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https://github.com/minkaixu/cgcf-confgen
:test_tube: Learning Neural Generative Dynamics for Molecular Conformation Generation (ICLR 2021)
https://github.com/minkaixu/cgcf-confgen
conformation conformation-generation conformer iclr iclr2021 molecule pytorch
Last synced: about 1 month ago
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:test_tube: Learning Neural Generative Dynamics for Molecular Conformation Generation (ICLR 2021)
- Host: GitHub
- URL: https://github.com/minkaixu/cgcf-confgen
- Owner: MinkaiXu
- Created: 2021-02-04T09:49:43.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2021-08-03T14:16:51.000Z (over 3 years ago)
- Last Synced: 2024-09-22T13:32:42.460Z (about 2 months ago)
- Topics: conformation, conformation-generation, conformer, iclr, iclr2021, molecule, pytorch
- Language: Python
- Homepage:
- Size: 1.43 MB
- Stars: 44
- Watchers: 4
- Forks: 16
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# 📢 News
**[NEWS]** Please check our latest work on molecular conformation generation, which has 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)]
-----
# CGCF for Conformation Generation
[[OpenReview](https://openreview.net/forum?id=pAbm1qfheGk)] [[arXiv](https://arxiv.org/abs/2102.10240)] [[Code](https://github.com/MinkaiXu/CGCF-ConfGen)]
This is the official code repository of our ICLR paper "Learning Neural Generative Dynamics for Molecular Conformation Generation" (2021).
## Installation
### Install via Conda (Recommended)
Step 1: Create a conda environment named `CGCF` from `env.yml` :
```bash
conda env create --file env.yml
```
Step 2: Install PyTorch Geometric :
```bash
conda activate CGCF
./install_torch_geometric.sh
```
### Install Manually
```bash
# Create conda environment
conda create --name CGCF python=3.7
# Activate the environment
conda activate CGCF
# Install packages
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -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
pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html
pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html
pip install --no-index torch-cluster -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html
pip install --no-index torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html
pip install torch-geometric
```
## 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
### Generate Conformations
Example: generating 50 conformations for each molecule in the QM9 test-split with the pre-trained model.
```bash
python generate.py --ckpt ./pretrained/ckpt_qm9.pt --dataset ./data/qm9/test.pkl --num_samples 50 --out ./generated.pkl
```
More generation options can be found in `generate.py`.
### Train
Example: training a model for QM9 molecules.
```bash
python train.py --train_dataset ./data/qm9/train.pkl --val_dataset ./data/qm9/val.pkl
```
More training options can be found in `train.py`.
## Citation
Please consider citing our work if you find it helpful.
```
@inproceedings{
xu2021learning,
title={Learning Neural Generative Dynamics for Molecular Conformation Generation},
author={Minkai Xu* and Shitong Luo* and Yoshua Bengio and Jian Peng and Jian Tang},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=pAbm1qfheGk}
}
```
## Contact
If you have any question, please contact me at or .
## Updates
- Feb 4, 2021. Code is coming soon.
- Feb 20, 2021. Code is released.