Toward Synthesizability-Aware Multi-Step Retrosynthetic Planning

摘要

Multi-step retrosynthetic planning aims to decompose target molecules into available starting materials by iteratively invoking single-step prediction models within a search algorithm. The success of retrosynthetic planning depends on the joint guidance of single-step reasoning and global search across steps. However, most existing frameworks make step-wise decisions based only on the current molecular state, without explicitly modeling synthesizability signals that reflect long-range reachability. In this work, we propose GuideRetro, a synthesizability-aware framework for multi-step retrosynthetic planning that integrates global synthesizability knowledge into step-wise retrosynthetic prediction. GuideRetro learns transferable knowledge from large-scale reaction networks by modeling the evolution of synthetic complexity along reaction pathways. During planning, a route-aware synthesis state modeling module combines the evolving retrosynthetic route with retrieved global signals to guide reactant generation at each step. Experiments on benchmark datasets show that GuideRetro achieves state-of-the-art performance. The integration of global synthesizability knowledge and route-aware modeling improves planning accuracy and search efficiency under realistic retrosynthetic settings. The code is available at: https://github.com/L-AJ/GuideRetro.