Virtual screening study to identify GSK-3β inhibitors: A combined ligand-based and structure-based drug discovery approach

Abstract

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting millions worldwide. While its aetiology is complex, a central role is attributed to the dysregulation of amyloid-beta (Aβ) protein homeostasis. Emerging evidence supports the involvement of glycogen synthase kinase-3β (GSK-3β) in AD pathogenesis through its influence on Aβ production and accumulation. Inhibiting GSK-3β is considered a promising therapeutic strategy to mitigate Aβ-related neurotoxicity. This study employed ligand-based drug design and computational modelling to identify novel GSK-3β inhibitors. Leveraging the pharmacophore of the known inhibitor CX-4945, a virtual screening campaign was conducted against the Molport database. The resulting hits were subjected to rigorous filtering based on drug-likeness and PAINS criteria. Subsequent docking and molecular dynamics simulations identified MolPort-002-524-637 and MolPort-006-387-505 as promising candidates. These compounds exhibited superior binding affinities compared to CX-4945 and displayed favourable in silico ADME/Tox properties.