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

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/.

Fig. 1.

An illustration of the proposed method.

Fig. 2.

Degree distributions for drugs and target proteins. The top four panels show the histograms of the degree of drugs targeting enzyme, ion channel, GPCR and nuclear receptor, respectively. The bottom four panels show the histogram of the degree of the corresponding target proteins.

Fig. 3.

Box-plots of chemical structure similarities between drugs and sequence similarities between target proteins against the network distance for enzyme, ion channel, GPCR, and nuclear receptor, respectively. The top four panels show the box-plot of the SIMCOMP scores between drugs against the network distance (d=0, 2, 4, 6, …). The bottom four panels show the box-plot of the normalized Smith–Waterman scores between target proteins against the network distance (d=0, 2, 4, 6, …). Note that the distance means the shortest path between objects (drugs or target proteins in each case) on the bipartite graph representation for the drug–target interaction network.

Fig. 4.

ROC curves of the bipartite graph learning method for four classes of drug–target interactions: enzymes, ion channels, GPCRs and nuclear receptors.

Fig. 5.

Predicted enzymes interaction network. Blue, red, light blue and orange nodes indicate known drugs, known targets, newly predicted compounds and newly predicted proteins, respectively. Gray and pink edges indicate known interactions and newly predicted interactions with 100 highest scores, respectively.