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


BACKGROUND
Softwares are available upon request.


BACKGROUND
Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.
Yamanishi
Yoshihiro
Y
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. Yoshihiro.Yamanishi@ensmp.fr
Araki
Michihiro
M
Gutteridge
Alex
A
Honda
Wataru
W
Kanehisa
Minoru
M
D001665:Binding Sites; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D016503:Drug Delivery Systems / Q000379:methods*; D008956:Models, Chemical; D008957:Models, Genetic; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D018511:Systems Integration
D004364:Pharmaceutical Preparations; D011506:Proteins