(nn.Module):
+
+    def __init__(self, config, n_vocab):
+        super(MoLEncoder, self).__init__()
+
+        # embeddings
+        self.config = config
+        self.tok_emb = nn.Embedding(n_vocab, config['n_embd'])
+        self.drop = nn.Dropout(config['d_dropout'])
+
+        # transformer
+        builder = RotateEncoderBuilder.from_kwargs(
+            n_layers=config['n_layer'],
+            n_heads=config['n_head'],
+            query_dimensions=config['n_embd']//config['n_head'],
+            value_dimensions=config['n_embd']//config['n_head'],
+            feed_forward_dimensions=config['n_embd'],
+            attention_type='linear',
+            # unless we do deterministic_eval here, we will have random outputs
+            feature_map=partial(GeneralizedRandomFeatures, 
+                                n_dims=config['num_feats'], 
+                                deterministic_eval=True),
+            activation='gelu'
+        )
+        self.blocks = builder.get()
+
+        # classification
+        self.lang_model = LangLayer(config['n_embd'], n_vocab)
+
+    def forward(self, idx, mask):
+        # transformer encoder
+        x = self.tok_emb(idx) # each index maps to a (learnable) vector
+        x = self.drop(x)
+        x = self.blocks(x, length_mask=LengthMask(mask.sum(-1), max_len=idx.shape[1]))
+
+        # add padding
+        token_embeddings = x
+        input_mask_expanded = mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+        mask_embeddings = (token_embeddings * input_mask_expanded)
+        token_embeddings = F.pad(mask_embeddings, pad=(0, 0, 0, self.config['max_len'] - mask_embeddings.shape[1]), value=0)
+
+        return token_embeddings
+
+
+class MoLDecoder(nn.Module):
+
+    def __init__(self, n_vocab, max_len, n_embd, n_gpu=None):
+        super(MoLDecoder, self).__init__()
+
+        self.max_len = max_len
+        self.n_embd = n_embd
+        self.n_gpu = n_gpu
+        self.autoencoder = AutoEncoderLayer(n_embd*max_len, n_embd)
+        self.lang_model = LangLayer(n_embd, n_vocab)
+
+
+class Smi_ted(nn.Module):
+    """materials.smi-ted-Light 289M Parameters"""
+
+    def __init__(self, tokenizer, config=None):
+        super(Smi_ted, self).__init__()
+
+        # configuration
+        self.config = config
+        self.tokenizer = tokenizer
+        self.padding_idx = tokenizer.get_padding_idx()
+        self.n_vocab = len(self.tokenizer.vocab)
+        self.is_cuda_available = torch.cuda.is_available()
+
+        # instantiate modules
+        if self.config:
+            self.encoder = MoLEncoder(self.config, self.n_vocab)
+            self.decoder = MoLDecoder(self.n_vocab, self.config['max_len'], self.config['n_embd'])
+            self.net = Net(self.config['n_embd'], n_output=self.config['n_output'], dropout=self.config['d_dropout'])
+        
+    def load_checkpoint(self, ckpt_path):
+        # load checkpoint file
+        checkpoint = torch.load(ckpt_path, map_location=torch.device('cpu'))
+
+        # load hyparameters
+        self.config = checkpoint['hparams']
+        self.max_len = self.config['max_len']
+        self.n_embd = self.config['n_embd']
+        self._set_seed(self.config['seed'])
+
+        # instantiate modules
+        self.encoder = MoLEncoder(self.config, self.n_vocab)
+        self.decoder = MoLDecoder(self.n_vocab, self.max_len, self.n_embd)
+        self.net = Net(self.n_embd, n_output=self.config['n_output'] if 'n_output' in self.config else 1, dropout=self.config['d_dropout'])
+
+        # load weights
+        if 'state_dict' in checkpoint:
+            if isinstance(checkpoint['state_dict'], list):
+                self.encoder.load_state_dict(checkpoint['state_dict'][0], strict=False)
+                self.decoder.load_state_dict(checkpoint['state_dict'][1], strict=False)
+            else:
+                self.load_state_dict(checkpoint['state_dict'], strict=False)
+        elif 'MODEL_STATE' in checkpoint:
+            self.load_state_dict(checkpoint['MODEL_STATE'], strict=False)
+
+        # load RNG states each time the model and states are loaded from checkpoint
+        if 'rng' in self.config:
+            rng = self.config['rng']
+            for key, value in rng.items():
+                if key =='torch_state':
+                    torch.set_rng_state(value.cpu())
+                elif key =='cuda_state':
+                    torch.cuda.set_rng_state(value.cpu())
+                elif key =='numpy_state':
+                    np.random.set_state(value)
+                elif key =='python_state':
+                    random.setstate(value)
+                else:
+                    print('unrecognized state')
+
+    def _set_seed(self, value):
+        print('Random Seed:', value)
+        random.seed(value)
+        torch.manual_seed(value)
+        torch.cuda.manual_seed(value)
+        torch.cuda.manual_seed_all(value)
+        np.random.seed(value)
+        cudnn.deterministic = True
+        cudnn.benchmark = False
+
+    def forward(self, smiles, batch_size=100):
+        return self.decode(self.encode(smiles, batch_size=batch_size, return_torch=True))
+            
+    def tokenize(self, smiles):
+        """Tokenize a string into tokens."""
