{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T17:13:29Z","timestamp":1774890809044,"version":"3.50.1"},"reference-count":77,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6220071694"],"award-info":[{"award-number":["6220071694"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Discipline Development of Peking University","award":["7101302940"],"award-info":[{"award-number":["7101302940"]}]},{"name":"Discipline Development of Peking University","award":["7101303005"],"award-info":[{"award-number":["7101303005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Accurate prediction of drug\u2013target interactions (DTIs), especially for novel targets or drugs, is crucial for accelerating drug discovery. Recent advances in pretrained language models (PLMs) and multi-modal learning present new opportunities to enhance DTI prediction by leveraging vast unlabeled molecular data and integrating complementary information from multiple modalities.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce DrugLAMP (PLM-assisted multi-modal prediction), a PLM-based multi-modal framework for accurate and transferable DTI prediction. DrugLAMP integrates molecular graph and protein sequence features extracted by PLMs and traditional feature extractors. We introduce two novel multi-modal fusion modules: (i) pocket-guided co-attention (PGCA), which uses protein pocket information to guide the attention mechanism on drug features, and (ii) paired multi-modal attention (PMMA), which enables effective cross-modal interactions between drug and protein features. These modules work together to enhance the model\u2019s ability to capture complex drug\u2013protein interactions. Moreover, the contrastive compound-protein pre-training (2C2P) module enhances the model\u2019s generalization to real-world scenarios by aligning features across modalities and conditions. Comprehensive experiments demonstrate DrugLAMP\u2019s state-of-the-art performance on both standard benchmarks and challenging settings simulating real-world drug discovery, where test drugs\/targets are unseen during training. Visualizations of attention maps and application to predict cryptic pockets and drug side effects further showcase DrugLAMP\u2019s strong interpretability and generalizability. Ablation studies confirm the contributions of the proposed modules.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Source code and datasets are freely available at https:\/\/github.com\/Lzcstan\/DrugLAMP. All data originate from public sources.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae693","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T16:29:23Z","timestamp":1732206563000},"source":"Crossref","is-referenced-by-count":23,"title":["Accurate and transferable drug\u2013target interaction prediction with DrugLAMP"],"prefix":"10.1093","volume":"40","author":[{"given":"Zhengchao","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Big Data and Biomedical AI, College of Future Technology, Peking University , Beijing 100871,","place":["China"]}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Big Data and Biomedical AI, College of Future Technology, Peking University , Beijing 100871,","place":["China"]}]},{"given":"Qichen","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Peking University , Beijing 100871,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9464-4426","authenticated-orcid":false,"given":"Jinzhuo","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Big Data and Biomedical AI, College of Future Technology, Peking University , Beijing 100871,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"2024121406365030200_btae693-B1","author":"Ahmad","year":"2022"},{"key":"2024121406365030200_btae693-B2","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1093\/bib\/bbz157","article-title":"Machine learning approaches and databases for prediction of drug\u2013target interaction: a survey paper","volume":"22","author":"Bagherian","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024121406365030200_btae693-B3","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1038\/s42256-022-00605-1","article-title":"Interpretable bilinear attention network with domain adaptation 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