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A multi-label approach to target prediction taking ligand promiscuity into account

Authors
  • Afzal, Avid M1
  • Mussa, Hamse Y1
  • Turner, Richard E2
  • Bender, Andreas1
  • Glen, Robert C1
  • 1 Centre for Molecular Informatics, University of Cambridge, Department of Chemistry, Lensfield Road, Cambridge, CB2 1EW, UK , Cambridge (United Kingdom)
  • 2 Computational and Biological Learning Lab, University of Cambridge, Department of Engineering, Trumpington Street, Cambridge, CB2 1PZ, UK , Cambridge (United Kingdom)
Type
Published Article
Journal
Journal of Cheminformatics
Publisher
Springer International Publishing
Publication Date
May 30, 2015
Volume
7
Issue
1
Identifiers
DOI: 10.1186/s13321-015-0071-9
Source
Springer Nature
Keywords
License
Yellow

Abstract

BackgroundAccording to Cobanoglu et al., it is now widely acknowledged that the single target paradigm (one protein/target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous – it can interact with more than one target protein.In recent years, in in silico 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 target-fishing.The target-fishing 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 target-fishing as a multi-label multi-class classification problem. For illustrative and comparison purposes, single-label and multi-label Naïve Bayes classification models (denoted here by SMM and MMM, respectively) for target-fishing were implemented. The models were constructed and tested on 65,587 compounds/ligands and 308 targets retrieved from the ChEMBL17 database.ResultsOn 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 < 5.1 × 10−94 and test statistics value of 6.8 × 105, in favour of MMM. The two models performed differently when tested on four datasets comprising single-label (non-promiscuous) compounds; McNemar’s test yielded χ2 values of 15.657, 16.500 and 16.405 (with corresponding p-values of 7.594 × 10−05, 4.865 × 10−05 and 5.115 × 10−05), respectively, for three test sets, in favour of MMM. The models performed similarly on the fourth set.ConclusionsThe 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 target-fishing.

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