The clinical urgency is difficult to overstate. H. pylori infects a large proportion of the global population and is firmly linked to chronic active gastritis, peptic ulcer disease, gastric mucosal atrophy, intestinal metaplasia, and gastric adenocarcinoma. Prospective cohort evidence has connected infection to subsequent gastric cancer development, and meta-analytic data indicate that eradication therapy can reduce cancer risk in appropriate populations. The Kyoto global consensus went so far as to define H. pylori gastritis as an infectious disease that should be diagnosed and treated wherever detected. Yet the therapeutic arsenal is weakening. The Maastricht VI/Florence Consensus Report urges clinicians to tailor regimens to local resistance patterns and prior antibiotic exposure, while the 2024 American College of Gastroenterology guideline now advises against empirical clarithromycin triple therapy unless susceptibility has been confirmed, recommending 14-day optimized bismuth quadruple therapy as the default empirical option when susceptibility is unknown.

Into this resistant landscape, the study’s authors introduce a deliberately small and transparent computational experiment. They selected three targets representing complementary biological functions: H. pylori urease, the nickel-dependent virulence enzyme that allows the bacterium to survive gastric acid by hydrolyzing urea; DNA gyrase subunit A, essential for DNA topology and replication; and shikimate kinase, part of a biosynthetic pathway present in many microorganisms and plants but absent from humans. Crystallographic structures from the Protein Data Bank served for urease (PDB 1E9Y, with its nickel-containing architecture carefully preserved) and shikimate kinase (PDB 1ZUH), while DNA gyrase A was represented by an AlphaFold model because a suitable experimental structure for blind docking was unavailable. Ligands were docked blindly using CB-Dock2, an automated server combining cavity detection with Vina-type scoring, and the resulting poses were inspected in three dimensions and mapped residue-by-residue in two-dimensional diagrams.

The choice of ligands was grounded in prior experimental data rather than computational convenience. Niclosamide, a salicylanilide anthelmintic long used against tapeworms, had already demonstrated striking anti-H. pylori activity in vitro: it inhibited strain 60190 with a minimum inhibitory concentration of 0.25 micrograms per milliliter, remained active at acidic pH, showed partial synergy with metronidazole and proton pump inhibitors, reduced bacterial adhesion and invasion of gastric epithelial cells by roughly 3 log CFU, and produced no detectable resistance after prolonged exposure in a Galleria mellonella infection model. Berberine, a quaternary ammonium alkaloid from plants such as Berberis species, carries clinical credentials of its own. A randomized phase IV trial found that a 14-day quadruple regimen containing berberine achieved eradication rates of 90.1 percent by intention-to-treat and 91.3 percent per protocol, non-inferior to a bismuth-containing regimen, and a later meta-analysis reported that adding berberine to standard triple therapy improved eradication with a risk ratio of 1.22.

The docking results positioned both candidates favorably. Against urease, niclosamide and berberine tied for the most negative score at –8.5 kcal/mol, outperforming levofloxacin’s –7.9 kcal/mol. Berberine again led against DNA gyrase A at –8.5 kcal/mol, ahead of niclosamide at –7.8 and levofloxacin at –7.5 kcal/mol. Shikimate kinase proved the least favorable target overall, with berberine at –7.3, levofloxacin at –7.0, and niclosamide at –6.9 kcal/mol. Across all three targets, berberine posted the most negative descriptive mean score at –8.10 ± 0.69 kcal/mol, compared with –7.73 ± 0.80 for niclosamide and –7.47 ± 0.45 for levofloxacin. The authors are categorical that these Vina-type outputs are ranking scores only, not binding free energies or affinity constants; a favorable docked score can occur even for a compound that fails to bind in solution.

Perhaps the most technically instructive finding emerged from the two-dimensional interaction maps: identical scores can mask entirely different binding modes. At urease, where niclosamide and berberine shared the same –8.5 kcal/mol value, their predicted stabilization patterns diverged sharply. Berberine’s pose relied mainly on pi-alkyl, pi-sigma, and hydrophobic contacts with residues including LEU A:13, ALA A:16, VAL A:33, PHE B:441, and TYR A:32, while niclosamide’s pose combined conventional hydrogen bonds with hydrophobic, pi-related, and van der Waals contacts involving VAL B:473, TYR B:475, PRO B:472, and MET A:12. At DNA gyrase A, berberine’s aromatic contacts with PHE A:549 and TYR A:525 drove its top ranking, whereas levofloxacin interacted with ASP A:91, ARG A:95, and GLN A:98, including one unfavorable positive-positive contact. The authors caution, however, that the isolated DNA gyrase A model cannot reproduce the DNA–enzyme–drug ternary complex on which fluoroquinolone pharmacodynamics actually depends.

