Li_2025_Molecules_30_

Butyrylcholinesterase (BChE), plays a critical role in alleviating the symptoms of Alzheimer's disease (AD) by regulating acetylcholine levels, emerging as an attractive target for AD treatment. This study employed a quantitative structure-activity relationship (QSAR) model based on ECFP4 molecular fingerprints with several machine learning algorithms (XGBoost, RF, SVM, KNN), among which the XGBoost model showed the best performance (AUC = 0.9740). A hybrid strategy integrating ligand- and structure-based virtual screening identified 12 hits from the Topscience core database, three of which were identified for the first time. Among them, piboserod and Rotigotine demonstrated the best BChE inhibitory potency (IC(50) = 15.33 microM and 12.76 microM, respectively) and exhibited favorable safety profiles as well as neuroprotective effects in vitro. Notably, Rotigotine, a marketed drug, was newly recognized for its anti-AD potential, with further enzyme kinetic analyses revealing that it acts as a mixed-type inhibitor in a non-competitive mode. Fluorescence spectroscopy, molecular docking, and molecular dynamics simulations further clarified their binding modes and stability. This study provides an innovative screening strategy for the discovery of BChE inhibitors, which not only identifies promising drug candidates for the treatment of AD but also demonstrates the potential of machine learning in drug discovery.