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dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions
by
Mcmichael, Joshua F
, Bratulin, Anastasia
, Costello, Heather
, Perry, Kathryn
, Wagner, Alex H
, Cannon, Matthew J
, Coffman, Adam
, Griffith, Malachi
, Griffith, Obi L
, Stevenson, James S
, Kiwala, Susanna
, Schimmelpfennig, Lars
in
Artificial intelligence
/ Medical research
2026
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dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions
by
Mcmichael, Joshua F
, Bratulin, Anastasia
, Costello, Heather
, Perry, Kathryn
, Wagner, Alex H
, Cannon, Matthew J
, Coffman, Adam
, Griffith, Malachi
, Griffith, Obi L
, Stevenson, James S
, Kiwala, Susanna
, Schimmelpfennig, Lars
in
Artificial intelligence
/ Medical research
2026
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Do you wish to request the book?
dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions
by
Mcmichael, Joshua F
, Bratulin, Anastasia
, Costello, Heather
, Perry, Kathryn
, Wagner, Alex H
, Cannon, Matthew J
, Coffman, Adam
, Griffith, Malachi
, Griffith, Obi L
, Stevenson, James S
, Kiwala, Susanna
, Schimmelpfennig, Lars
in
Artificial intelligence
/ Medical research
2026
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dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions
Paper
dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions
2026
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Overview
IMPORTANCE: The 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. OBJECTIVE: Identify new cost-effective AI curation strategies for incorporating new drug-gene interactions into DGIdb. METHODS: We 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. RESULTS: We 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. CONCLUSION: Taken together, our results highlight a promising, cost-effective method of ingesting new interactions into DGIdb.Competing Interest StatementThe authors have declared no competing interest.Funder Information DeclaredNational Human Genome Research Institute, https://ror.org/00baak391, R00HG010157
Publisher
Cold Spring Harbor Laboratory Press
Subject
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