High-throughput Virtual Screening- and Molecular Docking-based Prediction for Acetylcholinesterase Inhibitors and Exploring its Mechanisms against Alzheimer’s Disease based on Network Pharmacology

Abstract

OBJECTIVE This study aimed to identify promising ligands for inhibiting acetylcholinesterase (AChE) activity using virtual screening (VS). METHODS VS was used to identify potential AChE inhibitors from the PubChem database. Ligands with favorable binding pocket interactions were selected. SwissADME and pkCSM tools were used to assess drug-likeness and pharmacokinetic properties. Molecular dynamic (MD) simulations provided insights into binding interactions. Network pharmacology was used to explore interactions between the target molecule and AD-related genes to determine its mechanism of action. RESULTS VS identified promising AChE inhibitor candidates with acridone, carbazole, and xanthone scaffolds. Docking simulations showed strong binding with AChE. These ligands displayed favorable drug-likeness and ADMET properties, with one (M5) lacking predicted hepatotoxicity. MD simulations suggested stable binding of M5 to AChE, potentially affecting both catalytic and peripheral sites, hinting at dual inhibition. M5’s interactions, especially near His440, appeared more favorable than donepezil. Network analysis implicated M5 in targeting multiple pathways in AD, with potential focus on neuroinflammation. CONCLUSIONS This study identified promising AChE inhibitor candidates through virtual screening. Ligand M5 emerged as particularly promising due to its favorable binding characteristics, lack of predicted hepatotoxicity, and potential for targeting multiple pathways in AD. However, further in vitro and in vivo validation is essential for clinical development. KEYWORDS virtual screening, acetylcholinesterase inhibitors, PubChem database, molecular dynamics simulation, gene ontology, KEGG pathways