@inproceedings{
kim2024geneticguided,
title={Genetic-guided {GF}lowNets for Sample Efficient Molecular Optimization},
author={Hyeonah Kim and Minsu Kim and Sanghyeok Choi and Jinkyoo Park},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=B4q98aAZwt}
}
The challenge of discovering new molecules with desired properties is crucial in domains like drug discovery and material design. Recent advances in deep learning-based generative methods have shown promise but face the issue of sample efficiency due to the computational expense of evaluating the reward function. This paper proposes a novel algorithm for sample-efficient molecular optimization by distilling a powerful genetic algorithm into deep generative policy using GFlowNets training, the off-policy method for amortized inference. This approach enables the deep generative policy to learn from domain knowledge, which has been explicitly integrated into the genetic algorithm. Our method achieves state-of-the-art performance in the official molecular optimization benchmark, significantly outperforming previous methods. It also demonstrates effectiveness in designing inhibitors against SARS-CoV-2 with substantially fewer reward calls.
Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
Hyeonah Kim Minsu Kim Sanghyeok Choi Jinkyoo Park