Machine learning tools for the characterization of bioactive metabolites derived from different parts of Ochrosia elliptica Labill. for the management of Alzheimer's disease

Abstract

Currently, natural products are one of the most valuable resources for discovering novel chemical medicinal entities. A total of 41 compounds were tentatively identified from the stems, barks, roots, and fruits of Ochrosia elliptica Labill. using UPLC-MS/MS analysis. The binding affinities of these biomarkers for the active sites of acetyl- and butyryl-cholinesterase enzymes were further validated using molecular docking studies, which showed good results with (–)C-Docker interaction energy ranges of 30.17–86.73 and 26.81–72.42 (kcal mol⁻¹), respectively. The most active predicted compound was a quercetin derivative [quercetin-3-O-rhamnosyl-(1-3)-rhamnosyl-(1-6)-hexoside, E = −86.73, −72.42 kcal mol⁻¹], which was subjected to dynamic simulation studies against the two enzymes to investigate the stability of the docked conformation. Root-mean-square fluctuations (RMSFs) showed values of 0.25–4.0 and 0.50–4.75 compared to free-state protein RMSF values of 0.25–4.5 and 0.5–7.5, revealing stable fluctuations over time after docking of this compound to AChE and BChE active pockets, respectively. AI in pharmacology can significantly improve patient outcomes and advance healthcare. Ligand binding or catalytic sites for AzrBmH21, AzrBmH22/3, and AzrBmH24/5 were predicted using a machine learning algorithm based on the Prank Web and DeepSite chemoinformatics tools. These findings will establish a scientific foundation for further investigations into the Ochrosia genus, particularly in relation to Alzheimer's disease.