Identification of acetylcholinesterase inhibitors from traditional medicinal plants for Alzheimer's disease using in silico and machine learning approaches

Acetylcholinesterase (AChE) holds significance in Alzheimer's disease (AD), where cognitive impairment correlates with insufficient acetylcholine levels. AChE's role involves the breakdown of acetylcholine, moderating cholinergic neuron activity to prevent overstimulation and signal termination. Hence, inhibiting AChE emerges as a potential treatment avenue for AD. A library of 2500 compounds, derived from 25 traditionally used medicinal plants, was constructed using the IMPAAT database of traditional medicinal plants. The canonical SMILES of these compounds were collected and underwent virtual screening based on physicochemical properties, with subsequent determination of IC50 values for the screened compounds followed by analysis using machine learning (ML). Subsequently, a molecular docking study elucidated both binding affinity and interactions between these compounds and AChE. The top three compounds, exhibiting robust binding affinities, underwent MM-GBSA analysis for molecular docking validation, succeeded by pharmacokinetics and toxicity evaluations to gauge safety and efficacy. These three compounds underwent MD simulation studies to assess protein-ligand complex conformational stability. Additionally, Density Functional Theory (DFT) was employed to ascertain HOMO, LUMO, energy gap, and molecular electrostatic potential. Among 2500 compounds, physicochemical properties-based virtual screening identified 80 with good properties, of which 32 showed promising IC50 values. Molecular docking studies of these 32 compounds revealed various binding energies with AChE, with the best three compounds (CID 102267534, CID 15161648, CID 12441) selected for further analysis. MM-GBSA studies confirmed the promising binding energies of these three compounds, validating the molecular docking study. Further, the MD simulation studies have confirmed the structural and conformational stability of these three protein-ligand complexes. Finally, DFT calculations revealed favorable chemical features of these compounds. Thus, we can conclude that these three compounds (CID 102267534, CID 15161648, CID 12441) may inhibit the activity of AChE and can be useful as a treatment for Alzheimer's disease.