Datasets:
version: 1.0.0
license: cc-by-sa-4.0
task_categories:
- tabular-classification
language:
- en
pretty_name: MolData
size_categories:
- 1M<n<10M
tags:
- drug discovery
- bioassay
dataset_summary: >-
A comprehensive disease and target-based dataset with roughly 170 million drug
screening results from 1.4 million unique molecules and 600 assays which are
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
description: 'Binary classification (0/1) '
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
- config_name: default
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-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/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)
- Formatting (from wide form to long form)
- 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 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", name = "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_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
@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 }