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Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate of the polypharmacology of drugs, which on average have 11.5 targets below IC<jats:sub>50<\/jats:sub> 10\u2009\u00b5M. The average performance achieved across clinical drugs is remarkable (0.348 precision and 0.423 recall, with large drug-dependent variability), especially given the unusually large coverage of the target space. Furthermore, we show how a sparse ligand-target bioactivity matrix to retrospectively validate target prediction methods could underestimate prospective performance. Lastly, we present and validate a first-in-kind score capable of accurately predicting the reliability of target predictions.<\/jats:p>","DOI":"10.1038\/s41598-017-04264-w","type":"journal-article","created":{"date-parts":[[2017,6,13]],"date-time":"2017-06-13T11:38:17Z","timestamp":1497353897000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space"],"prefix":"10.1038","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4803-988X","authenticated-orcid":false,"given":"Antonio","family":"Pe\u00f3n","sequence":"first","affiliation":[]},{"given":"Stefan","family":"Naulaerts","sequence":"additional","affiliation":[]},{"given":"Pedro J.","family":"Ballester","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,6,19]]},"reference":[{"key":"4264_CR1","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.cbpa.2012.12.022","volume":"17","author":"J Lee","year":"2013","unstructured":"Lee, J. & Bogyo, M. 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