Integrated machine learning and deep learning-based virtual screening framework identifies novel natural GSK-3β inhibitors for Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder lacking effective therapies. Glycogen synthase kinase-3β (GSK-3β), a key regulator of Aβ aggregation and Tau hyperphosphorylation, has emerged as a promising therapeutic target. Here, we present a novel two-stage virtual screening (VS) framework that integrates an interpretable random forest (RF) model (AUC = 0.99) with a deep learning-based molecular docking platform, KarmaDock (NEF0.5% = 1.0), to identify potential GSK-3β inhibitors from natural products. The model's interpretability was enhanced using SHAP analysis to uncover key fingerprint features driving activity predictions. A curated natural compound library (n = 25,000) from TCMBank and HERB was constructed under drug-likeness constraints, and validated using multi-level decoy sets. Three compounds derived from Clausena and Psoralea exhibited favorable pharmacokinetic profiles in silico, including blood-brain barrier permeability and low neurotoxicity. Molecular docking, pharmacophore modeling, and molecular dynamics simulations confirmed their stable interactions with critical GSK-3β binding sites. Notably, our approach combines explainability and deep learning to enhance screening accuracy and interpretability, addressing limitations in traditional black-box models. While current findings are computational, they offer theoretical support and provide actionable leads for future experimental validation of natural GSK-3β inhibitors.