---
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](https://arxiv.org/abs/2110.01717) 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](https://github.com/divelab/MoleculeX/tree/molx/Molecule3D) where the dataset was uploaded in 2021.


## Preprocseeing
We utilized the raw data uploaded on [Github](https://github.com/divelab/MoleculeX/tree/molx/Molecule3D/data/raw) and performed several preprocessing:
1. Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
2. Combine the SMILES strings, SDF data, and 3D molecular properties for each molecule.
3. 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](https://huggingface.co/datasets/maomlab/Molecule3D/blob/main/Molecule3D_preprocessing.py) file located in our Molecule3D repository.



## 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 `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](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 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}
}