title: Chemogenomic approaches to infer drug-target interaction networks.
abstract: The identification of drug-target interactions from heterogeneous biological data is critical in the drug development. In this chapter, we review recently developed in silico chemogenomic approaches to infer unknown drug-target interactions from chemical information of drugs and genomic information of target proteins. We review several kernel-based statistical methods from two different viewpoints: binary classification and dimension reduction. In the results, we demonstrate the usefulness of the methods on the prediction of drug-target interactions from chemical structure data and genomic sequence data. We also discuss the characteristics of each method, and show some perspectives toward future research direction.
authors.affiliation: Institut Curie, Centre de recherche Biologie du developpement, U900 Unit of Bioinformatics and Computational Systems Biology of Cancer, Paris, France. yoshihiro.yamanishi@ensmp.fr
authors.forename: Yoshihiro
authors.initials: Y
authors.lastname: Yamanishi
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D055808:Drug Discovery / Q000379:methods*; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D011487:Protein Conformation; D015203:Reproducibility of Results