Agentic generation of verifiable rules for deterministic, self-expanding reaction classification
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 i
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via unknownagent-tools →
- LinkedLinked via unknownA near-autonomous AI chemist improves a challenging reaction in medicinal chemistry →
- LinkedLinked via unknownIEEE Rolls Out Large Language Models Virtual Training Course →
- LinkedLinked via unknownAgentCore-8B →
- LinkedLinked via arxiv author · 85%Daniel Armstrong →
“Agentic generation of verifiable rules for deterministic, self-expanding reaction classification”
- LinkedLinked via arxiv author · 85%Maarten Dobbelaere →
“Agentic generation of verifiable rules for deterministic, self-expanding reaction classification”
- LinkedLinked via arxiv author · 85%Valentas Olikauskas →
“Agentic generation of verifiable rules for deterministic, self-expanding reaction classification”