• QSAR modeling of a Ligand-based pharmacophore derived from Hepatitis B virus surface antigen inhibitors
  • Puria Qadirian,1 Alireza Mohebbi,2,* Reyhane Shaddel,3
    1. Golestan University of Medical sciences - Gorgan - Iran
    2. Golestan University of Medical sciences - Gorgan - Iran
    3. Golestan University of Medical sciences - Gorgan - Iran


  • Introduction: 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).
  • Methods: LigandScout v3.12 was used for LBP modeling. The best model with the highest score was used for high throughput screening (HTS) screening. 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 the lack of overfitting. Further RMSE and Q2 statistics were measured for testing the model on the validation set.
  • Results: 34 active anti-HBsAg compounds were used to develop an LBP model. 9/34 of 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 Yindex and GATS8m, with substantial variance information (p < 0.05). The model was well fitted (R2 = 0.9563, MSE = 0.0023).
  • Conclusion: 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 activities of the hit compounds with 95.63% accuracy with only two Yindex and GATS8m descriptors.
  • Keywords: Hepatitis B virus - QSAR - Ligand-based pharmacophore