{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:08Z","timestamp":1772138048234,"version":"3.50.1"},"reference-count":57,"publisher":"Oxford University Press (OUP)","issue":"24","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004052","name":"King Abdullah University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004052","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Office of Sponsored Research","award":["URF\/1\/3790-01-01"],"award-info":[{"award-number":["URF\/1\/3790-01-01"]}]},{"name":"Office of Sponsored Research","award":["URF\/1\/4355-01-01"],"award-info":[{"award-number":["URF\/1\/4355-01-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>In silico drug\u2013target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding affinities. Both approaches can be combined with information about interaction networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein\u2013protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major effects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>DTI-Voodoo source code and data necessary to reproduce results are freely available at https:\/\/github.com\/THinnerichs\/DTI-VOODOO.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab548","type":"journal-article","created":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T07:09:34Z","timestamp":1627283374000},"page":"4835-4843","source":"Crossref","is-referenced-by-count":23,"title":["DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug\u2013target interactions"],"prefix":"10.1093","volume":"37","author":[{"given":"Tilman","family":"Hinnerichs","sequence":"first","affiliation":[{"name":"Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology , Thuwal 23955, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8149-5890","authenticated-orcid":false,"given":"Robert","family":"Hoehndorf","sequence":"additional","affiliation":[{"name":"Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology , Thuwal 23955, Saudi Arabia"}]}],"member":"286","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"2023051607133472000_btab548-B1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat. Genet"},{"key":"2023051607133472000_btab548-B2","first-page":"1","author":"Bianchi","year":"2019"},{"key":"2023051607133472000_btab548-B3","doi-asserted-by":"crossref","first-page":"D344","DOI":"10.1093\/nar\/gkaa977","article-title":"The InterPro protein families and domains database: 20 years on","volume":"49","author":"Blum","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2023051607133472000_btab548-B4","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1046\/j.1365-2036.1999.00652.x","article-title":"Mesalazine-induced apoptosis of colorectal cancer: on the verge of a new chemopreventive era?","volume":"13","author":"Bus","year":"1999","journal-title":"Alimentary Pharmacol. Therap"},{"key":"2023051607133472000_btab548-B5","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1126\/science.1158140","article-title":"Drug target identification using side-effect similarity","volume":"321","author":"Campillos","year":"2008","journal-title":"Science"},{"key":"2023051607133472000_btab548-B6","first-page":"D325","article-title":"The gene ontology resource: enriching a GOld mine","volume":"49","author":"Carbon","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2023051607133472000_btab548-B7","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1093\/bioinformatics\/btaa879","article-title":"Predicting candidate genes from phenotypes, functions and anatomical site of expression","volume":"37","author":"Chen","year":"2021","journal-title":"Bioinformatics"},{"key":"2023051607133472000_btab548-B8","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1093\/bib\/bbv066","article-title":"Drug\u2013target interaction prediction: databases, web servers and computational models","volume":"17","author":"Chen","year":"2016","journal-title":"Brief. Bioinf"},{"key":"2023051607133472000_btab548-B9","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1093\/bib\/bbz152","article-title":"DTI-CDF: a cascade deep forest model towards the prediction of drug\u2013target interactions based on hybrid features","volume":"22","author":"Chu","year":"2021","journal-title":"Brief. Bioinf"},{"key":"2023051607133472000_btab548-B10","first-page":"3844","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS\u201916","author":"Defferrard","year":"2016"},{"key":"2023051607133472000_btab548-B11","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1093\/bib\/bbt056","article-title":"Similarity-based machine learning methods for predicting drug\u2013target interactions: a brief review","volume":"15","author":"Ding","year":"2014","journal-title":"Brief. Bioinf"},{"key":"2023051607133472000_btab548-B12","doi-asserted-by":"crossref","first-page":"1289259","DOI":"10.1155\/2017\/1289259","article-title":"Drug target protein-protein interaction networks: a systematic perspective","volume":"2017","author":"Feng","year":"2017","journal-title":"BioMed Res. Int"},{"key":"2023051607133472000_btab548-B13","author":"Fey","year":"2019"},{"key":"2023051607133472000_btab548-B14","doi-asserted-by":"crossref","first-page":"e1002444","DOI":"10.1371\/journal.pcbi.1002444","article-title":"\u201cguilt