Thai_2024_J.Mol.Graph.Model_134_108906

Reference

Title : Estimating AChE inhibitors from MCE database by machine learning and atomistic calculations - Thai_2024_J.Mol.Graph.Model_134_108906
Author(s) : Thai QM , Nguyen TH , Lenon GB , Thu Phung HT , Horng JT , Tran PT , Ngo ST
Ref : J Mol Graph Model , 134 :108906 , 2024
Abstract :

Acetylcholinesterase (AChE) is one of the most successful targets for the treatment of Alzheimer's disease (AD). Inhibition of AChE can result in preventing AD. In this context, the machine-learning (ML) model, molecular docking, and molecular dynamics calculations were employed to characterize the potential inhibitors for AChE from MedChemExpress (MCE) database. The trained ML model was initially employed for estimating the inhibitory of MCE compounds. Atomistic simulations including molecular docking and molecular dynamics simulations were then used to confirm ML outcomes. In particular, the physical insights into the ligand binding to AChE were clarified over the calculations. Two compounds, PubChem ID of 130467298 and 132020434, were indicated that they can inhibit AChE.

PubMedSearch : Thai_2024_J.Mol.Graph.Model_134_108906
PubMedID: 39561662

Related information

Citations formats

Thai QM, Nguyen TH, Lenon GB, Thu Phung HT, Horng JT, Tran PT, Ngo ST (2024)
Estimating AChE inhibitors from MCE database by machine learning and atomistic calculations
J Mol Graph Model 134 :108906

Thai QM, Nguyen TH, Lenon GB, Thu Phung HT, Horng JT, Tran PT, Ngo ST (2024)
J Mol Graph Model 134 :108906