An approach to design potent anti-Alzheimer’s agents by 3D-QSAR studies on fused 5,6-bicyclic heterocycles as γ-secretase modulators using kNN–MFA methodology

Abstract

Alzheimer’s disease (AD) is a chronic neurodegenerative disease. Current therapies of AD are only symptomatic, therefore the need for the development of new therapies to treat Alzheimer’s disease effectively. To achieve this objective quantitative structure–activity relationship (QSAR) studies were carried out as it provides the rationale for the changes in the structure to have more potent Aβ42 inhibitors or anti-Alzheimer’s agents. Quantitative structure–activity relationship (QSAR) studies were carried out on a series of 34 fused 5,6-bicyclic heterocycles to investigate the structural requirements of their inhibitory activity against Aβ42. The statistically significant best 3D-QSAR model having cross-validated squared correlation coefficient q2=0.8457 with external predictive ability of pred_r2=0.7556 was developed by SW-kNN. Developed kNN–MFA model highlighted the importance of shape of the molecules, i.e., hydrophobic and steric descriptors at the grid points H_83 and S_183, S_227 for γ-secretase binding interaction. This model (3D) was found to yield reliable clues for further optimization of fused 5,6-bicyclic heterocycles in the data set. The information rendered by the 3D-QSAR model may lead to a better understanding of the structural requirements of γ-secretase modulators and can also help in the design of novel potent γ-secretase modulators.