by association\u201d is the exception rather than the rule in gene networks","volume":"8","author":"Gillis","year":"2012","journal-title":"PLoS Comput. Biol"},{"key":"2023051607133472000_btab548-B15","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1038\/msb.2011.26","article-title":"PREDICT: a method for inferring novel drug indications with application to personalized medicine","volume":"7","author":"Gottlieb","year":"2011","journal-title":"Mol. Syst. Biol"},{"key":"2023051607133472000_btab548-B16","author":"Hamilton","year":"2017"},{"key":"2023051607133472000_btab548-B17","doi-asserted-by":"crossref","first-page":"e119","DOI":"10.1093\/nar\/gkr538","article-title":"PhenomeNET: a whole-phenome approach to disease gene discovery","volume":"39","author":"Hoehndorf","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2023051607133472000_btab548-B18","author":"Honda","year":"2019"},{"key":"2023051607133472000_btab548-B19","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1093\/bioinformatics\/btaa880","article-title":"MolTrans: molecular interaction transformer for drug\u2013target interaction prediction","volume":"37","author":"Huang","year":"2021","journal-title":"Bioinformatics"},{"key":"2023051607133472000_btab548-B20","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/S2468-1253(21)00018-2","article-title":"Chemoprevention with low-dose aspirin, mesalazine, or both in patients with familial adenomatous polyposis without previous colectomy (j-FAPP study IV): a multicentre, double-blind, randomised, two-by-two factorial design trial","volume":"6","author":"Ishikawa","year":"2021","journal-title":"Lancet Gastroenterol. Hepatol"},{"key":"2023051607133472000_btab548-B21","first-page":"245","author":"Jeni","year":"2013"},{"key":"2023051607133472000_btab548-B22","author":"Kingma","year":"2015"},{"key":"2023051607133472000_btab548-B23","author":"Kipf","year":"2016"},{"key":"2023051607133472000_btab548-B24","author":"Klicpera","year":"2018"},{"key":"2023051607133472000_btab548-B25","doi-asserted-by":"crossref","first-page":"D1018","DOI":"10.1093\/nar\/gky1105","article-title":"Expansion of the human phenotype ontology (HPO) knowledge base and resources","volume":"47","author":"K\u00f6hler","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023051607133472000_btab548-B26","doi-asserted-by":"crossref","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The SIDER database of drugs and side effects","volume":"44","author":"Kuhn","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023051607133472000_btab548-B27","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1093\/bioinformatics\/btz595","article-title":"DeepGOPlus: improved protein function prediction from sequence","volume":"36","author":"Kulmanov","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051607133472000_btab548-B28","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1186\/s12859-018-2199-x","article-title":"Identification of drug\u2013target interaction by a random walk with restart method on an interactome network","volume":"19","author":"Lee","year":"2018","journal-title":"BMC Bioinf"},{"key":"2023051607133472000_btab548-B29","doi-asserted-by":"crossref","first-page":"e1007129","DOI":"10.1371\/journal.pcbi.1007129","article-title":"DeepConv-DTI: prediction of drug\u2013target interactions via deep learning with convolution on protein sequences","volume":"15","author":"Lee","year":"2019","journal-title":"PLOS Comput. Biol"},{"key":"2023051607133472000_btab548-B30","author":"Li","year":"2019"},{"key":"2023051607133472000_btab548-B31","author":"Li","year":"2020"},{"key":"2023051607133472000_btab548-B32","doi-asserted-by":"crossref","first-page":"i911","DOI":"10.1093\/bioinformatics\/btaa822","article-title":"DeepCDR: a hybrid graph convolutional network for predicting cancer drug response","volume":"36","author":"Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051607133472000_btab548-B33","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1007\/s13277-012-0399-y","article-title":"Rsf-1 overexpression correlates with poor prognosis and cell proliferation in colon cancer","volume":"33","author":"Liu","year":"2012","journal-title":"Tumor Biol"},{"key":"2023051607133472000_btab548-B34","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug\u2013target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat. Commun"},{"key":"2023051607133472000_btab548-B35","author":"Mikolov","year":"2013"},{"key":"2023051607133472000_btab548-B36","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF03256752","article-title":"MedDRA","volume":"23","author":"Mozzicato","year":"2009","journal-title":"Pharmaceutical Med"},{"key":"2023051607133472000_btab548-B37","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","article-title":"GraphDTA: predicting drug\u2013target binding affinity with graph neural networks","volume":"37","author":"Nguyen","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051607133472000_btab548-B38","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1038\/35001165","article-title":"Guilt-by-association goes global","volume":"403","author":"Oliver","year":"2000","journal-title":"Nature"},{"key":"2023051607133472000_btab548-B39","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1038\/nrd2199","article-title":"How