Pattern recognition system based on support vector machines: HIV-1 integrase inhibitors application

Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to develop a predictive Quantitative Structure-Activity Relationship (QSAR) models using molecular descriptors. 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 Machines; Artificial Neural Network; Quantitative Structure-Activity Relationship

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

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; QSA

QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

Support vector machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR) models using molecular descriptors. Multiple linear regression (MLR) and artificial neural networks (ANNs) were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated

Support vector machines: Development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives

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