||September 24, 2008
||Dr. Yoshihiro Yamanishi, Mines ParisTech, Curie Institute, and Inserm U900
||Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.
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 study, 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.