Prediction of promiscuous p-glycoprotein inhibition using a novel machine learning scheme.

ABSTRACT:

Background

P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development.

Methodology/principal findings

An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to

SUBMITTER: Leong MK 

PROVIDER: S-EPMC3306300 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature