Retro-R1: LLM-based Agentic Retrosynthesis
Wei Liu, Jiangtao Feng, Hongli Yu, Yuxuan Song, Yuqiang Li, Shufei Zhang, LEI BAI, Wei-Ying Ma, Hao Zhou
Multi-step retrosynthesis planning is paramount in chemical discovery, and is extremely accelerated by advanced artificial intelligence~(AI) methods. Recent research on Large Language Models (LLMs) has demonstrated potent reasoning and planning capabilities with reinforcement learning techniques, especially math and code problem solving. A natural question is whether an LLM can learn a good policy on multi-step retrosynthesis planning. In this study, we introduce  Retro-R1, an innovative LLM-based retrosynthesis agent learned with reinforcement learning, to design molecular synthesis pathways. Unlike previous reasoning tasks, e.g., math and code, trained purely in a single-turn question-answering style,  Retro-R1 is designed in an agent-action way and is trained with single-step retrosynthesis tools, which are plug-in. The experimental results indicate that  Retro-R1 does boost the multi-step retrosynthesis policy with 55.79\% pass@1 success rate, surpassing the previous state-of-the-art method by 8.95\%.  Retro-R1 exhibits impressive out-of-domain generalization performance, while previous methods lag behind despite their high performance in the in-domain testset. We hope this work would signify a crucial step toward imbuing LLMs with advanced, chemist-like reasoning, underscoring RL's potential to unlock data-efficient, generalizable, and sophisticated problem-solving in LLM agents for scientific discovery.