{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T04:26:53Z","timestamp":1773980813614,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&D Programs of China","award":["2021YFC2100800"],"award-info":[{"award-number":["2021YFC2100800"]}]},{"name":"National Key R&D Programs of China","award":["2021YFC2100801"],"award-info":[{"award-number":["2021YFC2100801"]}]},{"name":"National Key R&D Programs of China","award":["2020YFA0908700"],"award-info":[{"award-number":["2020YFA0908700"]}]},{"name":"National Key R&D Programs of China","award":["2020YFA0908702"],"award-info":[{"award-number":["2020YFA0908702"]}]},{"name":"Graduate Research Innovation Foundation of Tianjin City","award":["2022BKY027"],"award-info":[{"award-number":["2022BKY027"]}]},{"name":"CAAI-MindSpore Open Fund"}],"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>Exploring the association between drugs and targets is essential for drug discovery and repurposing. Comparing with the traditional methods that regard the exploration as a binary classification task, predicting the drug\u2013target binding affinity can provide more specific information. Many studies work based on the assumption that similar drugs may interact with the same target. These methods constructed a symmetric graph according to the undirected drug similarity or target similarity. Although these similarities can measure the difference between two molecules, it is unable to analyze the inclusion relationship of their substructure. For example, if drug A contains all the substructures of drug B, then in the message-passing mechanism of the graph neural network, drug A should acquire all the properties of drug B, while drug B should only obtain some of the properties of A.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To this end, we proposed a structure-inclusive similarity (SIS) which measures the similarity of two drugs by considering the inclusion relationship of their substructures. Based on SIS, we constructed a drug graph and a target graph, respectively, and predicted the binding affinities between drugs and targets by a graph convolutional network-based model. Experimental results show that considering the inclusion relationship of the substructure of two molecules can effectively improve the accuracy of the prediction model. The performance of our SIS-based prediction method outperforms several state-of-the-art methods for drug\u2013target binding affinity prediction. The case studies demonstrate that our model is a practical tool to predict the binding affinity between drugs and targets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Source codes and data are available at https:\/\/github.com\/HuangStomach\/SISDTA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae563","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T16:27:04Z","timestamp":1726676824000},"source":"Crossref","is-referenced-by-count":5,"title":["Structure-inclusive similarity based directed GNN: a method that can control information flow to predict drug\u2013target binding affinity"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7542-6837","authenticated-orcid":false,"given":"Jipeng","family":"Huang","sequence":"first","affiliation":[{"name":"Centre for Bioinformatics and Intelligent Medicine, Nankai University , Tianjin 300071,","place":["China"]},{"name":"College of Computer Science, Nankai University , Tianjin 300071,","place":["China"]},{"name":"Tianjin Key Laboratory of Network and Data Security , Tianjin 300350,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8564-0856","authenticated-orcid":false,"given":"Chang","family":"Sun","sequence":"additional","affiliation":[{"name":"Centre for Bioinformatics and Intelligent Medicine, Nankai University , Tianjin 300071,","place":["China"]},{"name":"College of Computer Science, Nankai University , Tianjin 300071,","place":["China"]},{"name":"Tianjin Key Laboratory of Network and Data Security , Tianjin 300350,","place":["China"]}]},{"given":"Minglei","family":"Li","sequence":"additional","affiliation":[{"name":"Centre for Bioinformatics and Intelligent Medicine, Nankai University , Tianjin 300071,","place":["China"]},{"name":"College of Computer Science, Nankai University , Tianjin 300071,","place":["China"]},{"name":"Tianjin Key Laboratory of Network and Data Security , Tianjin 300350,","place":["China"]}]},{"given":"Rong","family":"Tang","sequence":"additional","affiliation":[{"name":"Centre for Bioinformatics and Intelligent Medicine, Nankai University , Tianjin 300071,","place":["China"]},{"name":"College of Computer Science, Nankai University , Tianjin 300071,","place":["China"]},{"name":"Tianjin Key Laboratory of Network and Data Security , Tianjin 300350,","place":["China"]}]},{"given":"Bin","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Hebei Normal University , Shijiazhuang 050024,","place":["China"]}]},{"given":"Shuqin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University , Tianjin, Xi Qing District 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