Searching for AChE inhibitors from natural compounds by using machine learning and atomistic simulations.
J Mol Graph Model
; 115: 108230, 2022 09.
Article
in English
| MEDLINE | ID: covidwho-1914638
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Cholinesterase Inhibitors
/
Alzheimer Disease
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Mol Graph Model
Journal subject:
Molecular Biology
Year:
2022
Document Type:
Article
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