{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:11:25Z","timestamp":1772910685559,"version":"3.50.1"},"reference-count":92,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"content-version":"vor","delay-in-days":331,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>It is getting increasingly challenging to efficiently exploit drug-related information described in the growing amount of scientific literature. Indeed, for drug\u2013gene\/protein interactions, the challenge is even bigger, considering the scattered information sources and types of interactions. However, their systematic, large-scale exploitation is key for developing tools, impacting knowledge fields as diverse as drug design or metabolic pathway research. Previous efforts in the extraction of drug\u2013gene\/protein interactions from the literature did not address these scalability and granularity issues. To tackle them, we have organized the DrugProt track at BioCreative VII. In the context of the track, we have released the DrugProt Gold Standard corpus, a collection of 5000 PubMed abstracts, manually annotated with granular drug\u2013gene\/protein interactions. We have proposed a novel large-scale track to evaluate the capacity of natural language processing systems to scale to the range of millions of documents, and generate with their predictions a silver standard knowledge graph of 53\u00a0993\u00a0602 nodes and 19\u00a0367\u00a0406 edges. Its use exceeds the shared task and points toward pharmacological and biological applications such as drug discovery or continuous database curation. Finally, we have created a persistent evaluation scenario on CodaLab to continuously evaluate new relation extraction systems that may arise. Thirty teams from four continents, which involved 110 people, sent 107 submission runs for the Main DrugProt track, and nine teams submitted 21 runs for the Large Scale DrugProt track. Most participants implemented deep learning approaches based on pretrained transformer-like language models (LMs) such as BERT or BioBERT, reaching precision and recall values as high as 0.9167 and 0.9542 for some relation types. Finally, some initial explorations of the applicability of the knowledge graph have shown its potential to explore the chemical\u2013protein relations described in the literature, or chemical compound\u2013enzyme interactions.<\/jats:p>\n               <jats:p>Database URL: \u00a0https:\/\/doi.org\/10.5281\/zenodo.4955410<\/jats:p>","DOI":"10.1093\/database\/baad080","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T20:12:04Z","timestamp":1701202324000},"source":"Crossref","is-referenced-by-count":24,"title":["Overview of\u00a0DrugProt task at BioCreative VII: data and\u00a0methods for\u00a0large-scale text mining and\u00a0knowledge graph generation of\u00a0heterogenous chemical\u2013protein relations"],"prefix":"10.1093","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5654-001X","authenticated-orcid":false,"given":"Antonio","family":"Miranda-Escalada","sequence":"first","affiliation":[{"name":"Life Sciences Department, Barcelona Supercomputing Center , Barcelona 08034, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5555-2828","authenticated-orcid":false,"given":"Farrokh","family":"Mehryary","sequence":"additional","affiliation":[{"name":"TurkuNLP Group, Department of Computing, University of Turku , Turku 20014, Finland"}]},{"given":"Jouni","family":"Luoma","sequence":"additional","affiliation":[{"name":"TurkuNLP Group, Department of Computing, University of Turku , Turku 20014, Finland"}]},{"given":"Darryl","family":"Estrada-Zavala","sequence":"additional","affiliation":[{"name":"Life Sciences Department, Barcelona Supercomputing Center , Barcelona 08034, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4976-9879","authenticated-orcid":false,"given":"Luis","family":"Gasco","sequence":"additional","affiliation":[{"name":"Life Sciences Department, Barcelona Supercomputing Center , Barcelona 08034, Spain"}]},{"given":"Sampo","family":"Pyysalo","sequence":"additional","affiliation":[{"name":"TurkuNLP Group, Department of Computing, University of Turku , Turku 20014, Finland"}]},{"given":"Alfonso","family":"Valencia","sequence":"additional","affiliation":[{"name":"Life Sciences Department, Barcelona Supercomputing Center , Barcelona 08034, Spain"}]},{"given":"Martin","family":"Krallinger","sequence":"additional","affiliation":[{"name":"Life Sciences Department, Barcelona Supercomputing Center , Barcelona 08034, Spain"}]}],"member":"286","published-online":{"date-parts":[[2023,11,28]]},"reference":[{"key":"2023112818141068700_R1","article-title":"Overview of\u00a0DrugProt BioCreative VII track: quality evaluation and\u00a0large scale text mining of\u00a0drug-gene\/protein relations","author":"Miranda","year":"2021"},{"key":"2023112818141068700_R2","doi-asserted-by":"crossref","first-page":"D684","DOI":"10.1093\/nar\/gkm795","article-title":"STITCH: interaction networks of\u00a0chemicals and\u00a0proteins","volume":"36","author":"Kuhn","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"2023112818141068700_R3","doi-asserted-by":"crossref","first-page":"D945","DOI":"10.1093\/nar\/gkw1074","article-title":"The ChEMBL database in\u00a02017","volume":"45","author":"Gaulton","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"2023112818141068700_R4","first-page":"pp. 141","article-title":"Overview of\u00a0the BioCreative VI chemical-protein interaction Track","author":"Krallinger","year":"2017"},{"key":"2023112818141068700_R5","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1136\/amiajnl-2011-000465","article-title":"Overcoming barriers to NLP for\u00a0clinical text: the role of\u00a0shared tasks and\u00a0the need for\u00a0additional creative solutions","volume":"18","author":"Chapman","year":"2011","journal-title":"J. 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