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


ABSTRACT: 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. 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 http://cbio.ensmp.fr/~yyamanishi/l1binary/ .

SUBMITTER: Tabei Y 

PROVIDER: S-EPMC3436839 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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

Tabei Yasuo Y   Pauwels Edouard E   Stoven Véronique V   Takemoto Kazuhiro K   Yamanishi Yoshihiro Y  

Bioinformatics (Oxford, England) 20120901 18


<h4>Motivation</h4>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.<h4>Results</h4>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 interacti  ...[more]

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