Toward a general predictive QSAR model for gamma-secretase inhibitors

Abstract

Gamma secretase (GS) is an appealing drug target for Alzheimer disease and cancer because of its central role in the processing of amyloid precursor protein and the notch family of proteins. In the absence of three-dimensional structure of GS, there is an urgent need for new methods for the prediction and screening of GS inhibitors, for facilitating discovery of novel GS inhibitors. The present study reports QSAR studies on diverse chemical classes comprising 233 compounds collected from the ChEMBL database. Herein, continuous [PLS regression and neural-network (NN)] and categorical QSAR models (NN and linear discriminant analysis) were developed to obtain pertinent descriptors responsible for variation of GS inhibitor potency. Also, SAR within various chemical classes of compounds is analyzed with respect to important QSAR descriptors, which revealed the significance of electronegative substitutions on aryl rings (PEOE3) in determining variation of GS inhibitor potency. Furthermore, substitution of acyclic amines with N-substituted cyclic amines appears to be favorable for enhancing GS inhibitor potency by increasing the values of sssN_Cnt and number of aliphatic rings. The models developed are statistically significant and improve our understanding of compounds contributing toward GS inhibitor potency and aid in the rational design of novel potent GS inhibitors.