Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design

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

Journal Name: Current Computer-Aided Drug Design

Volume 3 , Issue 4 , 2007

Submit Manuscript
Submit Proposal


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.

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

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2007
Page: [263 - 289]
Pages: 27
DOI: 10.2174/157340907782799372
Price: $58

Article Metrics

PDF: 5