{"type":"ScholarlyArticle","title":"Toward Synthesizability-Aware Multi-Step Retrosynthetic Planning","authors":[{"name":"Yujie Chen","slug":"yujie-chen","given":"Yujie","family":"Chen","affiliation":""},{"name":"AJie Lin","slug":"ajie-lin","given":"AJie","family":"Lin","affiliation":""},{"name":"Tengfei Ma","slug":"tengfei-ma","given":"Tengfei","family":"Ma","affiliation":""},{"name":"Shu Wu","slug":"shu-wu","given":"Shu","family":"Wu","affiliation":""},{"name":"Leyi Wei","slug":"leyi-wei","given":"Leyi","family":"Wei","affiliation":""},{"name":"Yiping Liu","slug":"yiping-liu","given":"Yiping","family":"Liu","affiliation":""},{"name":"Xiangxiang Zeng","slug":"xiangxiang-zeng","given":"Xiangxiang","family":"Zeng","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2026/611","url":"https://ijcai.org/proceedings/2026/611","pdf":"https://ijcai.org/proceedings/2026/0611.pdf","markdown":"https://ijcai.org/proceedings/2026/611.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/611","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":611,"pages":{"first":5486,"last":5494},"keywords":["Multidisciplinary Topics and Applications: Other","Multidisciplinary Topics and Applications: Physical sciences"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}