The logic of the target is compelling. GSK-3β is a constitutively active serine/threonine kinase that phosphorylates more than 100 substrates, governing glucose disposal, lipid metabolism, inflammation and fibrogenesis. Under normal conditions, insulin signalling activates the PI3K/Akt cascade, which phosphorylates GSK-3β at Ser9 and switches it off, freeing glycogen synthase to build glycogen stores in liver and muscle. In insulin resistance—the shared root of both type 2 diabetes and MASH—that brake fails. Hyperactive GSK-3β keeps glycogen synthase locked in its inactive, phosphorylated state, degrades insulin receptor substrate-1 through inhibitory serine phosphorylation, stabilizes the gluconeogenic factors PGC-1α and FOXO1, and drives hepatic glucose output. In the liver, it fuels SREBP-1c-mediated lipogenesis, undermines AMPK-driven fatty acid oxidation, sustains NF-κB inflammatory signalling and promotes TGF-β/Smad-driven collagen deposition in hepatic stellate cells. Preclinical rodent studies have repeatedly shown that inhibiting or deleting GSK-3β improves insulin sensitivity and blunts steatosis, inflammation and fibrosis.

Existing GSK-3 inhibitors have not translated into metabolic medicine. Lithium is non-selective with a narrow therapeutic window; AR-A014418 remains a biochemical tool compound; tideglusib, a clinical candidate for neurodegenerative disease, crosses the blood–brain barrier—an undesirable trait for chronic peripheral therapy—and LY2090314 and elraglusib have been pursued in oncology. Shared obstacles include poor selectivity between the GSK-3α and GSK-3β isoforms, whose ATP sites are nearly identical, and the theoretical oncogenic risk of sustained β-catenin stabilization. These gaps motivated the research team, led by Lateef Bello and colleagues at Adekunle Ajasin University in Nigeria, to search for peripherally restricted, structurally novel scaffolds in microbial chemistry.

The screening cascade began with 36,545 microbial natural products from the Natural Products Atlas. Applying Lipinski’s Rule of Five in DataWarrior retained 21,390 drug-like compounds—58.5 percent of the library—suggesting that microbial metabolites occupy an unusually favourable region of chemical space. A four-feature energy-optimized pharmacophore, derived from the crystal structure of human GSK-3β bound to the ATP-competitive inhibitor 7YG (PDB ID 4ACC), then filtered the library down to just 218 compounds matching three aromatic ring features and one hydrogen-bond donor. This ligand-based step eliminated 99 percent of candidates before any computationally expensive docking was attempted.

Critical to the study’s credibility was rigorous protocol validation. Re-docking the co-crystallized ligand reproduced the crystallographic pose with a root mean square deviation of 0.8357 angstroms, well within the conventional 2.0 angstrom cutoff. Enrichment analysis using 25 known GSK-3β inhibitors from ChEMBL and 1,255 generated decoys demonstrated exceptional discriminatory power: a ROC value of 0.98, an area under the curve of 0.97, and full recovery of all active compounds within the top 5 percent of the ranked list—a twenty-fold enrichment over random selection.

Structure-based docking then proceeded through three escalating precision tiers. High-throughput virtual screening cut the 218 pharmacophore hits to 178; standard precision docking at a −6.0 kcal/mol threshold retained 88 compounds, and a −6.5 kcal/mol cut kept 69. Extra-precision (XP) docking of the 50 best-ranked compounds ultimately yielded six hits—NPA011425, NPA014875, NPA020257, NPA004276, NPA034843 and NPA036064—with XP scores ranging from −6.858 to −11.059 kcal/mol. Every one of them outperformed the co-crystallized reference ligand (−6.455 kcal/mol), AR-A014418 (−5.929 kcal/mol) and tideglusib (−5.895 kcal/mol). All six formed hydrogen bonds with the conserved hinge-region residues Asp133, Val135 and Pro136 while packing against the hydrophobic ATP-binding pocket lined by Ile62, Val70, Ala83, Leu132 and Leu188, a signature consistent with ATP-competitive inhibition.

