Integrative computational and experimental identification of marine bacterial acetylcholinesterase inhibitors against alzheimer's disease.

Alzheimer's disease (AD) is a powerful neurodegenerative disease characterized by cholinergic deficiency, where the inhibition of acetylcholinesterase (AChE) remains a clinically validated strategy. In our current work, a virtual screening platform supported by machine learning identified new inhibitors of AChE out of a structurally diverse collection of 2,895 marine bacterial natural products. Following a curation based on a structure-based strategy, a robust regression model was constructed from the physicochemical and structural characteristics of the reported inhibitors of AChE in an attempt to predict the inhibitory strength (pIC₅₀) of the top-scored ligands. The model had high predictive fidelity and led to the selection of twenty prospective candidates, out of which three (CMNPD25858, CMNPD28646, and CMNPD28412) were shortlisted according to activity profiles and drug-likeness filters. The shortlisted compounds were prepared for quantum-level refinement through density functional theory in order to improve electronic and structural precision. These optimised ligands were then evaluated under physiological conditions in terms of binding stability, conformational study, and intermolecular interaction through all-atom molecular dynamics simulation. CMNPD25858 demonstrated outstanding structural retention, stable persistent hydrogen bonding, and negligible displacement in the catalytic site. Principal component analysis and free energy landscape mapping revealed a highly confined, energetically favorable conformational basin. Structural overlays of post-simulation minima with initial docking poses confirmed minimal divergence. MM-GBSA free energy calculations substantiated the superior binding affinities of CMNPD25858 (-87.90 kcal/mol) and CMNPD28646 (-83.44 kcal/mol) relative to the reference compound. In vitro AChE inhibition assays revealed that compound CMNPD25858 demonstrated the highest inhibition (75%) at 1 mg/ml, followed by CMNPD28646 (64%) and CMNPD28412 (57.81%), consistent with in silico predictions when compared to the standard Donepezil (95.27%). Therefore, these integrative studies highlight the strategic utility of machine learning in accelerating structure-activity prediction and rational hit selection, and identifies marine-derived CMNPD25858 and CMNPD28646 as potent, dynamically stable AChE inhibitors with high potential for anti-Alzheimer's therapeutic development.