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arXiv · 2607.01061

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

Daniel Armstrong·Maarten Dobbelaere·Valentas Olikauskas·Helena Avila·Octavian Susanu·Jérôme Waser·Philippe Schwaller
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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.

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Daniel Armstrong, Maarten Dobbelaere, Valentas Olikauskas, Helena Avila, Octavian Susanu, Jérôme Waser, Philippe Schwaller. 2026-07-01. Agentic generation of verifiable rules for deterministic, self-expanding reaction classification. https://arxiv.org/abs/2607.01061

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