Drug likeness prediction by DruLito software.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive decline, driven by the accumulation of amyloid-beta plaques and neurofibrillary tangles. It involves the dysfunction of key enzymes such as Acetylcholinesterase (AChE) and β-secretase (BACE1), making them critical targets for therapeutic intervention. In this study we investigated an in-house library of 820 secondary metabolites obtained from Ayurvedic plants against AChE and BACE1 with the aim to discover novel leads for AD. Virtual screening resulted in 15 ligands, mostly belonging to the ursane-type or dammarene-type triterpene saponins of Centella asiatica, reestablishing the potency of this plant in drug discovery against AD. The binding affinities were further verified by molecular dynamics (MD) simulation trajectories, including root mean square fluctuations (RMSF), root mean square deviation (RMSD), hydrogen bonding analysis, Coulomb interaction calculation, Lennard-Jones interactions, and the total interaction energy. Moreover, extensive Principal Component Analysis (PCA) and Gibbs free energy landscape were performed. Our results demonstrated three compounds, namely (S)-eriodictyol 7-O-(6-β-O-trans-p-coumaroyl)-β-d-glucopyranoside, sitoindoside-X and 1,5-di-o-caffeoyl quinic acid as more effective in treating AD due to their comparable drug-like properties. Drug-likeness, structural chemistry, pharmacophore, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis support their potential for future drug development. To establish the effectiveness of these lead compounds against AD, additional experimental testing should be performed.