---
title: "Toward Synthesizability-Aware Multi-Step Retrosynthetic Planning"
authors:
  - "Yujie Chen"
  - "AJie Lin"
  - "Tengfei Ma"
  - "Shu Wu"
  - "Leyi Wei"
  - "Yiping Liu"
  - "Xiangxiang Zeng"
doi: "10.24963/ijcai.2026/611"
url: "https://ijcai.org/proceedings/2026/611"
pdf: "https://ijcai.org/proceedings/2026/0611.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "5486-5494"
track: "Main Track"
keywords:
  - "Multidisciplinary Topics and Applications: Other"
  - "Multidisciplinary Topics and Applications: Physical sciences"
---

# Toward Synthesizability-Aware Multi-Step Retrosynthetic Planning

Yujie Chen, AJie Lin, Tengfei Ma, Shu Wu, Leyi Wei, Yiping Liu, Xiangxiang Zeng

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 5486-5494. DOI: [10.24963/ijcai.2026/611](https://doi.org/10.24963/ijcai.2026/611)

## 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.