File size: 4,270 Bytes
febd0c4 aaf34ce febd0c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
import os
import pandas as pd
import torch
from torch.nn import functional as F
from transformers import AutoTokenizer
from util.utils import *
from rdkit import Chem
from tqdm import tqdm
from train import markerModel
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '0 '
device_count = torch.cuda.device_count()
device_biomarker = torch.device('cuda' if torch.cuda.is_available() else "cpu")
device = torch.device('cpu')
a_model_name = 'DeepChem/ChemBERTa-10M-MLM'
d_model_name = 'DeepChem/ChemBERTa-10M-MTR'
tokenizer = AutoTokenizer.from_pretrained(a_model_name)
d_tokenizer = AutoTokenizer.from_pretrained(d_model_name)
#--biomarker Model
##-- hyper param config file Load --##
config = load_hparams('config/predict.json')
config = DictX(config)
model = markerModel(config.d_model_name, config.p_model_name,
config.lr, config.dropout, config.layer_features, config.loss_fn, config.layer_limit, config.pretrained['chem'], config.pretrained['prot'])
model = markerModel.load_from_checkpoint(config.load_checkpoint,strict=False)
model.eval()
model.freeze()
if device_biomarker.type == 'cuda':
model = torch.nn.DataParallel(model)
def get_marker(drug_inputs, prot_inputs):
output_preds = model(drug_inputs, prot_inputs)
predict = torch.squeeze( (output_preds)).tolist()
# output_preds = torch.relu(output_preds)
# predict = torch.tanh(output_preds)
# predict = predict.squeeze(dim=1).tolist()
return predict
def marker_prediction(smiles, aas):
try:
aas_input = []
for ass_data in aas:
aas_input.append(' '.join(list(ass_data)))
a_inputs = tokenizer(smiles, padding='max_length', max_length=510, truncation=True, return_tensors="pt")
# d_inputs = tokenizer(smiles, truncation=True, return_tensors="pt")
a_input_ids = a_inputs['input_ids'].to(device)
a_attention_mask = a_inputs['attention_mask'].to(device)
a_inputs = {'input_ids': a_input_ids, 'attention_mask': a_attention_mask}
d_inputs = d_tokenizer(aas_input, padding='max_length', max_length=510, truncation=True, return_tensors="pt")
# p_inputs = prot_tokenizer(aas_input, truncation=True, return_tensors="pt")
d_input_ids = d_inputs['input_ids'].to(device)
d_attention_mask = d_inputs['attention_mask'].to(device)
d_inputs = {'input_ids': d_input_ids, 'attention_mask': d_attention_mask}
output_list = get_marker(a_inputs, d_inputs)
return output_list
except Exception as e:
print(e)
return {'Error_message': e}
def smiles_aas_test(smile_acc,smile_don):
mola = Chem.MolFromSmiles(smile_acc)
smile_acc = Chem.MolToSmiles(mola, canonical=True)
mold = Chem.MolFromSmiles(smile_don)
smile_don = Chem.MolToSmiles(mold, canonical=True)
batch_size = 1
datas = []
marker_list = []
marker_datas = []
marker_datas.append([smile_acc,smile_don])
if len(marker_datas) == batch_size:
marker_list.append(list(marker_datas))
marker_datas.clear()
if len(marker_datas) != 0:
marker_list.append(list(marker_datas))
marker_datas.clear()
for marker_datas in tqdm(marker_list, total=len(marker_list)):
smiles_d , smiles_a = zip(*marker_datas)
output_pred = marker_prediction(list(smiles_d), list(smiles_a) )
if len(datas) == 0:
datas = output_pred
else:
datas = datas + output_pred
# ## -- Export result data to csv -- ##
# df = pd.DataFrame(datas)
# df.to_csv('./results/predictData_nontonon_bindingdb_test.csv', index=None)
# print(df)
return '{:.2f}'.format(float(datas))
if __name__ == '__main__':
a = smiles_aas_test('CCCCCCCCCCCC1=C(/C=C2\C(=O)C3=C(C=C(F)C(F)=C3)C2=C(C#N)C#N)SC2=C1SC1=C2N(CC(CC)CCCC)C2=C1C1=NSN=C1C1=C2N(CC(CC)CCCC)C2=C1SC1=C2SC(/C=C2\C(=O)C3=C(C=C(F)C(F)=C3)C2=C(C#N)C#N)=C1CCCCCCCCCCC','CCCCCCC(CCCC)CC1=C(C)SC(C2=CC3=C(S2)C2=C(C=C(C4=CC(CC(CCCC)CCCCCC)=C(C5=CC6=C(C7=CC=C(CC(CC)CCCC)S7)C7=C(C=C(C)S7)C(C7=CC=C(CC(CC)CCCC)S7)=C6S5)S4)S2)C2=NSN=C23)=C1')
|