Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration

• URL: http://arxiv.org/abs/2510.16590v1
• Date: Sat, 18 Oct 2025 17:27:44 GMT
• Title: Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration
• Authors: Alan Kai Hassen, Andrius Bernatavicius, Antonius P. A. Janssen, Mike Preuss, Gerard J. P. van Westen, Djork-Arné Clevert,
• Abstract summary: We introduce a framework for molecular reasoning using general-purpose Large Language Models.<n>Our method anchors chain-of-thought reasoning to the molecular structure by using unique atomic identifiers.<n>Our work also provides a method to generate theoretically grounded synthetic datasets.
• Score: 2.9496795797433073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised methods. In this work, we introduce a framework for molecular reasoning using general-purpose Large Language Models (LLMs) that operates without requiring labeled training data. Our method anchors chain-of-thought reasoning to the molecular structure by using unique atomic identifiers. First, the LLM performs a one-shot task to identify relevant fragments and their associated chemical labels or transformation classes. In an optional second step, this position-aware information is used in a few-shot task with provided class examples to predict the chemical transformation. We apply our framework to single-step retrosynthesis, a task where LLMs have previously underperformed. Across academic benchmarks and expert-validated drug discovery molecules, our work enables LLMs to achieve high success rates in identifying chemically plausible reaction sites ($\geq90\%$), named reaction classes ($\geq40\%$), and final reactants ($\geq74\%$). Beyond solving complex chemical tasks, our work also provides a method to generate theoretically grounded synthetic datasets by mapping chemical knowledge onto the molecular structure and thereby addressing data scarcity.

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