+        if isinstance(smiles, str):
+            batch = [smiles]
+        else:
+            batch = smiles
+        
+        tokens = self.tokenizer(
+            batch, 
+            padding=True,
+            truncation=True, 
+            add_special_tokens=True, 
+            return_tensors="pt",
+            max_length=self.max_len,
+        )
+        
+        idx = tokens['input_ids'].clone().detach()
+        mask = tokens['attention_mask'].clone().detach()
+
+        if self.is_cuda_available:
+            return idx.cuda(), mask.cuda()
+        
+        return idx, mask
+    
+    def extract_all(self, smiles):
+        """Extract all elements from each part of smi-ted. Be careful."""
+        # evaluation mode
+        self.encoder.eval()
+        self.decoder.eval()
+        if self.is_cuda_available:
+            self.encoder.cuda()
+            self.decoder.cuda()
+        
+        # tokenizer
+        idx, mask = self.tokenize(smiles)
+        
+        ###########
+        # Encoder #
+        ###########
+        # encoder forward
+        x = self.encoder.tok_emb(idx) # each index maps to a (learnable) vector
+        x = self.encoder.drop(x)
+        x = self.encoder.blocks(x, length_mask=LengthMask(mask.sum(-1)))
+
+        # mean pooling
+        token_embeddings = x
+        input_mask_expanded = mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+        sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+        sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+        true_set = sum_embeddings / sum_mask # DO NOT USE THIS FOR DOWNSTREAM TASKS, USE `pred_set` INSTEAD
+
+        # add padding
+        mask_embeddings = (token_embeddings * input_mask_expanded)
+        token_embeddings = F.pad(mask_embeddings, pad=(0, 0, 0, self.max_len - mask_embeddings.shape[1]), value=0)
+        idx = F.pad(idx, pad=(0, self.max_len - idx.shape[1], 0, 0), value=2)
+
+        true_ids = idx
+        true_cte = token_embeddings
+        true_cte = true_cte.view(-1, self.max_len*self.n_embd)
+
+        ###########
+        # Decoder #
+        ###########
+        # CTE autoencoder
+        pred_set = self.decoder.autoencoder.encoder(true_cte)
+        pred_cte = self.decoder.autoencoder.decoder(pred_set)
+
+        # reconstruct tokens
+        pred_ids = self.decoder.lang_model(pred_cte.view(-1, self.max_len, self.n_embd))
+        pred_ids = torch.argmax(pred_ids, axis=-1)
+        
+        return ((true_ids, pred_ids), # tokens
+               (true_cte, pred_cte),  # token embeddings
+               (true_set, pred_set))  # smiles embeddings
+
+    def extract_embeddings(self, smiles):
+        """Extract token and SMILES embeddings."""
+        # evaluation mode
+        self.encoder.eval()
+        if self.is_cuda_available:
+            self.encoder.cuda()
+        
+        # tokenizer
+        idx, mask = self.tokenize(smiles)
+        
+        # encoder forward
+        token_embeddings = self.encoder(idx, mask)
+
+        # aggregate token embeddings (similar to mean pooling)
+        # CAUTION: use the embeddings from the autoencoder.