many drug targets are there?","volume":"5","author":"Overington","year":"2006","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023051607133472000_btab548-B40","doi-asserted-by":"crossref","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: deep drug\u2013target binding affinity prediction","volume":"34","author":"\u00d6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051607133472000_btab548-B41","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1093\/bib\/bbu010","article-title":"Toward more realistic drug\u2013target interaction predictions","volume":"16","author":"Pahikkala","year":"2015","journal-title":"Brief. Bioinf"},{"key":"2023051607133472000_btab548-B42","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1093\/bib\/bbx017","article-title":"A review of network-based approaches to drug repositioning","volume":"19","author":"Shahreza","year":"2017","journal-title":"Brief. Bioinf"},{"key":"2023051607133472000_btab548-B43","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1002\/wsbm.44","article-title":"The mammalian phenotype ontology: enabling robust annotation and comparative analysis","volume":"1","author":"Smith","year":"2009","journal-title":"Wiley Interdiscip. Rev. Syst. Biol. Med"},{"key":"2023051607133472000_btab548-B44","doi-asserted-by":"crossref","first-page":"D447","DOI":"10.1093\/nar\/gku1003","article-title":"STRING v10: protein\u2013protein interaction networks, integrated over the tree of life","volume":"43","author":"Szklarczyk","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023051607133472000_btab548-B45","doi-asserted-by":"crossref","first-page":"D380","DOI":"10.1093\/nar\/gkv1277","article-title":"STITCH 5: augmenting protein\u2013chemical interaction networks with tissue and affinity data","volume":"44","author":"Szklarczyk","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023051607133472000_btab548-B46","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s13321-020-00447-2","article-title":"DTiGEMS: drug\u2013target interaction prediction using graph embedding, graph mining, and similarity-based techniques","volume":"12","author":"Thafar","year":"2020","journal-title":"J. Cheminf"},{"key":"2023051607133472000_btab548-B47","author":"Trebacz","year":"2020"},{"key":"2023051607133472000_btab548-B48","first-page":"23","volume-title":"Pattern Recognition in Bioinformatics","author":"van Laarhoven","year":"2014"},{"key":"2023051607133472000_btab548-B49","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"2023051607133472000_btab548-B50","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1093\/bioinformatics\/bty543","article-title":"NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug\u2013target interactions","volume":"35","author":"Wan","year":"2019","journal-title":"Bioinformatics"},{"key":"2023051607133472000_btab548-B51","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1093\/bib\/bby069","article-title":"Review and comparative assessment of similarity-based methods for prediction of drug\u2013protein interactions in the druggable human proteome","volume":"20","author":"Wang","year":"2019","journal-title":"Brief. Bioinf"},{"key":"2023051607133472000_btab548-B52","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1021\/acs.jproteome.6b00618","article-title":"Deep-learning-based drug\u2013target interaction prediction","volume":"16","author":"Wen","year":"2017","journal-title":"J. Proteome Res"},{"key":"2023051607133472000_btab548-B53","doi-asserted-by":"crossref","first-page":"D901","DOI":"10.1093\/nar\/gkm958","article-title":"DrugBank: a knowledgebase for drugs, drug actions and drug targets","volume":"36","author":"Wishart","year":"2007","journal-title":"Nucleic Acids Res"},{"key":"2023051607133472000_btab548-B54","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: a major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023051607133472000_btab548-B55","doi-asserted-by":"crossref","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","article-title":"Prediction of drug\u2013target interaction networks from the integration of chemical and genomic spaces","volume":"24","author":"Yamanishi","year":"2008","journal-title":"Bioinformatics"},{"key":"2023051607133472000_btab548-B56","author":"Zitnik","year":"2017"},{"key":"2023051607133472000_btab548-B57","author":"Zitnik","year":"2018"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab548\/40572293\/btab548.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/24\/4835\/50334767\/btab548.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/24\/4835\/50334767\/btab548.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T03:42:13Z","timestamp":1684208533000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/24\/4835\/6329632"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,7,28]]},"references-count":57,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2021,12,11]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab548","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.04.28.441733","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,12,15]]},"published":{"date-parts":[[2021,7,28]]}}}