{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T07:13:15Z","timestamp":1776323595530,"version":"3.50.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":3015,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug\u2013target interactions efficiently.<\/jats:p>\n               <jats:p>Results: In this article, we characterize four classes of drug\u2013target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug\u2013target interaction network topology. We then develop new statistical methods to predict unknown drug\u2013target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug\u2013target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call \u2018pharmacological space\u2019. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug\u2013target interaction networks. Our comprehensively predicted drug\u2013target interaction networks enable us to suggest many potential drug\u2013target interactions and to increase research productivity toward genomic drug discovery.<\/jats:p>\n               <jats:p>Availability: Softwares are available upon request.<\/jats:p>\n               <jats:p>Contact: \u00a0Yoshihiro.Yamanishi@ensmp.fr<\/jats:p>\n               <jats:p>Supplementary information: Datasets and all prediction results are available at http:\/\/web.kuicr.kyoto-u.ac.jp\/supp\/yoshi\/drugtarget\/.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btn162","type":"journal-article","created":{"date-parts":[[2008,6,27]],"date-time":"2008-06-27T07:43:13Z","timestamp":1214552593000},"page":"i232-i240","source":"Crossref","is-referenced-by-count":992,"title":["Prediction of drug\u2013target interaction networks from the integration of chemical and genomic spaces"],"prefix":"10.1093","volume":"24","author":[{"given":"Yoshihiro","family":"Yamanishi","sequence":"first","affiliation":[{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011 and 2Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639, Japan"}]},{"given":"Michihiro","family":"Araki","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011 and 2Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639, Japan"}]},{"given":"Alex","family":"Gutteridge","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011 and 2Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639, Japan"}]},{"given":"Wataru","family":"Honda","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011 and 2Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639, Japan"}]},{"given":"Minoru","family":"Kanehisa","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011 and 2Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639, Japan"},{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011 and 2Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639, Japan"}]}],"member":"286","published-online":{"date-parts":[[2008,7,1]]},"reference":[{"key":"2023020210380580200_B1","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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