Datasets:

maomlab
/

MolData

version: 1.0.0
license: cc-by-sa-4.0
task_categories:
  - tabular-regression
language:
  - en
pretty_name: MolData
size_categories:
  - 1M<n<10M
tags:
  - drug discovery
  - bioassay
dataset_summary: >-
  A comprehensive disease and target-based dataset with 1.4 million molecules
  and 600 assays, collected from PubChem to accelerate molecular machine
  learning for better drug discovery.
citation: |-
  @article{KeshavarziArshadi2022,
   title = {MolData,  a molecular benchmark for disease and target based machine learning},
   volume = {14},
   ISSN = {1758-2946},
   url = {http://dx.doi.org/10.1186/s13321-022-00590-y},
   DOI = {10.1186/s13321-022-00590-y},
   number = {1},
   journal = {Journal of Cheminformatics},
   publisher = {Springer Science and Business Media LLC},
   author = {Keshavarzi Arshadi,  Arash and Salem,  Milad and Firouzbakht,  Arash and Yuan,  Jiann Shiun},
   year = {2022},
   month = mar 
  }
dataset_info:
  config_name: MolData
  features:
    - name: SMILES
      dtype: string
    - name: PUBCHEM_CID
      dtype: int64
    - name: split
      dtype: string
    - name: AID
      dtype: string
    - name: 'Y'
      dtype: int64
  splits:
    - name: train
      num_bytes: 12634275804
      num_examples: 138547273
    - name: test
      num_bytes: 1578698654
      num_examples: 17069726
    - name: validation
      num_bytes: 1254512486
      num_examples: 12728449
  download_size: 5293486933
  dataset_size: 15467486944
configs:
  - config_name: MolData
    data_files:
      - split: train
        path: MolData/train-*
      - split: test
        path: MolData/test-*
      - split: validation
        path: MolData/validation-*

MolData

MolData is a comprehensive disease and target-based dataset collected from PubChem. The dataset contains 1.4 million unique molecules, and it is one the largest efforts to date for democratizing the molecular machine learning. This is a mirror of the Official Github repo where the dataset was uploaded in 2021.

Preprocessing

We utilized the raw data uploaded on Github and performed several preprocessing:

Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
Rename the columns
Split the dataset (train, test, validation)

If you would like to try these processes with the original dataset, please follow the instructions in the Preprocessing Script.py file located in our MolData repository.

Quickstart Usage

Load a dataset in python

Each subset can be loaded into python using the Huggingface datasets 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 the MolData datasets, e.g.,

>>> MolData = datasets.load_dataset("maomlab/MolData")
    Generating train split: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 138547273/138547273 [02:07<00:00, 1088043.12 examples/s]
    Generating test split: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 17069726/17069726 [00:16<00:00, 1037407.67 examples/s]
    Generating validation split: 100%|████████████████████████████████████████████████████████████████████████████████████████| 12728449/12728449 [00:11<00:00, 1093675.24 examples/s]

and inspecting the loaded dataset

>>> MolData
DatasetDict({
train: Dataset({
    features: ['SMILES', 'PUBCHEM_CID', 'split', 'AID', 'Y'],
    num_rows: 138547273
})
test: Dataset({
    features: ['SMILES', 'PUBCHEM_CID', 'split', 'AID', 'Y'],
    num_rows: 17069726
})
validation: Dataset({
    features: ['SMILES', 'PUBCHEM_CID', 'split', 'AID', 'Y'],
    num_rows: 12728449
})

})

Use a dataset to train a model

One way to use the dataset is through the 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 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 and evaluate the catboost model

split_dataset = load_dataset('maomlab/MolData', name = 'MolData')

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_regressor",
    "config": {
        "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
        "y_features": ['Y']}})

model.train(split_featurised_dataset["train"])
preds = model.predict(split_featurised_dataset["test"])

regression_suite = load_suite("regression")

scores = regression_suite.compute(
    references=split_featurised_dataset["test"]['Y'],
    predictions=preds["cat_boost_regressor::Y"])

Citation

@article{KeshavarziArshadi2022, title = {MolData, a molecular benchmark for disease and target based machine learning}, volume = {14}, ISSN = {1758-2946}, url = {http://dx.doi.org/10.1186/s13321-022-00590-y}, DOI = {10.1186/s13321-022-00590-y}, number = {1}, journal = {Journal of Cheminformatics}, publisher = {Springer Science and Business Media LLC}, author = {Keshavarzi Arshadi, Arash and Salem, Milad and Firouzbakht, Arash and Yuan, Jiann Shiun}, year = {2022}, month = mar }