RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets

• URL: http://arxiv.org/abs/2406.18739v1
• Date: Wed, 26 Jun 2024 20:10:03 GMT
• Title: RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
• Authors: Piotr Gaiński, Michał Koziarski, Krzysztof Maziarz, Marwin Segler, Jacek Tabor, Marek Śmieja,
• Abstract summary: Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule. We propose a novel model, RetroGFN, that can explore outside the limited dataset and return a diverse set of feasible reactions. We show that RetroGFN achieves competitive results on standard top-k accuracy while outperforming existing methods on round-trip accuracy.
• Score: 8.308430428140413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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