+        smiles_embeddings = self.decoder.autoencoder.encoder(token_embeddings.view(-1, self.max_len*self.n_embd))
+
+        # add padding
+        idx = F.pad(idx, pad=(0, self.max_len - idx.shape[1], 0, 0), value=self.padding_idx)
+
+        return idx, token_embeddings, smiles_embeddings
+    
+    def encode(self, smiles, useCuda=False, batch_size=100, return_torch=False):
+        """Extract efficiently SMILES embeddings per batches."""
+        # TODO: remove useCuda argument
+
+        # handle single str or a list of str
+        smiles = pd.Series(smiles) if isinstance(smiles, str) else pd.Series(list(smiles))
+        n_split = smiles.shape[0] // batch_size if smiles.shape[0] >= batch_size else smiles.shape[0]
+        
+        # process in batches
+        embeddings = [
+            self.extract_embeddings(list(batch))[2].cpu().detach().numpy() 
+                for batch in tqdm(np.array_split(smiles, n_split))
+        ]
+        flat_list = [item for sublist in embeddings for item in sublist]
+
+        # clear GPU memory
+        if self.is_cuda_available:
+            torch.cuda.empty_cache()
+            gc.collect()
+
+        if return_torch:
+            return torch.tensor(np.array(flat_list))
+        return pd.DataFrame(flat_list)
+    
+    def decode(self, smiles_embeddings):
+        """Decode SMILES embeddings back to SMILES."""
+        # evaluation mode
+        self.decoder.eval()
+        if self.is_cuda_available:
+            self.decoder.cuda()
+
+        # reconstruct token embeddings
+        pred_token_embds = self.decoder.autoencoder.decoder(smiles_embeddings)
+
+        # reconstruct tokens
+        pred_idx = self.decoder.lang_model(pred_token_embds.view(-1, self.max_len, self.n_embd))
+        pred_idx = torch.argmax(pred_idx, axis=-1).cpu().detach().numpy()
+
+        # convert idx to tokens
+        pred_smiles = []
+        for i in range(pred_idx.shape[0]):
+            idx = pred_idx[i]
+            smiles = self.tokenizer.idx_to_smiles(self, idx)
+            smiles = smiles.replace('<bos>', '') # begin token
+            smiles = smiles.replace('<eos>', '') # end token
+            smiles = smiles.replace('<pad>', '') # pad token
+            pred_smiles.append(smiles)
+
+        # clear GPU memory
+        if self.is_cuda_available:
+            torch.cuda.empty_cache()
+            gc.collect()
+
+        return pred_smiles
+
+    def __str__(self):
+        return 'smi-ted-Light'
+    
+
+def load_smi_ted(folder="./smi_ted_light", 
+              ckpt_filename="smi-ted-Light_40.pt",
+              vocab_filename="bert_vocab_curated.txt"
+              ):
+    tokenizer = MolTranBertTokenizer(os.path.join(folder, vocab_filename))
+    model = Smi_ted(tokenizer)
+    repo_id = "ibm/materials.smi-ted"
+    filename = "smi-ted-Light_40.pt"
+    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    model.load_checkpoint(file_path)
+    model.eval()
+    print('Vocab size:', len(tokenizer.vocab))
+    print(f'[INFERENCE MODE - {str(model)}]')
+    return model
+
+
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diff --git a/requirements.txt b/requirements.txt
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+pip-tools
+huggingface_hub==0.25.2
+matplotlib==3.9.2
+numpy>=1.26.1, <2.0.0
+scipy>=1.12.0
+torch>=2.1.0
+torchinfo>=1.8.0
+torch-geometric>=2.3.1
+pandas>=1.5.3
+pyarrow>=14.0.1
+scikit-learn>=1.5.0
+xgboost==1.6.2
+rdkit>=2024.3.5
+networkx>=2.8
+datasets>=2.13.1
+evaluate>=0.4.0
+selfies>=2.1.0
+gradio==3.41.0
+transformers>=4.38
+requests>=2.32.2
+urllib3>=2.2.2
+aiohttp>=3.10.2
+zipp>=3.19.1
+umap-learn
+torch-optimizer
+tqdm>=4.66.4
+pandas==2.2.3