Machine learning guided virtual screening of FDA approved drugs targeting GSK-3β in Alzheimer's disease.

Alzheimer's disease (AD) remains one of the most challenging neurodegenerative disorders, with limited therapeutic options and high failure rates in clinical trials. This work developed a drug repurposing pipeline powered by a machine learning (ML) model to find possible glycogen synthase kinase-3 beta (GSK-3β) inhibitors, a crucial target in AD pathogenesis. We selected, pre-processed, and optimized a dataset of 4,087 experimentally verified GSK-3β inhibitors using dimensionality reduction and descriptor creation. The most excellent prediction performance was obtained by Random Forest (100 descriptors) out of six supervised ML algorithms that were studied (R2 = 0.8178, RMSE = 0.8118, MAE = 0.6084). Following the virtual screening of 1,616 Food and Drug Administration (FDA)-approved drugs using this refined model, many compounds with projected IC₅₀ < 500 nM were found. Docking experiments showed insightful interactions and high binding affinities with the active-site residues of GSK-3β. With the best docking score (-9.3 kcal/mol), stable molecular dynamics (Average RMSD values (1000 ns): protein, 2.23 ± 0.93 Å; protein-ligand complex, 1.40 ± 0.43 Å) and long-lasting contacts with crucial residues, dolutegravir stood out among the top choices. ADMET profiling validated good pharmacokinetics and safety characteristics; however, possible hepatotoxicity needs more research. A HOMO-LUMO gap of 3.07 eV was found by density functional theory (DFT) analysis, indicating robust electron transport characteristics and balanced reactivity that are favorable for protein-ligand interaction. Together, these findings show that dolutegravir is a potential repurposable option against AD and how integrative ML, docking, MD, ADMET, and quantum chemistry techniques may speed up the identification of new drugs.