Hou_2025_Mol.Divers__

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases. Given the multifactorial pathophysiology of AD, monotargeted agents can only alleviate symptoms but not cure AD. Acetylcholinesterase (AChE) and Monoamine oxidase B (MAO-B) are two key targets in the treatment of AD, molecules that inhibiting both targets are considered promising avenue to develop more effective AD therapies. In the present work, a dual inhibition dataset containing 449 molecules was established, based on which five machine learning algorithms (KNN, SVM, RF, GBDT, and LGBM) four fingerprints (MACCS, ECFP4, RDKitFP, PubChemFP) and DRAGON descriptors were combined to develop 25 classification models in which GBDT paired with ECFP4 and RF paired with PubchemFP achieved the same best performance across multiple metrics (Accuracy = 0.92, F1 Score = 0.94, MCC = 0.81). Moreover, based on the curated bioactivity datasets of AChE and MAO-B, regression models were developed to predict pIC(50) values. For the AChE inhibition task, GBDT demonstrated the best performance (RMSE = 0.683, MAE = 0.500, R(2) = 0.721). The SVM algorithm emerged as the most effective for MAO-B inhibition (RMSE = 0.668, MAE = 0.507, R(2) = 0.675). The SHAP algorithm was used to interpret the optimal models, identifying and analyzing the key substructures and properties for both dual-target and single-target inhibitors. Moreover, molecules docking process provided potential mechanism and Structure-Activity Relationships (SAR) of dual-target inhibition further.