Agentic generation of verifiable rules for deterministic, self-expanding reaction classification

2026-07-01

AI Summary

Traditional reaction rules rely on manually encoded heuristics, limiting their coverage of the long-tail chemical space and adaptability to novel transformations. This work proposes an automated, self-extending framework for reaction rule generation based on a multi-agent large language model, enabling human-intervention-free rule evolution through a closed-loop validation mechanism. By integrating a lightweight fingerprint classifier with a symbolic system, the method achieves 97.7% classification accuracy on 665,901 unseen patent reactions, expanding standard reaction categories from 68 to 14,073—substantially outperforming leading commercial classifiers. Furthermore, it supports immediate generalization to out-of-distribution reactions, thereby establishing a sustainably evolving reactivity database.

Abstract

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.

Problem

• reaction classification
• rule generation
• long-tailed chemistry
• self-expanding systems
• deterministic labeling

Innovation

• agentic generation
• verifiable reaction rules
• self-expanding classification
• multi-agent LLMs
• deterministic symbolic systems