Identifying Potential BACE1 Inhibitors from the ChEMBL Database Using Machine Learning and Atomistic Simulation Approaches
Abstract
The inhibition of β-site amyloid precursor protein-cleaving enzyme 1 presents a promising therapeutic strategy for treating Alzheimer's disease by reducing amyloid-β (Aβ) production. This paper employed a computational approach that combined machine learning (ML) and atomistic simulations to accelerate the discovery of potential BACE1 inhibitors. Our ML models, trained on a set of ligands with experimental binding affinity, showed high accuracy when tested on a holdout test set. The best model was used to screen more than two million compounds in the CHEMBL33 chemical library to obtain a short list of top-hit compounds, which were further analyzed using molecular docking and fast pulling of ligand (FPL) simulations. The insights into structure and binding energetics obtained from FPL simulations elucidate the stability and interaction mechanisms of the BACE1-ligand bound state, providing data useful for the rational design of novel AD therapeutics.
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