Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Official implementation of NeurIPS'23 paper "Sample-efficient Multi-objective Molecular Optimization with GFlowNets". This code is built on top of the GFlowNet repo.
Environment:
- torch
- numpy
- scipy
- tqdm
- pymoo
- botorch
- gpytorch
- pandas
- rdkit
- torch-geometric
- h5py
- ray
- scikit-learn
- tensorboard
Experiments:
Single-round synthetic scenario
python main.py --condition_type HN --enable_tensorboard
Multi-objective Bayesian Optimization
python main_mobo.py --objectives gsk3b,jnk3,qed,sa --alpha_vector 3,3,1,1 --save --enable_tensorboard
Citation
If you find this repository useful, please consider citing our work:
@inproceedings{
zhu2023sampleefficient,
title={Sample-efficient Multi-objective Molecular Optimization with {GF}lowNets},
author={Yiheng Zhu and Jialu Wu and Chaowen Hu and Jiahuan Yan and Chang-Yu Hsieh and Tingjun Hou and Jian Wu},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=uoG1fLIK2s}
}