ArticleMolecular diversity2026
Deep learning-assisted virtual screening of a large chemical library for selective GSK3β inhibitors.
Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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3 authors.
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Abstract
Glycogen synthase kinase-3β (GSK3β) is a serine/threonine kinase involved in neurodegenerative, neuropsychiatric, and oncological disorders. The development of selective inhibitors remains challenging due to high kinase conservation, off-target effects, and suboptimal pharmacokinetic properties. A curated dataset of GSK3β molecules was obtained from PubChem, and molecular descriptors were calculated using PaDEL. GSK3BMTPred, a multitask deep neural network (DNN) model, was developed for simultaneous prediction of inhibitor classification and inhibitory potency. The optimized model achieved a training accuracy of 0.9809 for the classification task and a training coefficient of determination (R
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