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Bioinformatics. 2009 Sep 15;25(18):2397-403. doi: 10.1093/bioinformatics/btp433. Epub 2009 Jul 15.

Supervised prediction of drug-target interactions using bipartite local models.

Author information

  • 1Mines ParisTech, Centre for Computational Biology, Fontainebleau, France. kevbleakley@gmail.com

Abstract

MOTIVATION:

In silico prediction of drug-target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug-target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug-target interactions.

RESULTS:

We propose a novel supervised inference method to predict unknown drug-target interactions, represented as a bipartite graph. We use this method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein. This gives two independent predictions for each putative drug-target interaction, which we show can be combined to give a definitive prediction for each interaction. We demonstrate the excellent performance of the proposed method in the prediction of four classes of drug-target interaction networks involving enzymes, ion channels, G protein-coupled receptors (GPCRs) and nuclear receptors in human. This enables us to suggest a number of new potential drug-target interactions.

AVAILABILITY:

An implementation of the proposed algorithm is available upon request from the authors. Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/bipartitelocal/.

PMID:
19605421
[PubMed - indexed for MEDLINE]
PMCID:
PMC2735674
Free PMC Article
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