QSAR studies of some substituted imidazolinones angiotensin II receptor antagonists using Partial Least Squares Regression (PLSR) method based feature selection

Abstract

The Quantitative structure–activity relationship (QSAR) analyses were carried out for a series of imidazolinones as nonpeptide angiotensin II receptor antagonists to find out the structural requirements of their antihypertensive activities. Partial Least Squares Regression (PLSR) method coupled was applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The statistically significant best model with high correlation coefficient (r2 = 0.9031) was selected for further study. The model was further validated by means of crossed squared correlation coefficient (q2 = 0.8652 and pred_r2 = 0.8261) which shows the model has good predictive ability, higher value of F statics 66.831 also validates significance of model, Degree of freedom20. The physicochemical descriptor SssCH2count and alignment-independent descriptors T_N_O_6, T_2_Cl_6 and Chi5 were found to show significant correlation with biologic activity in imidazolinones. Successful implementation of a predictive QSAR model largely depends on the selection of a preferred set of molecular descriptors that can signify the antihypertensive new design molecules.