  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.
