QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors

Abstract

A new family of AChE inhibitors, N-benzylpiperidines, showed exceptional efficacy in vitro and in vivo, minimal side effects and high selectivity for acetylcholinesterase (AChE). Three regression methods were chosen in this work to develop robust predictive models, namely multiple linear regression (MLR), genetic function approximation (GFA) and multilayer perceptron network (MLP). Ten descriptors were selected for a dataset of 99 molecules, using a genetic algorithm. The best results were obtained for MLP with a 10-6-1 artificial neural network model trained with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. Statistical prediction for MLR and GFA were r² = 0.882 and r² = 0.875, respectively. Because internal and external validation strategies play an important role, we adopted all available validation strategies to check the robustness of the models. All criteria used to validate these models revealed the superiority of the GFA model. Therefore, the models developed in this study provide an excellent prediction of the inhibitory concentration of a new family of AChE inhibitors.

Keywords:

• QSAR
• MLP-ANN
• GFA
• MLR
• AChE inhibitors