QSAR Modeling of a Ligand-Based Pharmacophore Derived from Hepatitis B Virus Surface Antigen Inhibitors

Functional cure for Hepatitis B virus (HBV) by inhibiting HBV surface antigen (HBsAg) is crucial. Therefore, it was aimed to develop a predictive quantitative structure-activity relationship (QSAR) model on a ligand-based pharmacophore (LBP). LigandScout v3.12 was used for LBP modeling. The best model with the highest score was used for high throughput screening (HTS). A QSAR model was developed by a stepwise multiple linear regression (MLR) with a confidence interval (CI) of 95%. The goodness-of-fit statistics evaluated the fitness of the model. A comparable R2 and adjusted R2 were considered as lack of overfitting. Further RMSE and Q2 statistics were measured for testing the model on the validation set. Thir¬ty-four active anti-HBsAg compounds were used to develop an LBP model. Nine of 34 compounds with higher clustering pharmacophore-fit scores were tagged as the training set, and the rest of the inhibitors were used as the test set. The best model had a 0.8832 fit score. HTS resulted in 10 potential hit compounds with a fit score of 101.44±0.65. A QSAR model was developed with two response variables, including Yin¬dex and GATS8m, with substantial variance information (p < 0.05). The model was well fitted (R2 = 0.9563, MSE = 0.0023). A reliable well-fitted predictive QSAR model was developed. The model can be applied to the chemical libraries fitted to the LBP model, and the QSAR equation would estimate the biological activ¬ities of the hit compounds with 95.63% accuracy with only two Yindex and GATS8m descriptors