{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T03:39:58Z","timestamp":1778038798435,"version":"3.51.4"},"reference-count":61,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82160591"],"award-info":[{"award-number":["82160591"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Clinical Research of Shanghai East Hospital, Tongji University","award":["DFLC2022012"],"award-info":[{"award-number":["DFLC2022012"]}]},{"name":"Key Specialty Construction Project of Shanghai Pudong New Area Health Commission","award":["PWZzk2022-02"],"award-info":[{"award-number":["PWZzk2022-02"]}]},{"name":"Outstanding Leaders Training Program of Pudong Health Bureau of Shanghai","award":["PWR12023-02"],"award-info":[{"award-number":["PWR12023-02"]}]},{"name":"Shanghai Science Technology Innovation Action Plan","award":["23Y11909000"],"award-info":[{"award-number":["23Y11909000"]}]},{"name":"Science Research Project of Hebei Education Department","award":["QN2025011"],"award-info":[{"award-number":["QN2025011"]}]},{"name":"Doctoral Research Start-up Fund Program of The National Police University for Criminal Justice","award":["BSQDW202150"],"award-info":[{"award-number":["BSQDW202150"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Drug\u2013drug interactions (DDIs) are a significant source of adverse drug events and pose critical challenges to patient safety and clinical decision-making. Extracting DDIs from biomedical literature plays an essential role in pharmacovigilance, yet remains difficult due to data sparsity and high annotation costs. This study presents BioMCL-DDI, a novel few-shot learning framework that integrates meta-learning with contrastive embedding strategies to enable efficient DDI extraction under limited supervision. BioMCL-DDI jointly optimizes prototype-based classification and supervised contrastive representation learning within a unified architecture. The model captures both intra-class compactness and inter-class separability, enhancing its generalization in sparse biomedical settings. We evaluate BioMCL-DDI on three benchmark datasets: DDI-2013, DrugBank, and the more recent TAC 2018 DDI Extraction corpus. The model achieves F1 scores of 87.80% on DDI-2013, 86.00% on DrugBank, and 74.85%\/74.82% on the two official test sets of TAC 2018, consistently outperforming competitive baselines. Our model significantly outperforms state-of-the-art baselines in low-resource scenarios. BioMCL-DDI provides a scalable and effective solution for DDI extraction from biomedical texts, with strong potential for integration into clinical decision support systems and biomedical knowledge bases. All our code and data have been publicly released at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Hero-Legend\/BioMCL-DDI\" xlink:type=\"simple\">https:\/\/github.com\/Hero-Legend\/BioMCL-DDI<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1013722","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T19:06:09Z","timestamp":1764961569000},"page":"e1013722","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["A meta-contrastive learning approach for clinical drug-drug interaction extraction from biomedical literature"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5808-4997","authenticated-orcid":true,"given":"Yaxun","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Zhu","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Lian","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocab":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5725-842X","authenticated-orcid":true,"given":"Zuo-lin","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocab":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"issue":"4","key":"pcbi.1013722.ref001","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1111\/bcp.15970","article-title":"Drug-drug interactions and the risk of adverse drug reaction-related hospital admissions in the older population","volume":"90","author":"JE Hughes","year":"2024","journal-title":"Br J Clin Pharmacol."},{"issue":"1","key":"pcbi.1013722.ref002","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1111\/bcp.15882","article-title":"Adverse drug events caused by three high-risk drug\u2013drug interactions in patients admitted to intensive care units: A multicentre retrospective observational study","volume":"90","author":"JE Klopotowska","year":"2024","journal-title":"British Journal of Clinical Pharmacology."},{"issue":"2","key":"pcbi.1013722.ref003","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1111\/bcp.16266","article-title":"Economic burden of hospital admissions for adverse drug reactions in France: the IATROSTAT-ECO study","volume":"91","author":"M-L Laroche","year":"2025","journal-title":"Br J Clin Pharmacol."},{"issue":"1","key":"pcbi.1013722.ref004","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.13303","article-title":"Drug\u2013drug interaction extraction-based system: an natural language processing approach","volume":"42","author":"J Machado","year":"2023","journal-title":"Expert Systems."},{"issue":"6","key":"pcbi.1013722.ref005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3645089","article-title":"Drug\u2013drug interaction relation extraction based on deep learning: a review","volume":"56","author":"M Dou","year":"2024","journal-title":"ACM Comput Surv."},{"key":"pcbi.1013722.ref006","doi-asserted-by":"crossref","first-page":"134167","DOI":"10.1109\/ACCESS.2024.3462101","article-title":"BBL-GAT: a novel method for drug-drug interaction extraction from biomedical literature","volume":"12","author":"Y Jia","year":"2024","journal-title":"IEEE Access."},{"issue":"1","key":"pcbi.1013722.ref007","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1186\/s12859-024-05951-y","article-title":"Biomedical relation extraction method based on ensemble learning and attention mechanism","volume":"25","author":"Y Jia","year":"2024","journal-title":"BMC Bioinformatics."},{"issue":"5","key":"pcbi.1013722.ref008","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1093\/comjnl\/bxae131","article-title":"Variations towards an efficient drug\u2013drug interaction","volume":"68","author":"Y Jia","year":"2024","journal-title":"The Computer Journal."},{"issue":"6","key":"pcbi.1013722.ref009","doi-asserted-by":"crossref","first-page":"4560","DOI":"10.1109\/JBHI.2025.3540861","article-title":"Optimized drug-drug interaction extraction with BioGPT and focal loss-based attention","volume":"29","author":"Z Yuan","year":"2025","journal-title":"IEEE J Biomed Health Inform."},{"key":"pcbi.1013722.ref010","article-title":"LLM-DDI: leveraging large language models for drug-drug interaction prediction on biomedical knowledge graph","author":"D Li","year":"2025","journal-title":"IEEE J Biomed Health 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