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Bioinformatics. 2012 Sep 15;28(18):i487-i494. doi: 10.1093/bioinformatics/bts412.

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

Author information

1
ERATO Minato Project, Japan Science and Technology Agency, Sapporo 060-0814, Japan.

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/ .

PMID:
22962471
PMCID:
PMC3436839
DOI:
10.1093/bioinformatics/bts412
[Indexed for MEDLINE]
Free PMC Article
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