Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
arxiv(2024)
摘要
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.
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