---
version: 1.0.0
language: en
license: cc-by-4.0
source_datasets: curated
task_categories:
- tabular-classification
- tabular-regression
tags:
- chemistry
- cheminformatics
pretty_name: Mutagenicity Optimization
dataset_summary: >-
  The mutagenicity dataset consists of a training set with 6862 molecules and a test set with 1714 molecules. 
  The train and test datasets were created after sanitizing and splitting the original dataset in the paper below.
  Additionally, there is a validation dataset containing 1469 molecules that were not involved in the training set.
citation: >-
  @article {,
     author  = {Lou, C., Yang, H., Deng, H. et al.},
     title   = {Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods},
     journal = {Journal of Cheminformatics},
     year    = {2023},
     volume  = {15},
     number  = {35}
  }
size_categories:
- 10K<n<100K
config_names:
- train_test
- validation
configs:   
- config_name: train_test
  data_files:
    - split: train
      path: train_test/train.csv
    - split: test
      path: train_test/test.csv 
- config_name: validation
  data_files:
    - split: validation
      path: validation/validation.csv
dataset_info:
- config_name: train_test
  features:
  - name: SMILES
    dtype: string
  - name: ID
    dtype: int64
  - name: 'Y'
    dtype: int64
    description: >-
      Binary classification where 'O' represents 'non-mutagenic' and '1' represents 'mutagenic'
  - name: MW
    dtype: float64
  splits:
    - name: train
      num_bytes: 219712
      num_examples: 6862
    - name: test
      num_bytes: 54976
      num_examples: 1714
- config_name: validation
  features:
  - name: SMILES
    dtype: string
  - name: 'Y'
    dtype: int64
    description: >-
      Binary classification where 'O' represents 'non-mutagenic' and '1' represents 'mutagenic'
---

# Mutagenicity Optimization (MutagenLou2023)

In the original paper, they collected the Ames records from Hansen’s benchmark (6512 compounds) and the ISSSTY database (6052 compounds). 
After data preparation, a total of 8576 compounds with structural diversity were obtained, including 4643 Ames positives and 3933 Ames negatives. 
The comprehensive data set was then split into a training set including 7720 compounds and a test set containing 856 compounds. 
Overall, the numbers of negatives and positives in this data set were balanced with a ratio of 0.847 (Neg./Pos.). 
In addition, 805 approved drugs from DrugBank that were not involved in the training set, and 664 Ames strong positive samples 
from DGM/NIHS were built as an external validation set.

This is a mirror of the [official Github repo](https://github.com/Louchaofeng/Ames-mutagenicity-optimization) where the dataset was uploaded in 2023.


## Data splits
Here we have used the Realistic Split method described in [(Martin et al., 2018)](https://doi.org/10.1021/acs.jcim.7b00166) to split the MutagenLou2023 dataset.

## Quickstart Usage

### Load a dataset in python
Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
First, from the command line install the `datasets` library

    $ pip install datasets

then, from within python load the datasets library

    >>> import datasets
   
and load one of the `MutagenLou2023` datasets, e.g.,

    >>> train_test = datasets.load_dataset("maomlab/MutagenLou2023", name = "train_test")
    Downloading readme: 100%|██████████|  7.15k/7.15k [00:00<00:00, 182kB/s]
    Downloading data: 100%|██████████| 306k/306k [00:00<00:00, 4.45MkB/s]
    Downloading data: 100%|██████████| 115k/115k [00:00<00:00, 668kkB/s]
    Generating train split: 100%|██████████| 6862/6862 [00:00<00:00, 70528.05examples/s]
    Generating test split: 100%|██████████| 1714/1714 [00:00<00:00, 22632.33 examples/s]

and inspecting the loaded dataset

    >>> train_test
    DatasetDict({
    train: Dataset({
        features: ['SMILES', 'ID', 'Y', 'MW'],
        num_rows: 6862
    })
    test: Dataset({
        features: ['SMILES', 'ID', 'Y', 'MW'],
        num_rows: 1714
    })
    })
    


### Use a dataset to train a model
One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support

    pip install 'molflux[catboost,rdkit]'

then load, featurize, split, fit, and evaluate the a catboost model

    import json
    from datasets import load_dataset
    from molflux.datasets import featurise_dataset
    from molflux.features import load_from_dicts as load_representations_from_dicts
    from molflux.splits import load_from_dict as load_split_from_dict
    from molflux.modelzoo import load_from_dict as load_model_from_dict
    from molflux.metrics import load_suite
    
    split_dataset = load_dataset('maomlab/MutagenLou2023', name = 'train_test')
    
    split_featurised_dataset = featurise_dataset(
      split_dataset,
      column = "SMILES",
      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))

    model = load_model_from_dict({
        "name": "cat_boost_classifier",
        "config": {
            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
            "y_features": ['Y']}})
    
    model.train(split_featurised_dataset["train"])
    
    preds = model.predict(split_featurised_dataset["test"])
    
    classification_suite = load_suite("classification")
    
    scores = classification_suite.compute(
        references=split_featurised_dataset["test"]['Y'],
        predictions=preds["cat_boost_classifier::Y"])

### Citation

TY  - JOUR
AU  - Lou, Chaofeng
AU  - Yang, Hongbin
AU  - Deng, Hua
AU  - Huang, Mengting
AU  - Li, Weihua
AU  - Liu, Guixia
AU  - Lee, Philip W.
AU  - Tang, Yun
PY  - 2023
DA  - 2023/03/20
TI  - Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods
JO  - Journal of Cheminformatics
SP  - 35
VL  - 15
IS  - 1
AB  - Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. 
Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. 
However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data.
Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. 
A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. 
The model was used to assess the generalizability and validity of these mutagenicity transformation rules. 
The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. 
We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. 
To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 (http://lmmd.ecust.edu.cn/admetsar2/admetopt2/), 
a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints.
SN  - 1758-2946
UR  - https://doi.org/10.1186/s13321-023-00707-x
DO  - 10.1186/s13321-023-00707-x
ID  - Lou2023
ER  - 

---