Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908462269595648 |
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| author | Zhang, Situo Li, Hanqi Chen, Lu Zhao, Zihan Lin, Xuanze Zhu, Zichen Chen, Bo Chen, Xin Yu, Kai |
| author_facet | Zhang, Situo Li, Hanqi Chen, Lu Zhao, Zihan Lin, Xuanze Zhu, Zichen Chen, Bo Chen, Xin Yu, Kai |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_17448 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning Zhang, Situo Li, Hanqi Chen, Lu Zhao, Zihan Lin, Xuanze Zhu, Zichen Chen, Bo Chen, Xin Yu, Kai Computational Engineering, Finance, and Science Artificial Intelligence Chemical Physics 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. |
| title | Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Chemical Physics |
| url | https://arxiv.org/abs/2507.17448 |