Monte Carlo Optimization Based QSAR Modeling of Angiotensin II Receptor Antagonists

Abstract

The pathogenesis of essential hypertension, congestive heart failure, and reno-vascular hypertension is related to angiotensin II. This study presents QSAR modeling for a set of compounds acting as angiotensin II receptor antagonists based on the Monte Carlo optimization with molecular graph-based and SMILES notation based descriptors. Conformation independent QSAR models were developed for three random splits. Various statistical approaches were used to assess the statistical quality of the developed models, and the obtained results were very good. This study used a novel statistical metric known as the index of ideality of correlation for the final assessment of the model, and the results that were obtained suggested that the model was good. Also, molecular fragments which account for the increases and/or decreases of a studied activity were defined and then used for the computer-aided design of new compounds as potential angiotensin II receptor antagonists. The final assessment of the designed inhibitors, was performed with the use of molecular docking studies, highlighting exceptional correlation with the QSAR modeling results. The methodology which is presented in this research can be applied for seeking new agents for cardiovascular disorders treatment by angiotensin II receptor antagonism.