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

Pitchayakarn  Takomthong; Pornthip  Waiwut; Carlo  Ballatore; Kiattawee  Choowongkomon; Puguh Novi  Arsito; Yaowared  Chulikhit; Chantana Boonyarat

Authors

Pitchayakarn Takomthong Faculty of Pharmaceutical Sciences, Khon Kaen University, Thailand https://orcid.org/0000-0002-8358-0420
Pornthip Waiwut Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Thailand https://orcid.org/0000-0001-9776-4397
Carlo Ballatore Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, United States https://orcid.org/0000-0002-2718-3850
Kiattawee Choowongkomon Department of Biochemistry, Faculty of Science, Kasetsart University, Thailand https://orcid.org/0000-0002-2421-7859
Puguh Novi Arsito School of Pharmacy, Faculty of Medicine and Health Sciences, Universitas Muhammadi-yah Yogyakarta, Indonesia
Yaowared Chulikhit Faculty of Pharmaceutical Sciences, Khon Kaen University, Thailand https://orcid.org/0000-0001-7317-8721
Chantana Boonyarat Faculty of Pharmaceutical Sciences, Khon Kaen University, Thailand https://orcid.org/0000-0003-4065-6090

Keywords:

Virtual screening, Acetylcholinesterase inhibitors, Molecular docking, Drug-likeness properties, BBB permeability, Pharmacokinetic properties, 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.

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