{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:04:30Z","timestamp":1768280670226,"version":"3.49.0"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"High-level and Urgently Needed Overseas Talent Programs of Jiangxi Province","award":["20232BCJ25026"],"award-info":[{"award-number":["20232BCJ25026"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102133"],"award-info":[{"award-number":["62102133"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Kaifeng Major Science and Technology","award":["21ZD011"],"award-info":[{"award-number":["21ZD011"]}]},{"name":"Ji\u2019an Finance and Science Foundation","award":["20211085454"],"award-info":[{"award-number":["20211085454"]}]},{"name":"Ji\u2019an Finance and Science Foundation","award":["20222151746"],"award-info":[{"award-number":["20222151746"]}]},{"name":"Ji\u2019an Finance and Science Foundation","award":["20222151704"],"award-info":[{"award-number":["20222151704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>In drug discovery, it is crucial to assess the drug\u2013target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its application in large-scale virtual screening. Deep learning-based methods learn virtual scoring functions from labeled datasets and can quickly predict affinity. However, there are three limitations. First, existing methods only consider the atom-bond graph or one-dimensional sequence representations of compounds, ignoring the information about functional groups (pharmacophores) with specific biological activities. Second, relying on limited labeled datasets fails to learn comprehensive embedding representations of compounds and proteins, resulting in poor generalization performance in complex scenarios. Third, existing feature fusion methods cannot adequately capture contextual interaction information.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Therefore, we propose a novel DTA prediction method named HeteroDTA. Specifically, a multi-view compound feature extraction module is constructed to model the atom\u2013bond graph and pharmacophore graph. The residue concat graph and protein sequence are also utilized to model protein structure and function. Moreover, to enhance the generalization capability and reduce the dependence on task-specific labeled data, pre-trained models are utilized to initialize the atomic features of the compounds and the embedding representations of the protein sequence. A context-aware nonlinear feature fusion method is also proposed to learn interaction patterns between compounds and proteins. Experimental results on public benchmark datasets show that HeteroDTA significantly outperforms existing methods. In addition, HeteroDTA shows excellent generalization performance in cold-start experiments and superiority in the representation learning ability of drug\u2013target pairs. Finally, the effectiveness of HeteroDTA is demonstrated in a real-world drug discovery study.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and data are available at https:\/\/github.com\/daydayupzzl\/HeteroDTA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae240","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T09:35:34Z","timestamp":1719567334000},"page":"i539-i547","source":"Crossref","is-referenced-by-count":5,"title":["Enhancing generalizability and performance in drug\u2013target interaction identification by integrating pharmacophore and pre-trained models"],"prefix":"10.1093","volume":"40","author":[{"given":"Zuolong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Software, Henan University , Kaifeng, Henan Province 475000, China"}]},{"given":"Xin","family":"He","sequence":"additional","affiliation":[{"name":"School of Software, Henan University , Kaifeng, Henan Province 475000, China"},{"name":"Henan International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University , Kaifeng, Henan Province 475000, China"}]},{"given":"Dazhi","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Urology, Ji\u2019an Third People\u2019s Hospital , Ji\u2019an, Jiangxi Province 343000, China"}]},{"given":"Gang","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Nanchang University , Nanchang, Jiangxi Province 330031, China"}]},{"given":"Shengbo","family":"Chen","sequence":"additional","affiliation":[{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University , Kaifeng, Henan Province 475000, 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