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<p>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 (NEF<sub>0.5</sub>% = 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 <i>Clausena</i> and <i>Psoralea</i> 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.</p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Ya Zhou,
  • Ben-Rong Mu,
  • Xing-Yi Chen,
  • Li Liu,
  • Qing-Lin Wu,
  • Mei-Hong Lu,
  • Feng-Ling Qiao

摘要

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.

Graphical Abstract