Classification of estrogen receptor-β ligands on the basis of their binding affinities using support vector machine and linear discriminant analysis

Classification models of estrogen receptor-β ligands were proposed using linear and nonlinear models. The data set was divided into active and inactive classes on the basis of their binding affinities. The two-class problem (active, inactive) was firstly explored by linear classifier approach, linear discriminant analysis (LDA). In order to get a more accurate prediction model, the nonlinear novel machine learning technique, support vectors machine (SVM), was subsequently used to investigate. The heuristic method (HM) was used to pre-select the whole descriptor sets. The model containing eight descriptors founded by SVM, showed better predictive ability than LDA. The accuracy in prediction for the training, test and overall data sets are 92.9%, 85.8% and 91.4% for SVM, 83.1%, 76.1% and 81.9% for LDA, respectively. The results indicate that SVM can be used as a powerful modeling tool for QSAR studies.

Classification models of 105 estrogen receptor-β ligands were proposed using linear (linear discriminant analysis) and nonlinear models (support vectors machine). The results gave an insight into the descriptors that are likely to relate to the binding affinities of the newly reported diphenolic azoles.Figure optionsDownload as PowerPoint slide