Independent scoring methods corroborated the docking results. Molecular Mechanics Generalized Born Surface Area (MMGBSA) calculations gave NPA014875 a binding free energy of −62.67 kcal/mol, essentially matching the native ligand’s −63.15 kcal/mol and far exceeding the reference inhibitors. Induced Fit Docking, which permits the binding site to relax around each ligand, confirmed receptor-adaptive binding: NPA014875 scored −730.74 kcal/mol, comparable to AR-A014418 and superior to both the reference ligand and tideglusib, while several compounds gained new interactions—such as π–π stacking with Phe67 or cationic contacts with Arg141—once side-chain flexibility was introduced. A machine-learning AutoQSAR model, trained on 1,500 curated ChEMBL inhibitors with an R-squared of 0.7123, predicted the highest potency for NPA020257, with a pIC50 of 6.82.

The pharmacokinetic and safety picture was equally encouraging. All six hits showed zero Lipinski violations, molecular weights between 308 and 430 g/mol, and favourable oral bioavailability scores. Crucially, none was predicted to cross the blood–brain barrier, whereas tideglusib was—a property the authors highlight as a therapeutic advantage, since central GSK-3β inhibition has been linked to neuropsychiatric effects and circadian disruption. Four of the six compounds showed no predicted inhibition across the five major cytochrome P450 isoforms, an important consideration for diabetic patients on polypharmacy. Toxicity predictions with ProTox-3.0 assigned all hits to class IV, with none predicted cytotoxic or cardiotoxic. The only flags were specific: NPA011425 alone was predicted hepatotoxic, NPA004276 showed oestrogen-receptor activity, and NPA014875 and NPA004276 were predicted active in mitochondrial membrane potential assays—a liability that matters in MASH, where hepatocyte mitochondrial reserve is already compromised.

Reading all five evaluation axes together—docking score, binding free energy, induced-fit performance, predicted potency and toxicity liabilities—the study names NPA020257 and NPA034843 as the most balanced candidates, both carrying zero major predicted liabilities. NPA014875 stands out as the strongest calculated binder but requires lead optimization against its mitochondrial flag; NPA011425 and NPA004276 remain affinity-rich chemotypes with clearly defined medicinal-chemistry objectives; and NPA036064 offers the cleanest safety profile as a backup scaffold. The authors are careful to frame the composite ranking as a triage map rather than a verdict, keeping all six compounds in play.

The limitations are candidly acknowledged. Every finding is computational and awaits experimental confirmation: enzymatic GSK-3β assays, kinase selectivity profiling against GSK-3α and broader panels, molecular dynamics simulations of complex stability, and validation of the predicted ADMET profiles in cellular and animal models of diabetes and MASH. The AutoQSAR model was trained largely on synthetic inhibitors, and no formal applicability-domain check was performed for the natural product hits. Still, the work demonstrates the power of a disciplined virtual screening cascade to convert a massive natural product database into a short, chemically diverse list of prioritized hits—hits that, if validated, could one day deliver what current medicine cannot: a single molecule that simultaneously restores insulin sensitivity and halts the inflammatory, fibrotic progression of fatty liver disease.

Subject of Research: Computational identification of microbial natural product inhibitors of glycogen synthase kinase 3 beta as candidate dual therapeutics for type 2 diabetes mellitus and MASH.

Article Title: Computational screening of the natural products atlas identifies potential glycogen synthase kinase 3 beta inhibitors as candidate therapeutics for type 2 diabetes mellitus and MASH

Article References: Bello, L., Nwankwo, D. O., Shodehinde, S. A., Akerele, G. P., Okuntimehin, B., Ogunjobi, E. G., Awelewa, O. V., Abass, O. A., Oginni, S. A., & Olubode, S. O. (2026). Computational screening of the natural products atlas identifies potential glycogen synthase kinase 3 beta inhibitors as candidate therapeutics for type 2 diabetes mellitus and MASH. Discover Chemistry, 3(1), Article 536. https://doi.org/10.1007/s44371-026-00989-8

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00989-8

Keywords: GSK-3β, type 2 diabetes, MASH, Natural Products Atlas, virtual screening, molecular docking, MMGBSA, induced fit docking, AutoQSAR, ADMET, drug discovery, microbial natural products