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.Contact: yoshihiro.yamanishi@ensmp.fr

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

“…For a type of chemical substructure, the substructure similarity subsim ( , ) of drugs and can be computed by the weighted cosine correlation coefficient based on the substructure information [27].…”

“…Now it is possible for us to quickly and inexpensively identify potential DTIs and repurpose existing drugs [23][24][25][26][27] through the developments of computational methods. These methods are mainly divided into three categories, including basic network-based models, machine learningbased models, and other approaches based on similarity [28].…”

Mining Multi Drug-Pathways via A Probabilistic Heterogeneous Network Multi-label Classifier

“…Gönen [24] proposed a novel Bayesian formulation that combines dimensionality reduction, matrix factorization and binary classification for predicting drug-target interaction networks using only chemical similarity between drug compounds and genomic similarity between target proteins. Yamanishi et al [25] investigated 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. In addition, they developed a new method to predict unknown drug-target interactions from chemical, genomic and pharmacological data on a large scale.…”

Protein-Protein Interaction Networks: Structures, Evolution, and Application to Drug Design

“…The chemical structures of drugs have been used to predict their adverse side effects and target proteins , Yamanishi et al 2010. developed a large-scale database of adverse side effects of drugs.…”