Ajmaline-acetylcholinesterase interaction: Insights from Docking, Molecular dynamics, and Predictive machine learning models

Abstract

Alzheimer's disease is a debilitating neurodegenerative disorder characterized by cognitive decline and memory impairment, with limited treatment options available. Ajmaline, a natural alkaloid derived from Rauwolfia serpentina, has demonstrated inhibitory activity against acetylcholinesterase (AChE), a key therapeutic target, with an IC₅₀ of 3.5 ± 1.41 µM. This study aimed to investigate the in silico interaction of Ajmaline with AChE to elucidate its potential as an anti-Alzheimer agent. Molecular docking revealed strong π-π stacking, hydrogen bonding, and hydrophobic interactions with key active-site residues, such as Tyr337, Tyr341, and Trp286, highlighting Ajmaline’s effective inhibition of AChE. RMSF analysis indicated structural stability in the catalytic triad and peripheral anionic site, ensuring Ajmaline’s efficient binding while maintaining the enzyme’s dynamic adaptability. Ajmaline’s compliance with Lipinski's Rule of Five and a Wiener index of 928 underscore its drug-likeness and molecular complexity, supporting its bioavailability and interaction potential. Badapple analysis revealed minimal promiscuity, suggesting selective binding and reduced off-target effects. A machine learning-based Random Forest classifier further classified Ajmaline as biologically active with high prediction accuracy. These findings demonstrate Ajmaline's potential as a lead compound for Alzheimer's treatment, combining robust molecular interactions, favorable pharmacokinetics, and minimal off-target activity. Future studies integrating experimental validation and optimization may advance Ajmaline’s development as a promising therapeutic candidate for neurodegenerative disorders.