Application of support vector machines for prediction of anti-HIV activity of TIBO Derivatives.

Abstract

The performance and predictive power of support vector machines (SVM) for regression problems in quantitative structure-activity relationship were investigated. The SVM results are superior to those obtained by artificial neural network and multiple linear regression. These results indicate that the SVM model with the kernel radial basis function can be used as an alternative tool for regression problems in quantitative structure-activity relationship.

Keywords: support vector machine (SVM); ANN; QSAR