{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T04:25:59Z","timestamp":1745382359098},"reference-count":21,"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":1490,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,9,15]]},"abstract":"Abstract<\/jats:title>\n Motivation: Unexpected drug activities derived from off-targets are usually undesired and harmful; however, they can occasionally be beneficial for different therapeutic indications. There are many uncharacterized drugs whose target proteins (including the primary target and off-targets) remain unknown. The identification of all potential drug targets has become an important issue in drug repositioning to reuse known drugs for new therapeutic indications.<\/jats:p>\n Results: We defined pharmacological similarity for all possible drugs using the US Food and Drug Administration's (FDA's) adverse event reporting system (AERS) and developed a new method to predict unknown drug\u2013target interactions on a large scale from the integration of pharmacological similarity of drugs and genomic sequence similarity of target proteins in the framework of a pharmacogenomic approach. The proposed method was applicable to a large number of drugs and it was useful especially for predicting unknown drug\u2013target interactions that could not be expected from drug chemical structures. We made a comprehensive prediction for potential off-targets of 1874 drugs with known targets and potential target profiles of 2519 drugs without known targets, which suggests many potential drug\u2013target interactions that were not predicted by previous chemogenomic or pharmacogenomic approaches.<\/jats:p>\n Availability: Softwares are available upon request.<\/jats:p>\n Contact: \u00a0yamanishi@bioreg.kyushu-u.ac.jp<\/jats:p>\n Supplementary Information: Datasets and all results are available at http:\/\/cbio.ensmp.fr\/~yyamanishi\/aers\/.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts413","type":"journal-article","created":{"date-parts":[[2012,9,7]],"date-time":"2012-09-07T20:35:22Z","timestamp":1347050122000},"page":"i611-i618","source":"Crossref","is-referenced-by-count":113,"title":["Drug target prediction using adverse event report systems: a pharmacogenomic approach"],"prefix":"10.1093","volume":"28","author":[{"given":"Masataka","family":"Takarabe","sequence":"first","affiliation":[{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan and"}]},{"given":"Masaaki","family":"Kotera","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan and"}]},{"given":"Yosuke","family":"Nishimura","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan and"}]},{"given":"Susumu","family":"Goto","sequence":"additional","affiliation":[{"name":"1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan and"}]},{"given":"Yoshihiro","family":"Yamanishi","sequence":"additional","affiliation":[{"name":"2 Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan"}]}],"member":"286","published-online":{"date-parts":[[2012,9,3]]},"reference":[{"key":"2023012513071816600_B1","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1089\/cmb.2010.0255","article-title":"An algorithmic framework for predicting side-effects of drugs","volume":"18","author":"Atias","year":"2011","journal-title":"J. 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