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KGRxn-LLM: Knowledge Graph Enhanced Large Language Models for Molecular Reaction Reasoning

Paper · 2026 · Weichen Liu, Qiyao Xue, Yuyang Wu et al.

Summary

General-purpose LLMs often reason poorly about chemical reactions. They mislabel functional groups, mix up reaction classes and propose products with atoms that have no source in the reactants. Existing benchmarks mostly test product generation or fact recall, not mechanistic understanding. The authors address both gaps with KGRxn-LLM and a new benchmark, KGRxn-Bench.

They took 32,000 USPTO reactions and processed them with RDKit (canonicalization, atom mapping, functional-group detection). An LLM-assisted, expert-checked annotation loop then labelled the data. The result is a knowledge graph with several levels, linking individual compounds and the groups they contain to reaction categories (both their identifiers and their names) and to specific reaction instances. An agent-based retriever pulls relevant evidence from the graph, such as group changes, typical patterns and analogous reactions, and supplies it to the LLM. When no exact match exists, it falls back on similarity search.

Grounding 7–8B models this way strongly improves accuracy on both seen and held-out reactions, and also helps on external chemistry and biology benchmarks. This suggests that structured chemical knowledge reduces hallucination in reaction reasoning.

Key points

  • Data split: 30,000 USPTO reactions build the KG and the KG-covered benchmark part, and 2,000 are reserved for a hold-out split. KGRxn-Bench contains 1,200 questions on four tasks: functional group identification, reaction type classification, product prediction and reagent prediction.
  • Annotation and retrieval: Annotation is iterative human-in-the-loop, with resampling until accuracy reaches at least 90%. For unseen reactions, retrieval falls back to Jaccard similarity over the sets of functional groups consumed and formed.
  • Gains on KGRxn-Bench:
    • Llama-3.1-8B functional group identification on the KG-covered split rises from 58.82% to 94.12%.
    • Qwen2.5-7B on the hold-out split goes from 67.00% to 92.00%.
  • Gains on external benchmarks:
    • FGBench interaction queries (Qwen2.5-7B): 39.33% → 49.64%.
    • SciKnowEval biology boolean subset (Llama): 88.24% → 95.76%.
  • Limitations:
    • Only ten coarse reaction types are used.
    • The graph is modest at about 30K reactions.
    • Testing covered only 7–8B models.
    • Code and benchmark are released on GitHub (pitt-miskov-zivanov-lab).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Carnegie Mellon UniversityPittsburgh · United States539 works · 179,533 citations🎓University of North Carolina at Chapel HillChapel Hill · United States67 works · 16,474 citations🎓University of PittsburghPittsburgh · United States59 works · 22,324 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksComputer Vision and Pattern Recognition · 2,869 worksComputational Theory and Mathematics · 252 worksMaterials Science · 26 worksMaterials Chemistry · 26 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 worksKnowledge graphs & ontologies · 192 works

Cite

Cite as Weichen Liu, Qiyao Xue, Yuyang Wu, Olexandr Isayev and Natasa Miskov-Zivanov. 2026. KGRxn-LLM: Knowledge Graph Enhanced Large Language Models for Molecular Reaction Reasoning. BioNLP 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.bionlp-1.22

@inproceedings{liu2026kgrxn,
  title = {{KGRxn-LLM: Knowledge Graph Enhanced Large Language Models for Molecular Reaction Reasoning}},
  author = {Weichen Liu, Qiyao Xue, Yuyang Wu, Olexandr Isayev and Natasa Miskov-Zivanov},
  booktitle = {BioNLP 2026},
  year = {2026},
  doi = {10.18653/v1/2026.bionlp-1.22},
  url = {https://doi.org/10.18653/v1/2026.bionlp-1.22}
}

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