ArticleScientific reports2025
Machine learning guided virtual screening of FDA approved drugs targeting GSK-3β in Alzheimer's disease.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Deep learning-assisted virtual screening of a large chemical library for selective GSK3β inhibitors.Molecular diversity · 2026Article
- Applying molecular dynamics simulations to validate OCT1 substrates uncovers vitamin K1 as a high-affinity ligand.Frontiers in pharmacology · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (R
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Registered trials
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