Identification of Novel Benzimidazole-Based NS5B Polymerase Inhibitors of Hepatitis C Virus Using Molecular Docking, Atom-Based 3D-QSAR Model, and ADMET Prediction.

Hepatitis C virus (HCV) remains a major global health threat, and the non-structural 5B (NS5B) polymerase is a key target for antiviral drug discovery. In this study, a virtual screening strategy was applied to identify novel benzimidazole-based inhibitors of the NS5B polymerase. Molecular docking was first performed to evaluate the binding affinities of a series of benzimidazole derivatives toward the NS5B polymerase active site. Key interactions of the ligand with the receptor active site were identified, displaying common features with those reported for other known inhibitors, thereby reinforcing the validity of the findings. An atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) model was subsequently developed and found to be statistically robust, with high predictive performance for both the training set (R² = 0.95, SD = 0.18, N = 42) and the test set (Q² = 0.79, RMSE = 0.57,N = 11). Guided by these findings, new benzimidazole derivatives were designed as potential NS5B polymerase inhibitors. The most promising compounds were further evaluated for their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties using pkCSM. to assess drug-likeness. This integrated computational approach provides valuable insights into the molecular features governing benzimidazole NS5B binding and identifies promising lead compounds for the development of new hepatitis C virus (HCV) therapeutics.