Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
<|reference_start|>Genetic-guided GFlowNets for Sample Efficient Molecular Optimization: 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.<|reference_end|>
@article{kim2024genetic-guided,
    title={Genetic-guided GFlowNets for Sample Efficient Molecular Optimization},
    author={Hyeonah Kim, Minsu Kim, Sanghyeok Choi, Jinkyoo Park},
    journal={arXiv preprint arXiv:2402.05961},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05961},
    primaryClass={q-bio.BM cs.LG cs.NE}
}