Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers

Y. Tabei, E. Pauwels, V. Stoven, K. Takemoto, Y. Yamanishi
2012 Bioinformatics  
Motivation: Drug effects are mainly caused by the interactions between drug molecules and their target proteins including primary targets and off-targets. Identification of the molecular mechanisms behind overall drug-target interactions is crucial in the drug design process. Results: We develop a classifier-based approach to identify chemogenomic features (the underlying associations between drug chemical substructures and protein domains) that are involved in drug-target interaction networks.
more » ... We propose a novel algorithm for extracting informative chemogenomic features by using L 1 regularized classifiers over the tensor product space of possible drug-target pairs. It is shown that the proposed method can extract a very limited number of chemogenomic features without loosing the performance of predicting drug-target interactions and the extracted features are biologically meaningful. The extracted substructure-domain association network enables us to suggest ligand chemical fragments specific for each protein domain and ligand core substructures important for a wide range of protein families. Availability: Softwares are available at the supplemental website. Contact: yamanishi@bioreg.kyushu-u.ac.jp Supplementary Information: Datasets and all results are available at
doi:10.1093/bioinformatics/bts412 pmid:22962471 pmcid:PMC3436839 fatcat:7dgumrgpt5g5zc4by25z6ygpzu