{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T23:36:28Z","timestamp":1786318588444,"version":"3.56.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T00:00:00Z","timestamp":1564617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011419","name":"SFI","doi-asserted-by":"publisher","award":["SFI\/12\/RC\/2289"],"award-info":[{"award-number":["SFI\/12\/RC\/2289"]}],"id":[{"id":"10.13039\/100011419","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Computational approaches for predicting drug\u2013target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and\/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a novel computational approach for predicting drug target proteins. The approach is based on formulating the problem as a link prediction in knowledge graphs (robust, machine-readable representations of networked knowledge). We use biomedical knowledge bases to create a knowledge graph of entities connected to both drugs and their potential targets. We propose a specific knowledge graph embedding model, TriModel, to learn vector representations (i.e. embeddings) for all drugs and targets in the created knowledge graph. These representations are consequently used to infer candidate drug target interactions based on their scores computed by the trained TriModel model. We have experimentally evaluated our method using computer simulations and compared it to five existing models. This has shown that our approach outperforms all previous ones in terms of both area under ROC and precision\u2013recall curves in standard benchmark tests.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The data, predictions and models are available at: drugtargets.insight-centre.org.<\/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\/btz600","type":"journal-article","created":{"date-parts":[[2019,7,27]],"date-time":"2019-07-27T19:09:13Z","timestamp":1564254553000},"page":"603-610","source":"Crossref","is-referenced-by-count":199,"title":["Discovering protein drug targets using knowledge graph embeddings"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2659-2406","authenticated-orcid":false,"given":"Sameh K","family":"Mohamed","sequence":"first","affiliation":[{"name":"Data Science Institute, College of Engineering and Informatics"},{"name":"Insight Centre for Data Analytics , NUI Galway, Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V\u00edt","family":"Nov\u00e1\u010dek","sequence":"additional","affiliation":[{"name":"Data Science Institute, College of Engineering and Informatics"},{"name":"Insight Centre for Data Analytics , NUI Galway, Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aayah","family":"Nounu","sequence":"additional","affiliation":[{"name":"MRC Integrative Epidemiology Unit , Bristol Medical School, University of Bristol, Bristol, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,8,1]]},"reference":[{"key":"2023013112062300200_btz600-B1","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","author":"Bordes","year":"2013"},{"key":"2023013112062300200_btz600-B2","doi-asserted-by":"crossref","first-page":"909.","DOI":"10.1038\/nrd3845","article-title":"Reducing safety-related drug attrition: the use of in vitro pharmacological profiling","volume":"11","author":"Bowes","year":"2012","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023013112062300200_btz600-B3","first-page":"1","article-title":"Prediction of chemical\u2013protein interactions network with weighted network-based inference method","volume":"7","author":"Cheng","year":"2012","journal-title":"PLoS One"},{"key":"2023013112062300200_btz600-B4","doi-asserted-by":"crossref","first-page":"e1002503.","DOI":"10.1371\/journal.pcbi.1002503","article-title":"Prediction of drug\u2013target interactions and drug repositioning via network-based inference","volume":"8","author":"Cheng","year":"2012","journal-title":"PLoS Comput. Biol"},{"key":"2023013112062300200_btz600-B5","doi-asserted-by":"crossref","first-page":"D158","DOI":"10.1093\/nar\/gkw1099","article-title":"Uniprot: the universal protein knowledgebase","volume":"45","author":"Consortium","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B6","doi-asserted-by":"crossref","first-page":"833.","DOI":"10.1038\/nrd3869","article-title":"Drug repositioning for Alzheimer\u2019s disease","volume":"11","author":"Corbett","year":"2012","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023013112062300200_btz600-B7","author":"Davis","year":"2006"},{"key":"2023013112062300200_btz600-B8","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1126\/science.287.5460.1960","article-title":"Drug