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

The core problem is homology. The catalytic domain of MMP-9, where zinc-mediated peptide cleavage takes place, is architecturally almost indistinguishable from the catalytic domains of its more than twenty family members. Inhibitors designed to wedge into the catalytic cleft of one isoform tend to bind the others with nearly equal enthusiasm, and it is this promiscuity that is widely blamed for the musculoskeletal side effects that halted broad-spectrum matrix metalloproteinase inhibitors such as batimastat and marimastat in late-stage clinical trials during the 1990s. Blocking a family of enzymes that also performs essential physiological tissue remodelling, the reasoning goes, produces collateral damage that patients cannot tolerate. The field’s response has been a search for isoform-specific features, and one such feature has emerged as the most promising: the S1′ specificity pocket, the subsite that accommodates the side chain of a substrate’s substrate-position-one-prime residue.

Unlike the conserved catalytic machinery, the S1′ pocket varies substantially across MMP isoforms in size, shape and residue composition. In MMP-9, crystal structures reveal a pocket whose geometry differs from that of related gelatinases and other family members, offering a toehold for selectivity. Structural work on MMP-9 in complex with hydroxamate inhibitors, and later studies highlighting the contribution of the flexible Arg424 side chain, established that ligands filling the S1′ pocket can engage residues that simply do not exist in the same conformation elsewhere in the family. What had not been done, according to the Indian team, was a systematic fragment-based drug design campaign explicitly targeted at this pocket. Fragment-based approaches start from small, low-molecular-weight molecules that bind weakly but efficiently, and grow or link them into larger, higher-affinity ligands, a philosophy that has produced several approved drugs and that lends itself naturally to exploiting small, subtle binding cavities.

The study’s workflow began with fragment-based virtual screening of commercially available fragment libraries from suppliers including Enamine, ChemBridge, FCH Group and Otava Chemicals. The researchers prepared the MMP-9 receptor structure carefully, following established protein and ligand preparation protocols known to influence the enrichment quality of virtual screens, and docked the fragment collection into the S1′ pocket using high-throughput virtual screening followed by progressively more rigorous docking tiers. This triage surfaced low-molecular-weight fragments with moderate predicted binding affinity toward the pocket. Among them, one promising fragment hit was selected for optimisation using a receptor cavity-guided fragment-growing strategy, in which chemical groups are appended to the parent fragment so that they extend into neighbouring space, in this case reaching toward residues lining the adjacent S2′ pocket.

Growing a fragment is only as good as the poses it produces, so the team turned to induced-fit docking, a method that permits flexibility in both the ligand and the binding-site side chains. This step identified several optimised molecules whose predicted orientations within the S1′ pocket were favourable and whose binding affinities exceeded those of the parent fragment. Crucially, docking scores alone are notoriously optimistic, so the investigators subjected the protein-ligand complexes to molecular dynamics simulations of 200 nanoseconds each, tracking root mean square deviation, root mean square fluctuation, solvent accessible surface area, radius of gyration and secondary structure stability throughout. Four of the optimised molecules maintained stable binding interactions over the full simulation, their key contacts with the S1′ pocket persisting as the protein breathed and flexed around them.

Selectivity, the whole point of the exercise, was then interrogated directly. The team redocked the four stable hits against a panel of MMP isoforms and compared binding free energies calculated with the molecular mechanics/generalized born surface area, or MM-GBSA, method. The analysis demonstrated a preferential affinity of the hit molecules for MMP-9, with favourable energetic profiles and orientations driven specifically by their occupancy of the non-conserved S1′ pocket. While computational selectivity is not a substitute for measured enzyme inhibition, the concordance of binding orientation, free energy and pocket engagement across independent calculations suggests a genuine structural basis for discrimination between MMP-9 and its close relatives rather than a numerical artefact of any single scoring function.

Drug-likeness was assessed in parallel. In silico ADME profiling of all four stable hits indicated favourable pharmacokinetic properties, an encouraging sign for molecules descended from fragments, which typically begin life with excellent solubility and ligand efficiency. The team also applied density functional theory calculations, mapping electrostatic potential and the energies of the highest occupied and lowest unoccupied molecular orbitals to understand the electronic distribution and chemical reactivity of each candidate. These quantum chemical descriptors provide medicinal chemists with a preview of how the molecules might behave in biological systems, flagging reactive sites and electronic features that correlate with metabolic stability and target engagement. Collectively, the computational evidence supports the four hits as credible starting points for synthetic optimisation.

