Rapid Classification of CYP3A4 Inhibition Potential Using Support Vector Machine Approach

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.

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