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

Abstract

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.