{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T21:28:30Z","timestamp":1775942910045,"version":"3.50.1"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T00:00:00Z","timestamp":1728000000000},"content-version":"vor","delay-in-days":3,"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":["12261060"],"award-info":[{"award-number":["12261060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20242BAB26007"],"award-info":[{"award-number":["20242BAB26007"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Accurately predicting the drug\u2212target binding affinity (DTA) is crucial to drug discovery and repurposing. Although deep learning has been widely used in this field, it still faces challenges with insufficient generalization performance, inadequate use of 3D information, and poor interpretability.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To alleviate these problems, we developed the PocketDTA model. This model enhances the generalization performance by pre-trained models ESM-2 and GraphMVP. It ingeniously handles the first 3 (top-3) target binding pockets and drug 3D information through customized GVP-GNN Layers and GraphMVP-Decoder. In addition, it uses a bilinear attention network to enhance interpretability. Comparative analysis with state-of-the-art (SOTA) methods on the optimized Davis and KIBA datasets reveals that the PocketDTA model exhibits significant performance advantages. Further, ablation studies confirm the effectiveness of the model components, whereas cold-start experiments illustrate its robust generalization capabilities. In particular, the PocketDTA model has shown significant advantages in identifying key drug functional groups and amino acid residues via molecular docking and literature validation, highlighting its strong potential for interpretability.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Code and data are available at: https:\/\/github.com\/zhaolongNCU\/PocketDTA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae594","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T16:38:36Z","timestamp":1728059916000},"source":"Crossref","is-referenced-by-count":26,"title":["PocketDTA: an advanced multimodal architecture for enhanced prediction of drug\u2212target affinity from 3D structural data of target binding pockets"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3642-9078","authenticated-orcid":false,"given":"Long","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University , Nanchang 330031,","place":["China"]}]},{"given":"Hongmei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University , Nanchang 330031,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0045-868X","authenticated-orcid":false,"given":"Shaoping","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University , Nanchang 330031,","place":["China"]},{"name":"Institute of Mathematics and Interdisciplinary Sciences, Nanchang University , Nanchang 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