The significance of the work lies less in any single molecule than in the framework it establishes. By demonstrating that an integrated pipeline of fragment screening, cavity-guided growing, induced-fit docking, long molecular dynamics simulation, cross-isoform MM-GBSA comparison, ADME prediction and quantum chemical analysis can converge on molecules that discriminate MMP-9 from its family members, the study offers a reproducible template for one of medicinal chemistry’s most stubborn selectivity problems. It also reinforces a lesson from the broader fragment-based literature: fragments, precisely because they are small and efficient, are exquisitely sensitive to the fine geometric differences between protein pockets, making them ideal probes for pockets as subtly differentiated as the S1′ site. The authors acknowledge that the findings are computational and that supporting data are available on request; experimental validation of binding and enzyme inhibition will be the necessary next step.

If the hits translate, the implications extend beyond oncology. MMP-9 has been implicated in inflammatory, cardiovascular and neurodegenerative conditions, and a selective inhibitor scaffold that spares other matrix metalloproteinases could reopen a therapeutic avenue that clinical failures closed a generation ago. For cancer patients, the prospect is an anti-metastatic agent that disarms the tumour’s remodelling machinery without destabilising the normal tissue turnover on which health depends. The Hyderabad team’s S1′-targeted fragment strategy provides the molecular blueprint for pursuing that goal rationally, transforming a protein family once written off as undruggable-without-toxicity into a target whose selectivity problem now has a concrete, structurally grounded path forward.

Part of what makes the S1′ pocket such an attractive lever for selectivity is its functional role in catalysis. The MMP active site centres on a catalytic zinc ion coordinated by three histidines, and substrate cleavage depends on how the residue immediately N-terminal to the scissile bond slots into the S1′ subsite. Because different isoforms prefer different P1′ side chains, the depth and hydrophobic character of this pocket directly shape each enzyme’s substrate repertoire, and MMP-9’s preference for bulky, hydrophobic P1′ residues is reflected in a pocket architecture that diverges measurably from even its closest gelatinase relative, MMP-2.

The fragment-based philosophy the study adopts has a well-documented pedigree. Since the landmark SAR-by-NMR work of the late 1990s, fragment approaches have delivered approved drugs across multiple target classes, largely because small starting points leave ample chemical space for optimisation while maintaining favourable ligand efficiency. Commercial fragment libraries from vendors such as Enamine and ChemBridge typically enforce strict rules on molecular weight, hydrogen-bond donors and acceptors, and calculated lipophilicity, ensuring that hits remain tractable for the growing stage that follows.

It is also worth noting that MMP-9 offers domains beyond the catalytic site as selectivity handles. Its C-terminal hemopexin domain, organised into four propeller blades, mediates substrate interactions distinct from zinc-dependent cleavage, and earlier work showed that antibodies or ligands engaging this domain can block gelatin degradation without touching the catalytic machinery. The present study’s focus on the S1′ pocket complements such allosteric and exosite strategies, giving medicinal chemists multiple, mechanistically different entry points toward the same long-elusive goal of an isoform-selective MMP-9 inhibitor.

Subject of Research: Fragment-based computational design of selective MMP-9 inhibitors targeting the S1′ pocket

Article Title: Addressing the selectivity challenge in matrix metalloproteinases: fragment-based design of MMP-9 inhibitors targeting the S1′ pocket

Article References: Kumar, A., Md Sameer, Shaikh, A. S., Chauhan, Y., & Kaki, V. R. (2026). Addressing the selectivity challenge in matrix metalloproteinases: fragment-based design of MMP-9 inhibitors targeting the S1′ pocket. Molecular Diversity. https://doi.org/10.1007/s11030-026-11723-y

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11723-y

Keywords: MMP-9, fragment-based drug design, S1' pocket, selectivity, induced-fit docking, molecular dynamics, MM-GBSA, anticancer drug discovery, ADME, density functional theory, extracellular matrix, matrix metalloproteinase inhibitors