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}
}