Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis
This addresses the need for interpretable AI predictions in chemical synthesis, offering actionable insights for chemists, though it appears incremental by building on existing models.
The paper tackled the problem of black-box decision-making in retrosynthesis prediction by proposing Retro-Expert, an interpretable framework that combines Large Language Models and specialized models via reinforcement learning, resulting in performance surpassing existing models across metrics and providing expert-aligned explanations.
Retrosynthesis prediction aims to infer the reactant molecule based on a given product molecule, which is a fundamental task in chemical synthesis. However, existing models rely on static pattern-matching paradigm, which limits their ability to perform effective logic decision-making, leading to black-box decision-making. Building on this, we propose Retro-Expert, an interpretable retrosynthesis framework that performs collaborative reasoning by combining the complementary reasoning strengths of Large Language Models and specialized models via reinforcement learning. It outputs natural language explanations grounded in chemical logic through three components: (1) specialized models perform shallow reasoning to construct high-quality chemical decision space, (2) LLM-driven critical reasoning to generate predictions and corresponding interpretable reasoning path, and (3) reinforcement learning optimizing interpretable decision policy. Experiments show that Retro-Expert not only surpasses both LLM-based and specialized models across different metrics but also provides expert-aligned explanations that bridge the gap between AI predictions and actionable chemical insights.