discovery: a historical perspective","volume":"287","author":"Drews","year":"2000","journal-title":"Science"},{"key":"2023013112062300200_btz600-B9","author":"Dumontier","year":"2014"},{"key":"2023013112062300200_btz600-B10","author":"Glorot","year":"2010"},{"key":"2023013112062300200_btz600-B11","doi-asserted-by":"crossref","first-page":"D919","DOI":"10.1093\/nar\/gkm862","article-title":"Supertarget and matador: resources for exploring drug\u2013target relationships","volume":"36","author":"G\u00fcnther","year":"2007","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B12","doi-asserted-by":"crossref","first-page":"40376.","DOI":"10.1038\/srep40376","article-title":"Predicting drug\u2013target interactions by dual-network integrated logistic matrix factorization","volume":"7","author":"Hao","year":"2017","journal-title":"Sci. Rep"},{"key":"2023013112062300200_btz600-B13","doi-asserted-by":"crossref","first-page":"D1113","DOI":"10.1093\/nar\/gkr912","article-title":"Supertarget goes quantitative: update on drug\u2013target interactions","volume":"40","author":"Hecker","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B14","doi-asserted-by":"crossref","first-page":"e26726","DOI":"10.7554\/eLife.26726","article-title":"Systematic integration of biomedical knowledge prioritizes drugs for repurposing","volume":"6","author":"Himmelstein","year":"2017","journal-title":"eLife"},{"key":"2023013112062300200_btz600-B15","doi-asserted-by":"crossref","first-page":"D354","DOI":"10.1093\/nar\/gkj102","article-title":"From genomics to chemical genomics: new developments in KEGG","volume":"34","author":"Kanehisa","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B16","doi-asserted-by":"crossref","first-page":"D353","DOI":"10.1093\/nar\/gkw1092","article-title":"KEGG: new perspectives on genomes, pathways, diseases and drugs","volume":"45","author":"Kanehisa","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B17","author":"Lacroix","year":"2018"},{"key":"2023013112062300200_btz600-B18","doi-asserted-by":"crossref","first-page":"167","DOI":"10.3233\/SW-140134","article-title":"DBpedia \u2013 a large-scale, multilingual knowledge base extracted from wikipedia","volume":"6","author":"Lehmann","year":"2014","journal-title":"Semantic Web J"},{"key":"2023013112062300200_btz600-B19","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/S0140-6736(17)30770-5","article-title":"Age-specific risks, severity, time course, and outcome of bleeding on long-term antiplatelet treatment after vascular events: a population-based cohort study","volume":"390","author":"Li","year":"2017","journal-title":"Lancet"},{"key":"2023013112062300200_btz600-B20","doi-asserted-by":"crossref","first-page":"38860.","DOI":"10.1038\/srep38860","article-title":"Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem","volume":"6","author":"Lim","year":"2016","journal-title":"Sci. Rep"},{"key":"2023013112062300200_btz600-B21","doi-asserted-by":"crossref","first-page":"i221","DOI":"10.1093\/bioinformatics\/btv256","article-title":"Improving compound\u2013protein interaction prediction by building up highly credible negative samples","volume":"31","author":"Liu","year":"2015","journal-title":"Bioinformatics"},{"key":"2023013112062300200_btz600-B22","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1561\/1500000016","article-title":"Learning to rank for information retrieval","volume":"3","author":"Liu","year":"2007","journal-title":"Found. Trends Inf. Retrieval"},{"key":"2023013112062300200_btz600-B23","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1093\/bioinformatics\/bts670","article-title":"Drug\u2013target interaction prediction by learning from local information and neighbors","volume":"29","author":"Mei","year":"2013","journal-title":"Bioinformatics"},{"key":"2023013112062300200_btz600-B24","doi-asserted-by":"crossref","first-page":"D351","DOI":"10.1093\/nar\/gky1100","article-title":"Interpro in 2019: improving coverage, classification and access to protein sequence annotations","volume":"47","author":"Mitchell","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B25","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1093\/bib\/bbx099","article-title":"Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models","volume":"20","author":"Mu\u00f1oz","year":"2019","journal-title":"Brief. Bioinf"},{"key":"2023013112062300200_btz600-B26","doi-asserted-by":"crossref","first-page":"46.","DOI":"10.1186\/s12859-016-0890-3","article-title":"A multiple kernel learning algorithm for