Datasets:
version: 1.0.2
language: en
license: gpl-3.0
size_categories:
- 1M<n<10M
task_categories:
- tabular-regression
pretty_name: Molecule3D
tags:
- molecular geometry
- molecular graph
dataset_summary: >-
Curated dataset of ground-state geometries of 4 million molecules dervied from
density functional theory, consisting of SMILES, sdf, and 3D properties of
molecules. Random split and scaffold split datasets are uploaded to our
repository.
citation: >-
@misc{https://doi.org/10.48550/arxiv.2110.01717, doi =
{10.48550/ARXIV.2110.01717}, url = {https://arxiv.org/abs/2110.01717}, author
= {Xu, Zhao and Luo, Youzhi and Zhang, Xuan and Xu, Xinyi and Xie,
Yaochen and Liu, Meng and Dickerson, Kaleb and Deng, Cheng and Nakata,
Maho and Ji, Shuiwang}, keywords = {Machine Learning (cs.LG), Artificial
Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer
and information sciences}, title = {Molecule3D: A Benchmark for Predicting 3D
Geometries from Molecular Graphs}, publisher = {arXiv}, year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license} }
configs:
- config_name: Molecule3D_random_split
data_files:
- split: train
path: Molecule3D/Molecule3D_random_split/train-*
- split: test
path: Molecule3D/Molecule3D_random_split/test-*
- split: validation
path: Molecule3D/Molecule3D_random_split/validation-*
- config_name: Molecule3D_scaffold_split
data_files:
- split: train
path: Molecule3D/Molecule3D_scaffold_split/train-*
- split: test
path: Molecule3D/Molecule3D_scaffold_split/test-*
- split: validation
path: Molecule3D/Molecule3D_scaffold_split/validation-*
dataset_info:
- config_name: Molecule3D_random_split
features:
- name: index
dtype: int64
- name: SMILES
dtype: string
- name: sdf
dtype: string
- name: cid
dtype: int64
- name: dipole x
dtype: float64
- name: dipole y
dtype: float64
- name: dipole z
dtype: float64
- name: homo
dtype: float64
- name: lumo
dtype: float64
- name: 'Y'
dtype: float64
- name: scf energy
dtype: float64
splits:
- name: train
num_bytes: 3175820005
num_examples: 2339788
- name: test
num_bytes: 1058816993
num_examples: 779930
- name: validation
num_bytes: 1058522808
num_examples: 779929
download_size: 1881875022
dataset_size: 5293159806
- config_name: Molecule3D_scaffold_split
features:
- name: index
dtype: int64
- name: SMILES
dtype: string
- name: sdf
dtype: string
- name: cid
dtype: int64
- name: dipole x
dtype: float64
- name: dipole y
dtype: float64
- name: dipole z
dtype: float64
- name: homo
dtype: float64
- name: lumo
dtype: float64
- name: 'Y'
dtype: float64
- name: scf energy
dtype: float64
splits:
- name: train
num_bytes: 3066856853
num_examples: 2339788
- name: test
num_bytes: 1130636582
num_examples: 779930
- name: validation
num_bytes: 1095666371
num_examples: 779929
download_size: 1867778422
dataset_size: 5293159806
Molecule3D
Molecule3D is a comprehensive dataset containing ground-state geometries derived from Density Functional Theory (DFT) calculations for approximately 4 million molecules. This is a mirror of the Official Github repo where the dataset was uploaded in 2021.
Preprocseeing
We utilized the raw data uploaded on Github and performed several preprocessing:
- Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
- Combine the SMILES strings, SDF data, and 3D molecular properties for each molecule.
- Split the dataset using random split and scaffold split (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 Molecule3D 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 one of the Molecule3D datasets, e.g.,
>>> Molecule3D = datasets.load_dataset('maomlab/Molecule3D', name = 'Molecule3D_random_split') # can put 'Molecule3D_scaffold_split' for the name as well
README.md: 100% 4.95k/4.95k [00:00<00:00, 559kB/s]
Generating train split: 100% 2339788/2339788 [00:34<00:00, 85817.85 examples/s]
Generating test split: 100% 779930/779930 [00:15<00:00, 96660.33 examples/s]
Generating validation split: 100% 779929/779929 [00:09<00:00, 79064.99 examples/s]
and inspecting the dataset
>>> Molecule3D
DatasetDict({
train: Dataset({
features: ['index', 'SMILES', 'sdf', 'cid', 'dipole x', 'dipole y', 'dipole z', 'homo', 'lumo', 'Y', 'scf energy'],
num_rows: 2339788
})
test: Dataset({
features: ['index', 'SMILES', 'sdf', 'cid', 'dipole x', 'dipole y', 'dipole z', 'homo', 'lumo', 'Y', 'scf energy'],
num_rows: 779930
})
validation: Dataset({
features: ['index', 'SMILES', 'sdf', 'cid', 'dipole x', 'dipole y', 'dipole z', 'homo', 'lumo', 'Y', 'scf energy'],
num_rows: 779929
})
})
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_dataset = load_dataset('maomlab/Molecule3D', name = 'Molecule3D_random_split') # can put 'Molecule3D_scaffold_split' for the name as well
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
@misc{https://doi.org/10.48550/arxiv.2110.01717, doi = {10.48550/ARXIV.2110.01717}, url = {https://arxiv.org/abs/2110.01717}, author = {Xu, Zhao and Luo, Youzhi and Zhang, Xuan and Xu, Xinyi and Xie, Yaochen and Liu, Meng and Dickerson, Kaleb and Deng, Cheng and Nakata, Maho and Ji, Shuiwang}, keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs}, publisher = {arXiv}, year = {2021}, copyright = {arXiv.org perpetual, non-exclusive license} }