Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework

Abstract

Motivation: In silico prediction of drug-target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently.

Results: In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug-target interaction networks, and show that drug-target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug-target interactions from chemical, genomic and pharmacological data on a large scale. The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug-target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference. The originality of the proposed method lies in the prediction of potential pharmacological similarity for any drug candidate compounds and in the integration of chemical, genomic and pharmacological data in a unified framework. In the results, we make predictions for four classes of important drug-target interactions involving enzymes, ion channels, GPCRs and nuclear receptors. 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.

Supplementary information: Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/pharmaco/.

Availability: Softwares are available upon request.

Fig. 1.

Scatter-plots of pharmacological effect similarity scores and chemical structure similarity scores for drugs targeting enzyme, ion channel, GPCR and nuclear receptor, respectively.

Fig. 2.

Distributions of chemical structure similarity scores (top four panels) and pharmacological effect similarity scores (bottom four panels) against the network distance of drugs targeting enzymes, ion channels, GPCRs and nuclear receptors.

Fig. 3.

Barplot of AUC score for the five tag groups (caution, interaction, patient, pharmaceutical effect and property) and their combination.

Fig. 4.

Examples of the proposed drug–target interactions. Four boxes in the center of the figure are the target proteins, and bold lines indicate the known drug–target interactions. Solid lines represent the proposed interactions based on the resemblance to the known interacting drugs indicated by the dashed lines. Black stars indicate the interactions predicted by the previous method. White stars indicate the interactions additionally predicted by the proposed method.