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More often than not, a drug-like compound (ligand) can be promiscuous \u2013 it can interact with more than one target protein.<\/jats:p><jats:p>In recent years, in<jats:italic>in silico<\/jats:italic>target prediction methods the promiscuity issue has generally been approached computationally in three main ways: ligand-based methods; target-protein-based methods; and integrative schemes. In this study we confine attention to ligand-based target prediction machine learning approaches, commonly referred to as<jats:italic>target-fishing<\/jats:italic>.<\/jats:p><jats:p>The<jats:italic>target-fishing<\/jats:italic>approaches that are currently ubiquitous in cheminformatics literature can be essentially viewed as single-label multi-classification schemes; these approaches inherently bank on the single target paradigm assumption that a ligand can zero in on one single target. In order to address the ligand promiscuity issue, one might be able to cast<jats:italic>target-fishing<\/jats:italic>as a multi-label multi-class classification problem. For illustrative and comparison purposes, single-label and multi-label Na\u00efve Bayes classification models (denoted here by SMM and MMM, respectively) for<jats:italic>target-fishing<\/jats:italic>were implemented. The models were constructed and tested on 65,587 compounds\/ligands and 308 targets retrieved from the ChEMBL17 database.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>On classifying 3,332 test multi-label (promiscuous) compounds, SMM and MMM performed differently. At the 0.05 significance level, a Wilcoxon signed rank test performed on the paired target predictions yielded by SMM and MMM for the test ligands gave a p-value\u2009&lt;\u20095.1\u2009\u00d7\u200910<jats:sup>\u221294<\/jats:sup>and test statistics value of 6.8\u2009\u00d7\u200910<jats:sup>5<\/jats:sup>, in favour of MMM. The two models performed differently when tested on four datasets comprising single-label (non-promiscuous) compounds; McNemar\u2019s test yielded<jats:italic>\u03c7<\/jats:italic><jats:sup>2<\/jats:sup>values of 15.657, 16.500 and 16.405 (with corresponding p-values of 7.594\u2009\u00d7\u200910<jats:sup>\u221205<\/jats:sup>, 4.865\u2009\u00d7\u200910<jats:sup>\u221205<\/jats:sup>and 5.115\u2009\u00d7\u200910<jats:sup>\u221205<\/jats:sup>), respectively, for three test sets, in favour of MMM. The models performed similarly on the fourth set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The target prediction results obtained in this study indicate that multi-label multi-class approaches are more apt than the ubiquitous single-label multi-class schemes when it comes to the application of ligand-based classifiers to<jats:italic>target-fishing<\/jats:italic>.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s13321-015-0071-9","type":"journal-article","created":{"date-parts":[[2015,5,29]],"date-time":"2015-05-29T08:33:57Z","timestamp":1432888437000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A multi-label approach to target prediction taking ligand promiscuity into account"],"prefix":"10.1186","volume":"7","author":[{"given":"Avid M","family":"Afzal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamse Y","family":"Mussa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard E","family":"Turner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert C","family":"Glen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,5,30]]},"reference":[{"key":"71_CR1","doi-asserted-by":"publisher","first-page":"3399","DOI":"10.1021\/ci400219z","volume":"53","author":"MC Cobanoglu","year":"2013","unstructured":"Cobanoglu MC, Liu C, Hu F, Oltvai ZN, Bahar I. 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