{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T21:45:16Z","timestamp":1780782316807,"version":"3.54.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2636,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: \u00a0In silico prediction of drug\u2013target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug\u2013target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug\u2013target interactions.<\/jats:p>\n               <jats:p>Results: We propose a novel supervised inference method to predict unknown drug\u2013target interactions, represented as a bipartite graph. We use this method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein. This gives two independent predictions for each putative drug\u2013target interaction, which we show can be combined to give a definitive prediction for each interaction. We demonstrate the excellent performance of the proposed method in the prediction of four classes of drug\u2013target interaction networks involving enzymes, ion channels, G protein-coupled receptors (GPCRs) and nuclear receptors in human. This enables us to suggest a number of new potential drug\u2013target interactions.<\/jats:p>\n               <jats:p>Availability: An implementation of the proposed algorithm is available upon request from the authors. Datasets and all prediction results are available at http:\/\/cbio.ensmp.fr\/~yyamanishi\/bipartitelocal\/.<\/jats:p>\n               <jats:p>Contact: \u00a0kevbleakley@gmail.com<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp433","type":"journal-article","created":{"date-parts":[[2009,7,16]],"date-time":"2009-07-16T00:49:46Z","timestamp":1247705386000},"page":"2397-2403","source":"Crossref","is-referenced-by-count":557,"title":["Supervised prediction of drug\u2013target interactions using bipartite local models"],"prefix":"10.1093","volume":"25","author":[{"given":"Kevin","family":"Bleakley","sequence":"first","affiliation":[{"name":"1 Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honor\u00e9, F-77305 Fontainebleau Cedex, 2Institut Curie and 3INSERM, U900, F-75248, Paris, France"},{"name":"1 Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honor\u00e9, F-77305 Fontainebleau Cedex, 2Institut Curie and 3INSERM, U900, F-75248, Paris, France"},{"name":"1 Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honor\u00e9, F-77305 Fontainebleau Cedex, 2Institut Curie and 3INSERM, U900, F-75248, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshihiro","family":"Yamanishi","sequence":"additional","affiliation":[{"name":"1 Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honor\u00e9, F-77305 Fontainebleau Cedex, 2Institut Curie and 3INSERM, U900, F-75248, Paris, France"},{"name":"1 Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honor\u00e9, F-77305 Fontainebleau Cedex, 2Institut Curie and 3INSERM, U900, F-75248, Paris, France"},{"name":"1 Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honor\u00e9, F-77305 Fontainebleau Cedex, 2Institut Curie and 3INSERM, U900, F-75248, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2009,7,15]]},"reference":[{"key":"2023013112115744900_B1","doi-asserted-by":"crossref","first-page":"i57","DOI":"10.1093\/bioinformatics\/btm204","article-title":"Supervised reconstruction of biological networks with local models","volume":"23","author":"Bleakley","year":"2007","journal-title":"Bioinformatics"},{"key":"2023013112115744900_B2","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":"2023013112115744900_B3","author":"Chang","year":"2001","journal-title":"LIBSVM: a Library for Support Vector Machines."},{"key":"2023013112115744900_B4","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/nbt1273","article-title":"Structure-based maximal affinity model predicts small-molecule druggability","volume":"25","author":"Cheng","year":"2007","journal-title":"Nat. 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