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https://github.com/MinkaiXu/ConfVAE-ICML21
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming (ICML'21)
https://github.com/MinkaiXu/ConfVAE-ICML21
bilevel-optimization computational-biology computational-chemistry conformation generative-models graph-neural-networks icml-2021 molecule pytorch vae
Last synced: 24 days ago
JSON representation
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming (ICML'21)
- Host: GitHub
- URL: https://github.com/MinkaiXu/ConfVAE-ICML21
- Owner: MinkaiXu
- Created: 2021-05-28T09:41:46.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-08-03T14:17:42.000Z (over 3 years ago)
- Last Synced: 2024-11-21T23:27:07.634Z (30 days ago)
- Topics: bilevel-optimization, computational-biology, computational-chemistry, conformation, generative-models, graph-neural-networks, icml-2021, molecule, pytorch, vae
- Language: Python
- Homepage:
- Size: 381 KB
- Stars: 51
- Watchers: 3
- Forks: 17
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- StarryDivineSky - MinkaiXu/ConfVAE-ICML21
README
# ConfVAE for Conformation Generation
[[arXiv](https://arxiv.org/abs/2105.07246)] [[Code](https://github.com/MinkaiXu/ConfVAE-ICML21)]
This is the official code repository of our ICML paper "An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming" (2021).
## Installation
### Install via Conda (Recommended)
You can follow the instructions [here](https://github.com/MinkaiXu/CGCF-ConfGen#install-via-conda-recommended). We adopt an environment the same as our another previous project.
### Install Manually
```bash
# Create conda environment
conda create --name ConfVAE python=3.7
# Activate the environment
conda activate ConfVAE
# Install packages
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
conda install rdkit==2020.03.3 -c rdkit
conda install tqdm networkx scipy scikit-learn h5py tensorboard -c conda-forge
pip install torchdiffeq==0.0.1
# Install PyTorch Geometric
conda install pytorch-geometric -c rusty1s -c conda-forge
```
## Data
### Official Datasets
The official datasets are available [here](https://drive.google.com/drive/folders/1UTxgyWeXbgDehV1okdZ2UkKwZbe3I0hO?usp=sharing).
### Input Format / Make Your Own Datasets
The dataset file is a pickled Python list consisting of [``rdkit.Chem.rdchem.Mol``](https://www.rdkit.org/docs/source/rdkit.Chem.rdchem.html#rdkit.Chem.rdchem.Mol) objects. Each conformation is stored individually as a `Mol` object. For example, if a dataset contains 3 molecules, where the first molecule has 4 conformations, the second one and the third one have 5 and 6 conformations respectively, then the pickled Python list will contain 4+5+6 `Mol` objects in total.
### Output Format
The output format is identical to the input format.
## Usage
### Train
Example: training a model for QM9 molecules.
```bash
python train_vae.py \
--train_dataset ./data/qm9/train_QM9.pkl \
--val_dataset ./data/qm9/val_QM9.pkl
```
More training options can be found in `train_vae.py`.
### Generate Conformations
Example: generating conformations for each molecule in the QM9 test-split, with twice the number of test set for each molecule.
```bash
python eval_vae.py \
--ckpt ./logs/VAE_QM9 \
--dataset ./data/iclr/qm9/test_QM9.pkl \
--num_samples -2
```
More generation options can be found in `eval_vae.py`.
## Citation
Please consider citing our work if you find it helpful.
```
@inproceedings{
xu2021end,
title={An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming},
author={Xu, Minkai and Wang, Wujie and Luo, Shitong and Shi, Chence and Bengio, Yoshua and Gomez-Bombarelli, Rafael and Tang, Jian},
booktitle={International Conference on Machine Learning},
year={2021}
}
```
## Contact
If you have any question, please contact me at or .
-----
# 📢 Attention
Please also check our another concurrent work on molecular conformation generation, which has also been accepted in ICML'2021 (Long Talk): [Learning Gradient Fields for Molecular Conformation Generation](https://arxiv.org/abs/2105.03902). [[Code](https://github.com/DeepGraphLearning/ConfGF)]
