用AI自动生成可验证的反应分类规则，让化学知识库自我进化。
Agentic generation of verifiable rules for deterministic, self-expanding reaction classification
- 多智能体LLM自动构建规则并验证，无需人工标注。
- 将反应分类从68类扩展到14,073类，覆盖超66万专利反应。
- 新规则可动态扩展，适合研究新反应机理的化学家使用。
计算机辅助合成规划依赖大量反应规则库，为每种转化分配确定性、可解释的标签。但化学反应分布长尾，手动编码难以实现，现有工具依赖固定规则集，无法适应新化学领域。本文提出完全自动化流程：基于大语言模型的多智能体框架，在665,901条美国专利反应数据上自动分类并生成规则，每个规则均通过验证循环测试。该方法将标准分类体系从68类扩展至14,073类，无需人工干预。结合轻量指纹分类器，对未见过的反应分类准确率达97.7%，媲美领先专有系统，且能更精细解析化学行为，并按需扩展至训练数据外的化学领域。成果形成一个可自我演进的反应活性数据库，为生成模型构建可靠符号系统提供通用路径。
原文摘要 · Abstract (English)
Computer-assisted synthesis planning breaks target molecules into accessible precursors using large libraries of reaction rules that assign each transformation a deterministic, interpretable label. But chemistry is long-tailed, making manual encoding intractable, and existing tools rely on fixed rulesets that cannot adapt to new chemistries. Here we present a fully automated pipeline in which a multi-agent framework of large language models (LLMs) classifies reactions and writes the rules themselves across 665,901 US patent reactions, generating each rule under a verification loop that tests it against the corpus. It expands a standard taxonomy from 68 to 14,073 classes without human curation. With a lightweight fingerprint classifier, it classifies 97.7\% of unseen reactions, matching a leading proprietary classifier while resolving chemistry more finely and extending on demand to chemistry outside its training distribution. The result is a living reactivity database and a general route to turning generative models into reliable, self-expanding symbolic systems.
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