Assessing the druggability of protein-protein interactions by a supervised machine-learning method.

ABSTRACT:

Background

Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM).

Results

To assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previo

SUBMITTER: Sugaya N 

PROVIDER: S-EPMC2739204 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature