{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:15:56Z","timestamp":1774444556968,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF1201200"],"award-info":[{"award-number":["2021YFF1201200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017054","name":"NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization","doi-asserted-by":"publisher","award":["U1909208"],"award-info":[{"award-number":["U1909208"]}],"id":[{"id":"10.13039\/100017054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972423"],"award-info":[{"award-number":["61972423"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072473"],"award-info":[{"award-number":["62072473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["B18059"],"award-info":[{"award-number":["B18059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Provincial Science and Technology Program","award":["2018WK4001"],"award-info":[{"award-number":["2018WK4001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Identifying drug\u2013target interactions is a crucial step for drug discovery and design. Traditional biochemical experiments are credible to accurately validate drug\u2013target interactions. However, they are also extremely laborious, time-consuming and expensive. With the collection of more validated biomedical data and the advancement of computing technology, the computational methods based on chemogenomics gradually attract more attention, which guide the experimental verifications.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this study, we propose an end-to-end deep learning-based method named IIFDTI to predict drug\u2013target interactions (DTIs) based on independent features of drug\u2013target pairs and interactive features of their substructures. First, the interactive features of substructures between drugs and targets are extracted by the bidirectional encoder\u2013decoder architecture. The independent features of drugs and targets are extracted by the graph neural networks and convolutional neural networks, respectively. Then, all extracted features are fused and inputted into fully connected dense layers in downstream tasks for predicting DTIs. IIFDTI takes into account the independent features of drugs\/targets and simulates the interactive features of the substructures from the biological perspective. Multiple experiments show that IIFDTI outperforms the state-of-the-art methods in terms of the area under the receiver operating characteristics curve (AUC), the area under the precision-recall curve (AUPR), precision, and recall on benchmark datasets. In addition, the mapped visualizations of attention weights indicate that IIFDTI has learned the biological knowledge insights, and two case studies illustrate the capabilities of IIFDTI in practical applications.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The data and codes underlying this article are available in Github at https:\/\/github.com\/czjczj\/IIFDTI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac485","type":"journal-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T13:27:27Z","timestamp":1657286847000},"page":"4153-4161","source":"Crossref","is-referenced-by-count":68,"title":["IIFDTI: predicting drug\u2013target interactions through interactive and independent features based on attention mechanism"],"prefix":"10.1093","volume":"38","author":[{"given":"Zhongjian","family":"Cheng","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8319-9793","authenticated-orcid":false,"given":"Qichang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"given":"Yaohang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Old Dominion University , Norfolk, VA 23529, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]}],"member":"286","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"2023041408444597600_","doi-asserted-by":"crossref","first-page":"e0141287","DOI":"10.1371\/journal.pone.0141287","article-title":"Continuous distributed representation of biological sequences for deep proteomics and genomics","volume":"10","author":"Asgari","year":"2015","journal-title":"PLoS One"},{"key":"2023041408444597600_","volume-title":"arXiv preprint arXiv:2004.05150.","author":"Beltagy","year":"2020"},{"key":"2023041408444597600_","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.1093\/bioinformatics\/btp433","article-title":"Supervised prediction of drug\u2013target interactions using bipartite local models","volume":"25","author":"Bleakley","year":"2009","journal-title":"Bioinformatics"},{"key":"2023041408444597600_","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1093\/bioinformatics\/btv302","article-title":"Glass: a comprehensive database for experimentally validated GPCR-ligand associations","volume":"31","author":"Chan","year":"2015","journal-title":"Bioinformatics"},{"key":"2023041408444597600_","doi-asserted-by":"crossref","first-page":"4406","DOI":"10.1093\/bioinformatics\/btaa524","article-title":"TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"key":"2023041408444597600_","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1039\/c2mb25110h","article-title":"Prediction of chemical\u2013protein interactions: multitarget-QSAR versus computational chemogenomic methods","volume":"8","author":"Cheng","year":"2012","journal-title":"Mol. 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