{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:14:42Z","timestamp":1761491682691,"version":"3.41.2"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T00:00:00Z","timestamp":1715385600000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Enhanced by contemporary computational advances, the prediction of drug\u2013target interactions (DTIs) has become crucial in developing de novo and effective drugs. Existing deep learning approaches to DTI prediction are frequently beleaguered by a tendency to overfit specific molecular representations, which significantly impedes their predictive reliability and utility in novel drug discovery contexts. Furthermore, existing DTI networks often disregard the molecular size variance between macro molecules (targets) and micro molecules (drugs) by treating them at an equivalent scale that undermines the accurate elucidation of their interaction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a novel DTI network with a differential-scale scheme to model the binding site for enhancing DTI prediction, which is named as BindingSiteDTI. It explicitly extracts multiscale substructures from targets with different scales of molecular size and fixed-scale substructures from drugs, facilitating the identification of structurally similar substructural tokens, and models the concealed relationships at the substructural level to construct interaction feature. Experiments conducted on popular benchmarks, including DUD-E, human, and BindingDB, shown that BindingSiteDTI contains significant improvements compared with recent DTI prediction methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of BindingSiteDTI can be accessed at https:\/\/github.com\/MagicPF\/BindingSiteDTI.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae308","type":"journal-article","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T04:29:35Z","timestamp":1715401775000},"source":"Crossref","is-referenced-by-count":4,"title":["BindingSiteDTI: differential-scale binding site modelling for drug\u2013target interaction prediction"],"prefix":"10.1093","volume":"40","author":[{"given":"Feng","family":"Pan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University , Kowloon, 999077, Hong Kong"}]},{"given":"Chong","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University , Kowloon, 999077, Hong Kong"}]},{"given":"Si-Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University , Kowloon, 999077, Hong Kong"},{"name":"Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen) , 518172, China"}]},{"given":"Tao","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Chinese Medicine, Hong Kong Baptist University , Kowloon, 999077, Hong Kong"}]},{"given":"Zhaoxiang","family":"Bian","sequence":"additional","affiliation":[{"name":"School of Chinese Medicine, Hong Kong Baptist University , Kowloon, 999077, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9343-2202","authenticated-orcid":false,"given":"Pong Chi","family":"Yuen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University , Kowloon, 999077, Hong Kong"}]}],"member":"286","published-online":{"date-parts":[[2024,5,10]]},"reference":[{"key":"2024071814071955000_btae308-B1","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1093\/bioinformatics\/btaa544","article-title":"DeepCDA: deep cross-domain compound\u2013protein affinity prediction through LSTM and convolutional neural networks","volume":"36","author":"Abbasi","year":"2020","journal-title":"Bioinformatics"},{"key":"2024071814071955000_btae308-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 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