Identification of AChE-targeted natural compounds for Alzheimer’s disease: an integrated in-silico study

This study introduces a multi-stage computational pipeline designed to identify natural Acetylcholinesterase inhibitors for Alzheimer’s disease. It screens 450,000 natural compounds from the COCONUT database through high-throughput virtual screening using the Schrödinger Suite, then refines hits using MMGB/SA rescoring, ADMET assessments, DFT computations, and Molecular dynamics trajectories. By layering these steps, the approach overcomes the limitations of standard docking alone and focuses on candidates with proven stability and drug potential. Among the natural compounds screened via virtual screening and MM/GBSA studies, CNP0237740 emerged as the top molecule, with a docking score of − 12.76 kcal/mol and MM-GBSA binding free energy of − 74.62 kcal/mol, which have similar values to those of Galantamine (− 67.80 kcal/mol). Its ADMET profile supports strong drug candidacy, featuring optimal lipophilicity (QLogP 2.30), compact polar surface area (PSA 35 Å 2 ), excellent Caco-2 cell permeability, and reliable oral bioavailability. CNP0237740 showed fewer toxicity endpoints than Galantamine in silico toxicity studies using Pro Tox 3.0, eliminating the possibility of hepatotoxicity, neurotoxicity, nephrotoxicity, cardiotoxicity, respiratory toxicity, or carcinogenicity. DFT analysis further showed CNP0237740 to be more reactive and softer than Galantamine, aiding its interactions at the AChE active site. Molecular dynamics simulations confirmed the robustness of the CNP0237740-AChE complex, with steady RMSD profiles and persistent key contacts that align with effective inhibition mechanisms. These attributes collectively mark CNP0237740 as a standout natural lead, primed for experimental follow-up in Alzheimer’s treatment.