In silico identification of novel lead compounds with AT1 receptor antagonist activity: successful application of chemical database screening protocol.

Background: AT1 receptor antagonists are clinically effective drugs for the treatment of hypertension, cardiovascular, and related disorders. In an attempt to identify new AT1 receptor antagonists, a pharmacophore-based virtual screening protocol was applied. The pharmacophore models were generated from 30 training set compounds. The best model was chosen on the basis of squared correlation coefficient of training set and internal test set. The validity of the developed model was also ensured using catScramble validation method and external test set prediction.

Results: The final model highlighted the importance of hydrogen bond acceptor, hydrophobic aliphatic, hydrophobic, and ring aromatic features. The model satisfied all the statistical criteria such as cost function analysis and correlation coefficient. The result of estimated activity for internal and external test set compounds reveals that the generated model has high prediction capability. The validated pharmacophore model was further used for mining of 56000 compound database (MiniMaybridge). Total 141 hits were obtained and all the hits were checked for druggability, this led to the identification of two active druggable AT1 receptor antagonists with diverse structure.

Conclusion: A highly validated pharmacophore model generated in this study identified two novel druggable AT1 receptor antagonists. The developed model can also be further used for mining of other virtual database.