标题： 基于强化学习的大型语言模型推理驱动的逆合成预测 显示英文标题

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

摘要： 逆合成规划在有机合成和药物发现中至关重要，近年来受益于人工智能驱动的进展。 然而，现有方法在适用性和可解释性方面经常面临限制。 基于图的传统模型和序列到序列模型通常缺乏通用的化学知识，导致预测结果既不一致准确，也难以解释。 为了解决这些挑战，我们引入了RetroDFM-R，这是一种专门用于化学逆合成的基于推理的大语言模型（LLM）。 利用由化学可验证奖励引导的大规模强化学习，RetroDFM-R显著提高了预测准确性和可解释性。 全面评估表明，RetroDFM-R显著优于最先进方法，在USPTO-50K基准测试中达到了65.0%的top-1准确率。 双盲人类评估进一步验证了RetroDFM-R预测的化学合理性与实际应用价值。 RetroDFM-R还能准确预测文献中报道的现实世界药物分子和钙钛矿材料的多步逆合成路线。 关键的是，该模型的显式推理过程提供了人类可解释的见解，从而增强了实际逆合成应用中的信任度和实用价值。 显示英文摘要

摘要： 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.