Lead Identification Through In Silico Studies: Targeting Acetylcholinesterase Enzyme Against Alzheimer's Disease.

Abstract

In this work, we aimed to acquire the best potential small molecule for Alzheimer's disease (AD) treatment using different models in Biovia Discovery Studio to identify new potential inhibitors of acetylcholinesterase (AChE) via in silico studies. The prevalence of cognitive impairment-related neurodegenerative disorders, such as AD, has been observed to escalate rapidly. However, we still know little about the underlying functions, outcome predictors, or intervention targets causing AD. The objective of the study was to optimize and identify the lead compound to target AChE against Alzheimer's disease. Different in silico studies were employed, including the pharmacophore model, virtual screening, molecular docking, de novo evolution model, and molecular dynamics. The pharmacophoric features of AChE inhibitors were determined by ligand-based pharmacophore models and 3D QSAR pharmacophore generation. Further validation of the best pharmacophore model was done using the cost analysis method, Fischer's randomization method, and test set. The molecules that harmonized the best pharmacophore model with the estimated activity < 1 nM and ADMET parameters were filtered, and 12 molecules were subjected to molecular docking studies to obtain binding energy. 3vsp_EK8_1 secured the highest binding energy of 65.60 kcal/mol. Further optimization led to a 3v_Evo_4 molecule with a better binding energy of 70.17 kcal/mol. The molecule 3v_evo_4 was subjected to 100 ns molecular simulation compared to donepezil, which showed better stability at the binding site. A lead compound, 3v_Evo_4 molecule, was identified to inhibit AChE, and it could be further studied to develop as a drug with better efficacy than the existing available drugs for treating AD.