Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning

• URL: http://arxiv.org/abs/2507.17448v1
• Date: Wed, 23 Jul 2025 12:13:06 GMT
• Title: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
• Authors: Situo Zhang, Hanqi Li, Lu Chen, Zihan Zhao, Xuanze Lin, Zichen Zhu, Bo Chen, Xin Chen, Kai Yu,
• Abstract summary: RetroDFM-R is a reasoning-based large language model (LLM) designed specifically for chemical retrosynthesis.<n>It significantly enhances prediction accuracy and explainability, achieving a top-1 accuracy of 65.0% on the USPTO-50K benchmark.<n>It also accurately predicts multistep retrosynthetic routes reported in the literature for both real-world drug molecules and perovskite materials.
• Score: 16.284576756413184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrosynthesis planning, essential in organic synthesis and drug discovery, has greatly benefited from recent AI-driven advancements. Nevertheless, existing methods frequently face limitations in both applicability and explainability. Traditional graph-based and sequence-to-sequence models often lack generalized chemical knowledge, leading to predictions that are neither consistently accurate nor easily explainable. To address these challenges, we introduce RetroDFM-R, a reasoning-based large language model (LLM) designed specifically for chemical retrosynthesis. Leveraging large-scale reinforcement learning guided by chemically verifiable rewards, RetroDFM-R significantly enhances prediction accuracy and explainability. Comprehensive evaluations demonstrate that RetroDFM-R significantly outperforms state-of-the-art methods, achieving a top-1 accuracy of 65.0% on the USPTO-50K benchmark. Double-blind human assessments further validate the chemical plausibility and practical utility of RetroDFM-R's predictions. RetroDFM-R also accurately predicts multistep retrosynthetic routes reported in the literature for both real-world drug molecules and perovskite materials. Crucially, the model's explicit reasoning process provides human-interpretable insights, thereby enhancing trust and practical value in real-world retrosynthesis applications.

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