{"ID":2853469,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16590","arxiv_id":"2510.16590","title":"Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration","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 task-specific model training. Our method anchors chain-of-thought reasoning to the molecular structure by using unique atomic identifiers. First, the LLM performs a zero-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 ($\\geq74\\%$). Ultimately, our work establishes a general blueprint for applying LLMs to challenges where molecular reasoning and molecular transformations are key, positioning atom-anchored LLMs as a powerful solution for data-scarce chemistry domains.","short_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 task-specific model t...","url_abs":"https://arxiv.org/abs/2510.16590","url_pdf":"https://arxiv.org/pdf/2510.16590v2","authors":"[\"Alan Kai Hassen\",\"Andrius Bernatavicius\",\"Antonius P. A. Janssen\",\"Mike Preuss\",\"Gerard J. P. van Westen\",\"Djork-Arné Clevert\"]","published":"2025-10-18T17:27:44Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"q-bio.BM\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}