HypoKG: Evidence-Disciplined Biomedical Hypothesis Generation Beyond Endpoint Knowledge
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Computer Science > Computation and Language
arXiv:2609.12260 (cs)
[Submitted on 10 Sep 2026]
Title:HypoKG: Evidence-Disciplined Biomedical Hypothesis Generation Beyond Endpoint Knowledge
View a PDF of the paper titled HypoKG: Evidence-Disciplined Biomedical Hypothesis Generation Beyond Endpoint Knowledge, by Dominic Okonkwo and 2 other authors
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Abstract:Large language models (LLMs) can generate biomedical hypotheses, but it remains unclear whether they truly reason from scientific evidence or simply produce convincing-sounding ideas. To study this, we combine three major biological databases: the Kyoto Encyclopedia of Genes and Genomes (KEGG), Rhea, and UniProt, into a unified biochemical knowledge graph and construct a benchmark of 550 paths connecting enzyme sources to rare disease endpoints, yielding 13,200 hypotheses from six LLMs under four conditions varying the biological information each model receives: source enzyme only, full biological path, or source and disease endpoint only. Hypotheses are scored using an expert-derived five-criterion rubric on a 1-5 scale per criterion. We find that models given both the source and disease endpoint often produce the highest-scoring hypotheses, showing that LLMs can generate compelling ideas from minimal information. However, these hypotheses are less grounded in the evidence. In contrast, models given the full biological path generate hypotheses more consistent with known mechanistic relationships. We call this evidence-disciplined reasoning. To confirm this effect, we shuffled intermediate path steps while keeping endpoints fixed. Evidence grounding dropped significantly (delta = -0.793, p < 0.001), confirming models genuinely used path structure during reasoning. Our findings show that knowledge graphs support hypothesis generation in two ways: they identify biological endpoint pairs absent from the literature, and their mechanistic paths guide how LLMs reason between them.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2609.12260 [cs.CL] |
| (or arXiv:2609.12260v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.12260
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026) |
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View a PDF of the paper titled HypoKG: Evidence-Disciplined Biomedical Hypothesis Generation Beyond Endpoint Knowledge, by Dominic Okonkwo and 2 other authors
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