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dc.contributor.authorMavridis, Lazaros
dc.contributor.authorMitchell, John B. O.
dc.identifier.citationMavridis , L & Mitchell , J B O 2013 , ' Predicting the protein targets for athletic performance-enhancing substances ' , Journal of Cheminformatics , vol. 5 , no. 31 , 31 .
dc.identifier.otherPURE: 57886905
dc.identifier.otherPURE UUID: 92cb23d2-6cc3-439c-8aa3-ab54ad390b09
dc.identifier.otherScopus: 84880054720
dc.identifier.otherORCID: /0000-0002-0379-6097/work/34033397
dc.description.abstractBackground: The World Anti-Doping Agency (WADA) publishes the Prohibited List, a manually compiled international standard of substances and methods prohibited in-competition, out-of-competition and in particular sports. It would be ideal to be able to identify all substances that have one or more performance-enhancing pharmacological actions in an automated, fast and cost effective way. Here, we use experimental data derived from the ChEMBL database (~7,000,000 activity records for 1,300,000 compounds) to build a database model that takes into account both structure and experimental information, and use this database to predict both on-target and off-target interactions between these molecules and targets relevant to doping in sport. Results: The ChEMBL database was screened and eight well populated categories of activities (Ki, Kd, EC50, ED50, activity, potency, inhibition and IC50) were used for a rule-based filtering process to define the labels “active” or “inactive”. The “active” compounds for each of the ChEMBL families were thereby defined and these populated our bioactivity-based filtered families. A structure-based clustering step was subsequently performed in order to split families with more than one distinct chemical scaffold. This produced refined families, whose members share both a common chemical scaffold and bioactivity against a common target in ChEMBL. Conclusions: We have used the Parzen-Rosenblatt machine learning approach to test whether compounds in ChEMBL can be correctly predicted to belong to their appropriate refined families. Validation tests using the refined families gave a significant increase in predictivity compared with the filtered or with the original families. Out of 61,660 queries in our Monte Carlo cross-validation, belonging to 19,639 refined families, 41,300 (66.98%) had the parent family as the top prediction and 53,797 (87.25%) had the parent family in the top four hits. Having thus validated our approach, we used it to identify the protein targets associated with the WADA prohibited classes. For compounds where we do not have experimental data, we use their computed patterns of interaction with protein targets to make predictions of bioactivity. We hope that other groups will test these predictions experimentally in the future.
dc.relation.ispartofJournal of Cheminformaticsen
dc.rights© 2013 Mavridis and Mitchell; licensee Chemistry Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.subjectProtein target predictionen
dc.subjectMachine learningen
dc.subjectSide effectsen
dc.subjectMulti-label predictionen
dc.subjectDrugs in sporten
dc.subjectDrug repurposingen
dc.subjectQD Chemistryen
dc.titlePredicting the protein targets for athletic performance-enhancing substancesen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Chemistryen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.contributor.institutionUniversity of St Andrews. EaSTCHEMen
dc.description.statusPeer revieweden

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