Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis

TL;DR

Retro-Expert introduces an interpretable, collaborative reasoning framework combining large language models and specialized models for more accurate and explainable retrosynthesis predictions grounded in chemical logic.

Contribution

It presents a novel collaborative reasoning approach using reinforcement learning to enhance interpretability and accuracy in retrosynthesis prediction.

Findings

Outperforms existing models on multiple metrics

Provides chemically grounded explanations

Bridges AI predictions with chemical insights

Abstract

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 analyze the product 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.