Agentic generation of verifiable rules for deterministic, self-expanding reaction classification
arXiv:2607.01061v1 Announce Type: cross 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, generatin
Advances in large language models enable autonomous systems to handle complex, domain-specific tasks previously requiring extensive human expertise, making agentic generation viable.
This development allows for the automated creation and verification of reaction rules in chemistry, significantly accelerating drug discovery, materials science, and industrial synthesis.
Chemistry no longer relies solely on manual rule encoding or fixed rulesets, but can adapt and expand its knowledge base autonomously through AI agents.
- · Pharmaceutical industry
- · Chemical engineering firms
- · AI research labs
- · Materials science
- · Manual rule encoders in chemistry
Automated discovery of new chemical reactions and synthesis pathways.
Faster development of new drugs, advanced materials, and catalysts.
Shift in R&D paradigms, with AI agents becoming integral to scientific hypothesis generation and experimental design.
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