{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:52:09Z","timestamp":1773489129197,"version":"3.50.1"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3024238","type":"journal-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T20:52:24Z","timestamp":1600203144000},"page":"170433-170451","source":"Crossref","is-referenced-by-count":55,"title":["DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2794-3936","authenticated-orcid":false,"given":"Mohamed","family":"Abdel-Basset","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9925-3232","authenticated-orcid":false,"given":"Hossam","family":"Hawash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-8368","authenticated-orcid":false,"given":"Mohamed","family":"Elhoseny","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7373-0149","authenticated-orcid":false,"given":"Ripon K.","family":"Chakrabortty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6335-3773","authenticated-orcid":false,"given":"Michael","family":"Ryan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s12539-020-00376-6"},{"key":"ref33","article-title":"Prediction of potential commercially inhibitors against SARS-CoV-2 by multi-task deep model","author":"hu","year":"2020","journal-title":"arXiv 2003 00728"},{"key":"ref32","article-title":"Self-attention based molecule representation for predicting drug-target interaction","author":"shin","year":"2019","journal-title":"arXiv 1908 06760"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2020.03.025"},{"key":"ref30","article-title":"DeepGS: Deep representation learning of graphs and sequences for drug-target binding affinity prediction","author":"lin","year":"2020","journal-title":"arXiv 2003 13902"},{"key":"ref37","article-title":"Machine intelligence design of 2019-nCoV drugs","author":"nguyen","year":"2020","journal-title":"BioRxiv"},{"key":"ref36","article-title":"Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches","volume":"307","author":"zhavoronkov","year":"2020"},{"key":"ref35","article-title":"AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2","author":"tang","year":"2020","journal-title":"BioRxiv"},{"key":"ref34","article-title":"A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19","author":"ge","year":"2020","journal-title":"BioRxiv"},{"key":"ref60","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"arXiv 1609 02907"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1080\/17460441.2018.1465407"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290989"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty535"},{"key":"ref64","first-page":"5753","article-title":"XLNet: Generalized autoregressive pretraining for language understanding","author":"yang","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbu010"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"821i","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: Deep drug&#x2013;target binding affinity prediction","volume":"34","author":"\u00f6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-54849-w"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41589-020-0484-2"},{"key":"ref20","first-page":"876","article-title":"On the use of atmospheric plasmas as electromagnetic reflectors (online source style)","volume":"21","author":"vidmar","year":"1992","journal-title":"IEEE Trans Plasma Sci"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.chom.2020.03.023"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-micro-020518-115759"},{"key":"ref24","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"shi","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0209-z"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.tips.2020.03.006"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.lfs.2020.117592"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1038\/s41584-020-0418-0"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0853-0"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.18632\/aging.103001"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.jinf.2020.03.060"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0849-9"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.lfs.2020.117652"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.3390\/v12040445"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmaid.2020.101646"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-3263-x"},{"key":"ref11","article-title":"GraphDTA: Prediction of drug-target binding affinity using graph convolutional networks","author":"nguyen","year":"2019","journal-title":"BioRxiv"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2020.00267"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.01243"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM47256.2019.8983125"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.lfs.2020.117627"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-020-05985-9"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.02.052"},{"key":"ref18","year":"2020","journal-title":"Middle East respiratory syndrome coronavirus (MERS-CoV)"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s41421-020-0153-3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1033"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1075"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-1052-0"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkx1037"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1039\/C9SC03414E"},{"key":"ref7","article-title":"WideDTA: Prediction of drug-target binding affinity","author":"\u00f6zt\u00fcrk","year":"2019","journal-title":"arXiv 1902 04166"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30262-0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-12928-6"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1038\/nbt1228"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkl999"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.antiviral.2020.104787"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1038\/s41422-020-0282-0"},{"key":"ref42","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"mikolov","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2018.8621313"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1021\/ci400709d"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.1990"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09197589.pdf?arnumber=9197589","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:56Z","timestamp":1639770956000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9197589\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":64,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3024238","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}