用序列生成方法重构模板，实现高效准确的多步逆合成规划
TempRe: Template generation for single and direct multi-step retrosynthesis
- 将模板法转化为序列生成任务，提升可扩展性与化学合理性
- 在PaRoutes数据集上达到领先级多步逆合成路径准确率
- 支持直接生成多步合成路线，比传统方法更轻量高效
逆合成规划因化学反应空间庞大复杂仍是分子发现的核心挑战。传统模板法虽具可解释性，但扩展性差、泛化能力弱；无模板生成方法则易产生无效反应。本文提出TempRe，将模板法重新建模为序列生成问题，实现可扩展、灵活且化学合理的逆合成规划。在单步与多步逆合成任务上，其性能优于模板分类与SMILES生成方法。在PaRoutes多步基准测试中，TempRe取得优异的top-k路径准确率。进一步地，该框架可直接生成多步合成路径，提供一种轻量高效的替代方案，无需分步执行或搜索。结果表明，模板生成建模是辅助合成规划的强大新范式。
原文摘要 · Abstract (English)
Retrosynthesis planning remains a central challenge in molecular discovery due to the vast and complex chemical reaction space. While traditional template-based methods offer tractability, they suffer from poor scalability and limited generalization, and template-free generative approaches risk generating invalid reactions. In this work, we propose TempRe, a generative framework that reformulates template-based approaches as sequence generation, enabling scalable, flexible, and chemically plausible retrosynthesis. We evaluated TempRe across single-step and multi-step retrosynthesis tasks, demonstrating its superiority over both template classification and SMILES-based generation methods. On the PaRoutes multi-step benchmark, TempRe achieves strong top-k route accuracy. Furthermore, we extend TempRe to direct multi-step synthesis route generation, providing a lightweight and efficient alternative to conventional single-step and search-based approaches. These results highlight the potential of template generative modeling as a powerful paradigm in computer-aided synthesis planning.
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