arXiv cs.AI· Jiaxing Sun, Xuli Shen, Jiang Wu, Ruiyi Ding, Ruikang Zhang, Xiao An, Xibao Li, Yubin Wang, Xingjian Wei, Yinfan Wang, Yu Liu, Lingli Ge, Xi Xue, Xiaojian Wang, Lijun Wu, Chang-Yu Hsieh, Yuqiang Li, Bowen Zhou, Dahua Lin, Conghui He·· 2 小时前AI 评分48
反应先例让逆合成预测突破训练覆盖范围
Reaction precedents extend retrosynthetic prediction beyond training coverage
AI 导读
一项基于 271 万个反应的研究表明，推理时检索反应先例无需更新模型参数即可弥补逆合成预测的训练覆盖缺口。对训练未覆盖的转化类型，平均 Top-1 准确率从 13.0% 提升到 58.0%，在时间外推测试集上从 4.9% 提升到 23.1%；仅用 2017 年前数据训练的模型经检索扩展后可匹敌或超越用 2025 年前数据训练的非检索模型。
正文 · 原文
Authors:Jiaxing Sun, Xuli Shen, Jiang Wu, Ruiyi Ding, Ruikang Zhang, Xiao An, Xibao Li, Yubin Wang, Xingjian Wei, Yinfan Wang, Yu Liu, Lingli Ge, Xi Xue, Xiaojian Wang, Lijun Wu, Chang-Yu Hsieh, Yuqiang Li, Bowen Zhou, Dahua Lin, Conghui He
Abstract:Reaction precedents guide retrosynthesis, but how far they can compensate for gaps in task-specific training coverage remains unclear. Using 2.71 million reactions, we restricted training and retrieval coverage by transformation or time, then expanded only the retrieval corpus with model parameters fixed. For transformations excluded from training, mean Top-1 accuracy increased from 13.0% to 58.0% on a historical test set and from 4.9% to 23.1% on a temporally held-out test set. For temporal coverage gaps, retrieval expansion enabled a model trained on records before 2017 to match or exceed a non-retrieval model trained on records before 2025 on both test sets. Without task-specific fine-tuning, precedents improved all 14 representative general-purpose models on both test sets and USPTO-50K; the strongest with precedents surpassed all eight representative specialized models. These findings show that inference-time reaction precedents can extend retrosynthetic prediction beyond task-specific training coverage without parameter updates.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.40249 [cs.AI] |
| (or arXiv:2610.40249v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40249 arXiv-issued DOI via DataCite |
Submission history
From: Jiang Wu [view email]
[v1]
Thu, 29 Oct 2026 15:19:54 UTC (20,552 KB)
来源：arXiv cs.AI · arxiv.org