drug\u2013target interaction prediction","volume":"17","author":"Nascimento","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2023013112062300200_btz600-B27","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","article-title":"A review of relational machine learning for knowledge graphs","volume":"104","author":"Nickel","year":"2016","journal-title":"Proc. IEEE"},{"key":"2023013112062300200_btz600-B28","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1093\/bioinformatics\/btx731","article-title":"DDR: efficient computational method to predict drug\u2013target interactions using graph mining and machine learning approaches","volume":"34","author":"Olayan","year":"2018","journal-title":"Bioinformatics"},{"key":"2023013112062300200_btz600-B29","doi-asserted-by":"crossref","first-page":"D380.","DOI":"10.1093\/nar\/gkw952","article-title":"Brenda in 2017: new perspectives and new tools in Brenda","volume":"45","author":"Placzek","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B30","author":"Reddi","year":"2018"},{"key":"2023013112062300200_btz600-B31","doi-asserted-by":"crossref","first-page":"e00235.","DOI":"10.1002\/prp2.235","article-title":"Mitochondrial fission\u2014a drug target for cytoprotection or cytodestruction?","volume":"4","author":"Rosdah","year":"2016","journal-title":"Pharmacol. Res. Perspect"},{"key":"2023013112062300200_btz600-B32","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1016\/S0140-6736(10)61543-7","article-title":"Long-term effect of aspirin on colorectal cancer incidence and mortality: 20-year follow-up of five randomised trials","volume":"376","author":"Rothwell","year":"2010","journal-title":"Lancet"},{"key":"2023013112062300200_btz600-B33","doi-asserted-by":"crossref","first-page":"431D","DOI":"10.1093\/nar\/gkh081","article-title":"Brenda, the enzyme database: updates and major new developments","volume":"32","author":"Schomburg","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B34","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.cbpa.2008.01.022","article-title":"Proteomic methods for drug target discovery","volume":"12","author":"Sleno","year":"2008","journal-title":"Curr. Opin. Chem. Biol"},{"key":"2023013112062300200_btz600-B35","doi-asserted-by":"crossref","DOI":"10.1002\/0470015535","volume-title":"Drug Discovery: A History","author":"Sneader","year":"2005"},{"key":"2023013112062300200_btz600-B36","doi-asserted-by":"crossref","first-page":"891.","DOI":"10.1038\/nrd2410","article-title":"Target deconvolution strategies in drug discovery","volume":"6","author":"Terstappen","year":"2007","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023013112062300200_btz600-B37","author":"Trouillon","year":"2016"},{"key":"2023013112062300200_btz600-B38","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":"2023013112062300200_btz600-B39","doi-asserted-by":"crossref","first-page":"D668","DOI":"10.1093\/nar\/gkj067","article-title":"Drugbank: a comprehensive resource for in silico drug discovery and exploration","volume":"34","author":"Wishart","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B40","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":"2008","journal-title":"Nucleic Acids Res"},{"key":"2023013112062300200_btz600-B41","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1146\/annurev-pharmtox-010611-134630","article-title":"Novel computational approaches to polypharmacology as a means to define responses to individual drugs","volume":"52","author":"Xie","year":"2012","journal-title":"Annu. Rev. Pharmacol. Toxicol"},{"key":"2023013112062300200_btz600-B42","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":"2023013112062300200_btz600-B43","article-title":"Embedding entities and relations for learning and inference in knowledge bases","author":"Yang","year":"2015"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btz600\/29026304\/btz600.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/2\/603\/48990858\/btz600.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/2\/603\/48990858\/btz600.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T21:22:36Z","timestamp":1675200156000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/2\/603\/5542390"}},"subtitle":[],"editor":[{"given":"Lenore","family":"Cowen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2019,8,1]]},"references-count":43,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,1,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btz600","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,1,15]]},"published":{"date-parts":[[2019,8,1]]}}}