Hepatitis C virus (HCV) infection constitutes a global health problem, which affects more than 170 million individuals.HCV is an enveloped single strand RNA virus and encodes a polyprotein chain of about 3000 amino acids, which is processed into structural and nonstructural (NS) proteins.Polyprotein processing by viral and cellular host factors results in four structural proteins (Core, El, E2, p7) and six nonstructural (NS2, NS3, NS4A, NS4B, NS5A, NS5B) proteins.NS5B is a RNA-dependent RNA polymerase (RdRp) at the core of the HCV replicative complex.Given the essential role of this enzyme in viral replication, it is anticipated that agents capable of disrupting its function will prove efficacious in the treatment of HCV infections.This work built several computational models for classification the bioactivity of HCV NS5B Polymerase inhibitors.Using a support vectōr 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 .