Prediction of drug–target interaction networks from the integration of chemical and genomic spaces
1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011 and 2Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai Minato-ku, Tokyo 108-8639, Japan
*To whom correspondence should be addressed.
 |
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.
Contact: Yoshihiro.Yamanishi{at}ensmp.fr
Supplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.
Present address: Centre for Computational Biology, Ecole des Mines de Paris, 35 rue Saint Honore, 77305 Fontainebleau Cedex, France.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  H. Kashima, Y. Yamanishi, T. Kato, M. Sugiyama, and K. Tsuda Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach Bioinformatics, November 15, 2009; 25(22): 2962 - 2968. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Bleakley and Y. Yamanishi Supervised prediction of drug-target interactions using bipartite local models Bioinformatics, September 15, 2009; 25(18): 2397 - 2403. [Abstract] [Full Text] [PDF]
|
|