Artificial neural network-based quantitative structure-activity relationships model and molecular docking for virtual screening of novel potent acetylcholinesterase inhibitors

The deficit in cholinergic neurotransmission is the main incentives for the development of new therapeutic drugs for the treatment of Alzheimer's disease (AD). In this study, we employed cheminformatics tools to explore new molecules that can serve as effective therapeutic agents against AD. Artificial neural networks (ANNs) were applied in a quantitative structure anti-acetylcholinesterase (AChE)-activity relationship study on a series of AChE inhibitors (AChEIs). The best computational neural network had an [5-10-12-1] architecture, with a low value of mean squared error (MSE = 0.06) and a high value of R-2 (0.96). All validations showed that the ANN model can be used quite satisfactorily for the screening of a new series of molecules having anti-AChE activity. The virtual screening based on the molecular similarity method and applicability domain of ANN-quantitative structure-activity relationships allowed the discovery of novel anti-AChE candidates with improved activity. Docking simulation carried out on these novel AChEIs has identified eight best hits with a higher binding affinity toward their target (4EY7). These eight stable complexes were in good agreement with the biological activity and they have an inhibition profile at a similar rate as the reference drug donepezil. The results showed that these compounds were strongly bound up with the AChE enzyme active site with the optimal conformations.