Quantitative Structure-Activity Relationship Analysis and a Combined Ligand-Based/Structure-Based Virtual Screening...

Area of Science:

• Biochemistry
• Medicinal Chemistry
• Computational Biology

Background:

• Glycogen synthase kinase-3 (GSK-3) is a key regulator of cellular processes.
• GSK-3β is a significant therapeutic target for neurodegenerative and metabolic diseases.
• Novel inhibitors are needed to effectively target GSK-3β.

Purpose of the Study:

• To identify structurally novel inhibitors of GSK-3β.
• To validate a combined ligand-based and structure-based virtual screening approach.
• To discover potent GSK-3β inhibitors for potential therapeutic applications.

Main Methods:

• A hierarchical quantitative structure-activity relationship (QSAR) model was developed to integrate diverse experimental data.
• Support vector machines and random forests were used for predictive model construction.
• Virtual screening combined QSAR analysis with molecular docking predictions.

Main Results:

• A hierarchical QSAR model achieved an R² of 0.752 on a test set of 141 compounds.
• Virtual screening identified 2 hit compounds with sub-micromolar inhibitory activity against GSK-3β.
• The validated approach demonstrated effectiveness in discovering novel GSK-3β inhibitors.

Conclusions:

• The combined ligand-based/structure-based virtual screening approach is effective for identifying GSK-3β inhibitors.
• The identified hit compounds represent promising leads for developing new therapeutics.
• This study provides a validated computational strategy for drug discovery targeting GSK-3β.