In Silico Discovery and Predictive Modeling of Novel Acetylcholinesterase (AChE) Inhibitors for Alzheimer's Treatment.

Abstract

Introduction: Alzheimer's disease, akin to coronary artery disease of the heart, is a progressive brain disorder driven by nerve cell damage.

Method: This study utilized computational methods to explore 14 anti-acetylcholinesterase (AChE) derivatives (1 ̶ 14) as potential treatments. By scrutinizing their interactions with 11 essential target proteins (AChE, Aβ, BChE, GSK-3β, MAO B, PDE-9, Prion, PSEN-1, sEH, Tau, and TDP-43) and comparing them with established drugs such as donepezil, galantamine, memantine, and rivastigmine, ligand 14 emerged as notable. During molecular dynamics simulations, the protein boasting the strongest bond with the critical 1QTI protein and exceeding drug-likeness criteria also exhibited remarkable stability within the enzyme's pocket across diverse temperatures (300 ̶ 320 K). In addition, we utilized density functional theory (DFT) to compute dipole moments and molecular orbital properties, including assessing the thermodynamic stability of AChE derivatives.

Result: This finding suggests a welldefined, potentially therapeutic interaction further supported by theoretical and future in vitro and in vivo investigations.

Conclusion: Ligand 14 thus emerges as a promising candidate in the fight against Alzheimer's disease.