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Peón A, Naulaerts S, Ballester PJ. Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space. Sci Rep. 2017;7(1):3820doi: 10.1038/s41598-017-04264-w.
Peón, A., Naulaerts, S., & Ballester, P. J. (2017). Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space. Scientific reports, 7(1), 3820. https://doi.org/10.1038/s41598-017-04264-w
Peón, Antonio, et al. "Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space." Scientific reports vol. 7,1 (2017): 3820. doi: https://doi.org/10.1038/s41598-017-04264-w
Peón A, Naulaerts S, Ballester PJ. Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space. Sci Rep. 2017 Jun 19;7(1):3820. doi: 10.1038/s41598-017-04264-w. PMID: 28630414; PMCID: PMC5476590.
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Sci Rep. 2017 Jun 19;7(1):3820. doi: 10.1038/s41598-017-04264-w.

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

Scientific reports

Antonio Peón, Stefan Naulaerts, Pedro J Ballester

Affiliations

  1. Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, Marseille, F-13009, France.
  2. CNRS, UMR7258, Marseille, F-13009, France.
  3. Institut Paoli-Calmettes, Marseille, F-13009, France.
  4. Aix-Marseille University, UM 105, F-13284, Marseille, France.
  5. Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, Marseille, F-13009, France. [email protected].
  6. CNRS, UMR7258, Marseille, F-13009, France. [email protected].
  7. Institut Paoli-Calmettes, Marseille, F-13009, France. [email protected].
  8. Aix-Marseille University, UM 105, F-13284, Marseille, France. [email protected].

PMID: 28630414 PMCID: PMC5476590 DOI: 10.1038/s41598-017-04264-w

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 IC

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