Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis
Researchers have developed Retro-Expert, a new framework for retrosynthesis prediction that combines large language models (LLMs) with specialized models through reinforcement learning. This approach aims to overcome the limitations of static pattern-matching methods by enabling collaborative reasoning and providing interpretable, chemically grounded explanations. Experiments indicate that Retro-Expert outperforms existing methods and enhances trust among chemists by offering a clear reasoning path for its predictions. AI
IMPACT Enhances interpretability and trust in AI for chemical synthesis, potentially accelerating drug discovery and materials science.