Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space

Abstract Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively...

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Autores principales:	Antonio Peón, Stefan Naulaerts, Pedro J. Ballester
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d3c61d3ca21340c9835c42f70e3c47ad
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spelling	oai:doaj.org-article:d3c61d3ca21340c9835c42f70e3c47ad2021-12-02T15:04:55ZPredicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space10.1038/s41598-017-04264-w2045-2322https://doaj.org/article/d3c61d3ca21340c9835c42f70e3c47ad2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04264-whttps://doaj.org/toc/2045-2322Abstract Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. 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 IC50 10 µM. 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.Antonio PeónStefan NaulaertsPedro J. BallesterNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Antonio Peón Stefan Naulaerts Pedro J. Ballester Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space
description	Abstract Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. 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 IC50 10 µM. 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.
format	article
author	Antonio Peón Stefan Naulaerts Pedro J. Ballester
author_facet	Antonio Peón Stefan Naulaerts Pedro J. Ballester
author_sort	Antonio Peón
title	Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space
title_short	Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space
title_full	Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space
title_fullStr	Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space
title_full_unstemmed	Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space
title_sort	predicting the reliability of drug-target interaction predictions with maximum coverage of target space
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/d3c61d3ca21340c9835c42f70e3c47ad
work_keys_str_mv	AT antoniopeon predictingthereliabilityofdrugtargetinteractionpredictionswithmaximumcoverageoftargetspace AT stefannaulaerts predictingthereliabilityofdrugtargetinteractionpredictionswithmaximumcoverageoftargetspace AT pedrojballester predictingthereliabilityofdrugtargetinteractionpredictionswithmaximumcoverageoftargetspace
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Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space

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