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
Letters in Drug Design & Discovery
Title: Rapid Classification of CYP3A4 Inhibition Potential Using Support Vector Machine Approach
Volume: 4 Issue: 3
Author(s): Diansong Zhou, Ruifeng Liu, Sara A. Otmani, Scott W. Grimm, Randy J. Zauhar and Ismael Zamora
Affiliation:
Keywords: CYP3A4, Inhibition, QSAR, Support Vector Machine
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.
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Cite this article as:
Zhou Diansong, Liu Ruifeng, Otmani A. Sara, Grimm W. Scott, Zauhar J. Randy and Zamora Ismael, Rapid Classification of CYP3A4 Inhibition Potential Using Support Vector Machine Approach, Letters in Drug Design & Discovery 2007; 4 (3) . https://dx.doi.org/10.2174/157018007780077462
DOI https://dx.doi.org/10.2174/157018007780077462 |
Print ISSN 1570-1808 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-628X |
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