Classification of HCV NS5B Polymerase Inhibitors Using Support Vector Machine

Abstract: Using a support vector machine (SVM), three classification models were built to predict whether a compound is an active or weakly active inhibitor based on a dataset of 386 hepatitis C virus (HCV) NS5B polymerase NNIs (non-nucleoside analogue inhibitors) fitting into the pocket of the NNI III binding site. For each molecule, global descriptors, 2D and 3D property autocorrelation descriptors were calculated from the program ADRIANA.Code. Three models were developed with the combination of different types of descriptors. Model 2 based on 16 global and 2D autocorrelation descriptors gave the highest prediction accuracy of 88.24% and MCC (Matthews correlation coefficient) of 0.789 on test set. Model 1 based on 13 global descriptors showed the highest prediction accuracy of 86.25% and MCC of 0.732 on external test set (including 80 compounds). Some molecular properties such as molecular shape descriptors (InertiaZ, InertiaX and Span), number of rotatable bonds (NRotBond), water solubility (LogS), and hydrogen bonding related descriptors performed important roles in the interactions between the ligand and NS5B polymerase.