Retrosynthesis prediction aims to infer the reactant molecules based on a given product molecule, which is a fundamental task in chemical synthesis. However, existing methods rely on a static pattern-matching paradigm, which limits their ability to perform effective logical decision-making from chemical data, leading to a black-box process. We propose Retro-Expert, an interpretable retrosynthesis framework that performs collaborative reasoning by combining the complementary strengths of Large Language Models and specialized models via pure reinforcement learning. It outputs natural language explanations grounded in chemical logic through three components: (1) specialized models provide chemical knowledge that is distilled into a high-quality chemical decision space, (2) LLM-driven critical reasoning to generate predictions with an interpretable reasoning path, and (3) knowledge-grounded policy optimization refines the interpretable decision policy. Experiments show that Retro-Expert surpasses both LLM-based and specialized models across different metrics, while generating chemically grounded explanations that enhance chemists' trust in practice. The source code for this paper is available at https://github.com/MagixRab-ll/Retro-Expert.

翻译：[translated abstract in Chinese] 逆合成预测旨在根据给定的目标产物分子推断其反应物分子，这是化学合成领域的一项基础任务。然而，现有方法依赖于静态的模式匹配范式，难以从化学数据中进行高效的逻辑决策，从而导致过程不透明。我们提出Retro-Expert——一个可解释的逆合成框架，它通过纯强化学习方法，结合大型语言模型与专用模型的互补优势来实现协同推理。该框架通过三个组件输出基于化学逻辑的自然语言解释：（1）专用模型提供化学知识，并将其浓缩为高质量的化学决策空间；（2）大语言模型驱动的批判性推理，生成具有可解释推理路径的预测；（3）基于知识的策略优化，改进可解释的决策策略。实验结果表明，Retro-Expert在各项指标上均超越了基于大语言模型和专用模型的方法，同时生成基于化学逻辑的解释，增强了化学家在实际应用中的信任度。本文源代码可在 https://github.com/MagixRab-ll/Retro-Expert 获取。