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Bioinformatics. 2008 Jul 1;24(13):i232-40. doi: 10.1093/bioinformatics/btn162.

Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.

Author information

1
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. Yoshihiro.Yamanishi@ensmp.fr

Abstract

MOTIVATION:

The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently.

RESULTS:

In this article, we characterize four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug-target interaction network topology. We then develop new statistical methods to predict unknown drug-target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug-target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call 'pharmacological space'. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug-target interaction networks. Our comprehensively predicted drug-target interaction networks enable us to suggest many potential drug-target interactions and to increase research productivity toward genomic drug discovery.

AVAILABILITY:

Softwares are available upon request.

SUPPLEMENTARY INFORMATION:

Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.

PMID:
18586719
PMCID:
PMC2718640
DOI:
10.1093/bioinformatics/btn162
[Indexed for MEDLINE]
Free PMC Article

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