Single-step retrosynthesis aims to predict a set of reactions that lead to
the creation of a target molecule, which is a crucial task in molecular
discovery. Although a target molecule can often be synthesized with multiple
different reactions, it is not clear how to verify the feasibility of a
reaction, because the available datasets cover only a tiny fraction of the
possible solutions. Consequently, the existing models are not encouraged to
explore the space of possible reactions sufficiently. In this paper, we propose
a novel single-step retrosynthesis model, RetroGFN, that can explore outside
the limited dataset and return a diverse set of feasible reactions by
leveraging a feasibility proxy model during the training. We show that RetroGFN
achieves competitive results on standard top-k accuracy while outperforming
existing methods on round-trip accuracy. Moreover, we provide empirical
arguments in favor of using round-trip accuracy which expands the notion of
feasibility with respect to the standard top-k accuracy metric.

该研究提出了一种新颖的单步逆向合成模型 RetroGFN，通过在训练过程中利用一个可行性代理模型，可以探索有限数据集之外的可能反应，并返回多样的可行反应集合。实验证明 RetroGFN 在标准前 k 名准确率上具有竞争力，并在往返准确率方面优于现有方法。此外，提供了使用往返准确率的经验性论据，扩展了与标准前 k 名准确率指标相关的可行性概念。