Searching for AChE inhibitors from natural compounds by using machine learning and atomistic simulations

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease treatment. In this work, a combined approach involving machine-learning (ML) model and atomistic simulations was established to predict the ligand-binding affinity to AChE of the natural compounds from...

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Bibliographic Details
Published in:Journal of molecular graphics & modelling 2022-09, Vol.115, p.108230-108230, Article 108230
Main Authors: Thai, Quynh Mai, Pham, T. Ngoc Han, Hiep, Dinh Minh, Pham, Minh Quan, Tran, Phuong-Thao, Nguyen, Trung Hai, Ngo, Son Tung
Format: Article
Language:English
Subjects:
Acetylcholinesterase - chemistry
Alzheimer Disease - drug therapy
Cholinesterase Inhibitors - chemistry
Cholinesterase Inhibitors - pharmacology
Humans
Machine Learning
Molecular Docking Simulation
Molecular Dynamics Simulation
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Summary:Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease treatment. In this work, a combined approach involving machine-learning (ML) model and atomistic simulations was established to predict the ligand-binding affinity to AChE of the natural compounds from VIETHERB database. The trained ML model was first utilized to rapidly and accurately screen the natural compound database for potential AChE inhibitors. Atomistic simulations including molecular docking and steered-molecular dynamics simulations were then used to confirm the ML outcome. Good agreement between ML and atomistic simulations was observed. Twenty compounds were suggested to be able to inhibit AChE. Especially, four of them including geranylgeranyl diphosphate, 2-phosphoglyceric acid, and 2-carboxy-d-arabinitol 1-phosphate, and farnesyl diphosphate are highly potent inhibitors with sub-nanomolar affinities. [Display omitted]
ISSN:1093-3263
1873-4243
DOI:10.1016/j.jmgm.2022.108230