{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T13:36:04Z","timestamp":1787664964649,"version":"build-2736575974"},"reference-count":310,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T00:00:00Z","timestamp":1579219200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"publisher","award":["P30 ES017885"],"award-info":[{"award-number":["P30 ES017885"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug\u2013target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.<\/jats:p>","DOI":"10.1093\/bib\/bbz157","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T15:13:04Z","timestamp":1573225984000},"page":"247-269","source":"Crossref","is-referenced-by-count":397,"title":["Machine learning approaches and databases for prediction of drug\u2013target interaction: a survey paper"],"prefix":"10.1093","volume":"22","author":[{"given":"Maryam","family":"Bagherian","sequence":"first","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elyas","family":"Sabeti","sequence":"additional","affiliation":[{"name":"Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maureen A","family":"Sartor","sequence":"additional","affiliation":[{"name":"Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaneta","family":"Nikolovska-Coleska","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Medical School, University of Michigan, Ann Arbor, MI, 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayvan","family":"Najarian","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, 48109, 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