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

Bioinformatics
Yoshihiro YamanishiMinoru Kanehisa

Abstract

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. 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 networ...Continue Reading

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Citations

Mar 4, 2011·Interdisciplinary Sciences, Computational Life Sciences·Lun YangLin He
Sep 22, 2009·Journal of Chemical Information and Modeling·Nikil Wale, George Karypis
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Methods Mentioned

BETA
interaction profiles

Software Mentioned

SIMCOMP

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