Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design
Jean-Pierre Doucet, Florent Barbault, Hairong Xia, Annick Panaye and Botao Fan
Affiliation: ITODYS-CNRS UMR7086,Université Paris 7-Denis Diderot, 1, rue Guy de la Brosse, 75005 Paris,France.
Keywords: Support vector machine (SVM), QSPR/QSAR, drug-design, classification, correlation
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.
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