RETRO SYNFLOW: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesis

• URL: http://arxiv.org/abs/2506.04439v1
• Date: Wed, 04 Jun 2025 20:46:05 GMT
• Title: RETRO SYNFLOW: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesis
• Authors: Robin Yadav, Qi Yan, Guy Wolf, Avishek Joey Bose, Renjie Liao,
• Abstract summary: We model single-step retrosynthesis planning and introduce RETRO SYNFLOW (RSF) a discrete flow-matching framework.<n>We employ Feynman-Kac steering with Sequential Monte Carlo based resampling to steer promising generations at inference.
• Score: 23.422202032748924
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: A fundamental problem in organic chemistry is identifying and predicting the series of reactions that synthesize a desired target product molecule. Due to the combinatorial nature of the chemical search space, single-step reactant prediction -- i.e. single-step retrosynthesis -- remains challenging even for existing state-of-the-art template-free generative approaches to produce an accurate yet diverse set of feasible reactions. In this paper, we model single-step retrosynthesis planning and introduce RETRO SYNFLOW (RSF) a discrete flow-matching framework that builds a Markov bridge between the prescribed target product molecule and the reactant molecule. In contrast to past approaches, RSF employs a reaction center identification step to produce intermediate structures known as synthons as a more informative source distribution for the discrete flow. To further enhance diversity and feasibility of generated samples, we employ Feynman-Kac steering with Sequential Monte Carlo based resampling to steer promising generations at inference using a new reward oracle that relies on a forward-synthesis model. Empirically, we demonstrate \nameshort achieves $60.0 \%$ top-1 accuracy, which outperforms the previous SOTA by $20 \%$. We also substantiate the benefits of steering at inference and demonstrate that FK-steering improves top-$5$ round-trip accuracy by $19 \%$ over prior template-free SOTA methods, all while preserving competitive top-$k$ accuracy results.

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