ArticleBiochemistry research international2026
Machine Learning-Driven Bioprospecting of Cholinergic Medicinal Plants for Putative Leads Against Butyrylcholinesterase Towards Alzheimer's Care.
Article in Biochemistry research international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Butyrylcholinesterase (BChE) plays a key role in preserving appropriate cholinergic neurotransmission that is essentially altered in the brains of advanced Alzheimer's disease (AD), hence a therapeutic target. This study employed a machine learning (ML) bioactivity predictive model to explore the chemical space of potential BChE inhibitors. A cheminformatics pipeline was explored to create a ML model for BChE inhibition using structural insights complemented with a comprehensive variance importance plot (VIP) and correlation matrix analysis. Specifically, a compiled library of 2179 secondary metabolites (SMs) from 50 Nigerian medicinal plants with reported cholinergic activity was investigated using the ML model. After which, molecular modelling was used to further screen the active SMs (827). The final predicted models demonstrated significant robustness, with a correlation coefficient of 0.8981. Molecular docking investigation of the 827 SMs identified the top five candidates based on their scores: three triterpenoids (adipedatol, lupenone and β-amyrin), one steroid (9 (11)-dehydroergosterol benzoate) and one flavonoid (tiliroside). These leads exhibited favourable ADMET properties and promising safety profiles. Among these five, lupenone (-48.78 kcal/mol), β-amyrin (-49.19 kcal/mol) and adipedatol (-48.87 kcal/mol), from
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