Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design

Title: Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design

Volume: 3 Issue: 4

Author(s): Jean-Pierre Doucet, Florent Barbault, Hairong Xia, Annick Panaye and Botao Fan

Affiliation:

Keywords: Support vector machine (SVM), QSPR/QSAR, drug-design, classification, correlation

Abstract: Recently, a new promising nonlinear method, the support vector machine (SVM), was proposed by Vapnik. It rapidly found numerous applications in chemistry, biochemistry and pharmacochemistry. Several attempts using SVM in drug design have been reported. It became an attractive nonlinear approach in this field. In this review, the theoretical basis of SVM in classification and regression is briefly described. Its applications in QSPR/QSAR studies, and particularly in drug design are discussed. Comparative studies with some linear and other nonlinear methods show SVMs high performance both in classification and correlation.

Cite this article as:

Doucet Jean-Pierre, Barbault Florent, Xia Hairong, Panaye Annick and Fan Botao, Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design, Current Computer-Aided Drug Design 2007; 3 (4) . https://dx.doi.org/10.2174/157340907782799372