@inproceedings{liu-etal-2026-kgrxn,
    title = "{KGR}xn-{LLM}: Knowledge Graph Enhanced Large Language Models for Molecular Reaction Reasoning",
    author = "Liu, Weichen  and
      Xue, Qiyao  and
      Wu, Yuyang  and
      Isayev, Olexandr  and
      Miskov-Zivanov, Natasa",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.22/",
    pages = "268--281",
    ISBN = "979-8-89176-434-7",
    abstract = "Large language models (LLMs) demonstrate strong general language capabilities but remain limited in chemical reasoning, particularly for tasks requiring structured, mechanistic understanding of molecular reactions. We present Knowledge Graph Reaction LLM (KGRxn-LLM), a framework that augments LLMs with a hierarchical chemical knowledge graph (KG) to ground reasoning in molecular transformations and reaction patterns. Existing benchmarks primarily emphasize reaction or molecular fact recall, providing limited assessment of reaction-level mechanistic reasoning. To address this gap, we introduce KGRxn-Bench, a benchmark of 1,200 questions designed to evaluate LLMs on reaction-centric reasoning tasks, including functional group identification, reaction type classification, and product and reagent prediction. Experimental results show that our approach of grounding LLMs in structured KG substantially improves performance across multiple tasks and model backbones, outperforming domain-specific fine-tuned models on KG-covered splits and most hold-out splits."
}