Accurate prediction of acetylcholinesterase (AChE) inhibitory activity is important in drug discovery and environmental toxicology because AChE inhibition represents a key mechanism underlying neurotoxicity associated with pharmaceuticals and environmental contaminants. In this study, machine learning approaches were used to develop predictive models for AChE inhibitory activity using experimentally measured bioactivity data for small molecules targeting human AChE. A curated dataset containing 5795 molecules was compiled from BindingDB to support reliable model development. Fifteen predictive models were evaluated, including twelve individual machine learning and deep learning models and three hybrid fusion models, using multiple molecular representations such as physicochemical descriptors derived from RDKit and PaDEL and graph-based molecular structures. Among the individual models, tree-based ensemble methods demonstrated strong baseline performance, indicating that physicochemical descriptors capture important chemical features associated with AChE inhibition. Graph neural networks, particularly Graph Isomorphism Network effectively learn structural patterns related to inhibitory activity. To integrate complementary molecular information, a late-fusion hybrid framework combining descriptor-based predictions and graph-based representations was implemented using leakage-safe stacking with a Ridge regression meta-learner. Across ten independent train-test splits, the best-performing hybrid model integrating PaDEL-based XGBoost and GIN achieved r2 = 0.7400 ± 0.0138, demonstrating improved and stable predictive performance over individual models.
Laskar et al. (Wed,) studied this question.