dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions

Cannon, M. J.; Bratulin, A.; Stevenson, J. S.; Perry, K.; Coffman, A.; Kiwala, S.; Schimmelpfennig, L.; Costello, H.; McMichael, J. F.; Griffith, M.; Griffith, O. L.; Wagner, A. H.

doi:10.64898/2026.01.16.699733

bioRxiv · 10.64898/2026.01.16.699733

dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions

Cannon, M. J.·Bratulin, A.·Stevenson, J. S.·Perry, K.·Coffman, A.·Kiwala, S.·Schimmelpfennig, L.·Costello, H.·McMichael, J. F.·Griffith, M.·Griffith, O. L.·Wagner, A. H.

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Abstract

IMPORTANCEThe Drug-Gene Interaction Database (DGIdb) has a long history of driving hypothesis generation for biomedical research through the careful curation of drug-gene interaction data from primary and secondary sources with supporting literature. Recent advances in large-language model (LLM) and artificial intelligence (AI) technologies have enabled new paradigms for knowledge extraction and biocuration. The accelerating growth of biomedical literature presents a significant challenge for maintaining up-to-date interaction data. With more than 38 million citations indexed in PubMed alone, new strategies must evolve to identify and incorporate new interaction data into DGIdb. OBJECTIVEIdentify new cost-effective AI curation strategies for incorporating new drug-gene interactions into DGIdb. METHODSWe present a methodology that leverages deterministic natural language processing techniques, existing harmonization frameworks, and AI-assisted curation to systematically narrow the literature space and identify new drug-gene interactions from published studies for inclusion in DGIdb. RESULTSWe demonstrate the use of lemmatization to prioritize a set of 100 abstracts containing high amounts of interaction words for downstream AI curation. From our set of abstracts, we were then able to identify 137 drug-gene interactions via an AI curation task, with 121 (88.3%) of these interactions being completely novel to DGIdb. A human expert evaluator reviewed this interaction set and was able to validate 134 of 137 (97.8%) interactions as being valid based on the text provided. CONCLUSIONTaken together, our results highlight a promising, cost-effective method of ingesting new interactions into DGIdb.

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Cannon, M. J., Bratulin, A., Stevenson, J. S., Perry, K., Coffman, A., Kiwala, S., Schimmelpfennig, L., Costello, H., McMichael, J. F., Griffith, M., Griffith, O. L., Wagner, A. H.. 2026-01-19. dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions. https://doi.org/10.64898/2026.01.16.699733

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