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. 2022 Jun 8;7(24):20673-20682.
doi: 10.1021/acsomega.2c00908. eCollection 2022 Jun 21.

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

Trung Hai Nguyen  1   2 , Phuong-Thao Tran  3 , Ngoc Quynh Anh Pham  4 , Van-Hai Hoang  5   6 , Dinh Minh Hiep  7 , Son Tung Ngo  1   2
Affiliations

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

Trung Hai Nguyen et al. ACS Omega. .

Abstract

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay. Moreover, atomistic simulations including molecular docking and molecular dynamics simulations were then used to understand molecular insights into the binding process of ligands to AChE. In particular, two compounds including benzyl trifluoromethyl ketone and trifluoromethylstyryl ketone were indicated as highly potent inhibitors of AChE because they established IC50 values of 0.51 and 0.33 μM, respectively. The obtained IC50 of two compounds is significantly lower than that of galantamine (2.10 μM). The predicted log(BB) suggests that the compounds may be able to traverse the blood-brain barrier. A good agreement between computational and experimental studies was observed, indicating that the hybrid approach can enhance AD therapy.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Workflow for predicting potential inhibitors for AChE. (A) Investigation scheme was applied to estimate potential inhibitors for AChE using ML, atomistic calculations, and in vitro studies. (B) Refined investigation of the ML prediction via an in vitro enzyme assay. (C) Predicted potential inhibitors by the ML model were docked to the AChE active site via the modified AutoDock Vina. (D) AChE + trifluoromethylstyryl ketone complex was simulated using MD simulations to find the ligand-binding pose.
Figure 2
Figure 2
Distribution of binding free energy from the experiment for the labeled set (left) and from prediction by the GraphConv model for the test and ChEMBL sets.
Figure 3
Figure 3
Comparison of binding free energy between the experiment and prediction made by the GraphConv model for 162 test compounds.
Figure 4
Figure 4
Correlation between docking and experimental data.
Figure 5
Figure 5
Interaction diagram of the AChE + inhibitor complex. The outcome was obtained via the analysis of Maestro over the representative structure of the solvated complex. The structure was obtained via clustering all of the conformational complex within the interval of 40–100 ns with a cutoff of 0.12 nm.
See this image and copyright information in PMC

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