Letters in Drug Design & Discovery

G. Perry
University of Texas
San Antonio, TX
USA
Email: lddd@benthamscience.org

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Rapid Classification of CYP3A4 Inhibition Potential Using Support Vector Machine Approach

Author(s): Diansong Zhou, Ruifeng Liu, Sara A. Otmani, Scott W. Grimm, Randy J. Zauhar, Ismael Zamora.

Abstract:

The CYP3A4 inhibition SVM classification model achieved high prediction accuracy, which can be used as high throughput computational filter for identifying CYP3A4 inhibition liability. Its demonstrated that the distance to the separating surface in the feature space can be used as valuable confidence index for the prediction of each compound.

Keywords: CYP3A4, Inhibition, QSAR, Support Vector Machine

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Article Details

VOLUME: 4
ISSUE: 3
Year: 2007
Page: [192 - 200]
Pages: 9
DOI: 10.2174/157018007780077462
Price: $58