The ADMET profiling added a sobering pharmacokinetic dimension. All three compounds showed zero Lipinski rule-of-five violations and identical bioavailability scores of 0.55, with high predicted gastrointestinal absorption, but their developability profiles differ meaningfully. Niclosamide’s consensus logP of 3.78 and topological polar surface area of 49.33 square angstroms predict membrane permeability, yet its poor aqueous solubility makes formulation-oriented work essential. Berberine’s permanent quaternary cationic structure, which was preserved in the docking preparation, is associated with complex, formulation-dependent exposure that demands experimental confirmation. Neither descriptor set, the study emphasizes, constitutes evidence of therapeutic suitability; they flag questions about exposure, solubility, and formulation that only laboratory measurement can resolve.

The methodological self-awareness sets this study apart from much of the computational drug-repurposing literature. The authors explicitly note that no redocking validation, receiver operating characteristic benchmarking, molecular dynamics simulations, or MM/GBSA or MM/PBSA free-energy analyses were performed, meaning pose accuracy and screening enrichment cannot be claimed. Alternative protonation and tautomeric states of the ligands were not systematically enumerated, a constraint that can influence both poses and scores. Thermodynamically, they explain, a docking score approximates none of the free-state, solvation, desolvation, and entropic contributions required for a true binding free energy, and virtual screening workflows carry substantial false-positive rates. With only three ligands and three targets, no inferential statistics were applied; all summaries are purely descriptive of this specific matrix.

The study also situates itself honestly within prior evidence. The docking results neither reproduce niclosamide’s previously observed antibacterial effects nor identify their mechanism; instead they generate a separate, target-oriented hypothesis that urease, gyrase, and shikimate kinase engagement could be tested experimentally. Similarly, the mixed clinical record of berberine—a more recent randomized trial found no clear superiority for berberine-containing triple therapy, and reported MIC values against H. pylori vary widely by strain and method—means the alkaloid should be viewed as a scaffold for mechanistic and formulation-focused testing rather than a docking-supported monotherapy. The shikimate kinase results, with their small score differences, are downgraded to a lower-priority experimental hypothesis despite the target’s theoretical selectivity advantage.

What remains is a deliberately restricted but actionable set of next steps. The authors propose urease inhibition assays as the first priority, followed by broth microdilution or agar-dilution MIC determination against reference and clinical H. pylori strains, checkerboard combination testing with standard antibiotics, cytotoxicity screening on gastric epithelial cell models, and, if justified by results, molecular dynamics and post-docking free-energy analyses. Within this framework, berberine earns the most consistent score pattern, niclosamide remains the clearest repurposing candidate for urease-focused testing given its prior experimental pedigree, and levofloxacin serves its role as a clinically interpretable yardstick. None of these outputs demonstrates binding, enzyme inhibition, bacterial killing, or therapeutic efficacy—but as an exercise in reducing the experimental search space before costly microbiological testing, the study offers a model of how computational prioritization can be conducted with the caveats firmly attached.

Subject of Research: Computational docking and ADMET profiling of niclosamide and berberine against Helicobacter pylori urease, DNA gyrase A, and shikimate kinase

Article Title: Comparative molecular docking and ADMET profiling of niclosamide and berberine against multiple Helicobacter pylori enzymatic targets

Article References: Danila, C., Faur, L., Mocan, L., & Cavalu, D. S. (2026). Comparative molecular docking and ADMET profiling of niclosamide and berberine against multiple Helicobacter pylori enzymatic targets. Pharmacia, 73, Article e212118. https://doi.org/10.3897/pharmacia.73.e212118

Image Credits: AI Generated

DOI: 10.3897/pharmacia.73.e212118

Keywords: Helicobacter pylori, molecular docking, niclosamide, berberine, levofloxacin, ADMET, drug repurposing, urease, DNA gyrase, shikimate kinase, antimicrobial resistance, in silico screening

The research team, led by Defang Liu, Jiao Yan, Ting Luo and colleagues at the General Hospital of Western Theater Command in Chengdu, China, began with a rich resource: the Gene Expression Omnibus dataset GSE46480, containing peripheral blood mononuclear cell samples from 98 healthy volunteers after acute high-altitude exposure and 98 matched sea-level controls, profiled on the Affymetrix Human Genome U133 Plus 2.0 array. Applying rigorous differential expression analysis with the limma framework, they identified 1,103 differentially expressed genes, of which a striking 1,063 were upregulated and only 40 downregulated at altitude. Gene Ontology and KEGG enrichment analyses revealed that these genes clustered around ribonucleoprotein complex biogenesis, cytoplasmic translation, ribosomal structure and electron transfer activity, alongside a neurodegeneration-related pathway, painting a coherent picture of hypoxia disrupting protein synthesis and energy handling in immune cells.

On the intervention side, the team turned to the drug–food homology tradition rooted in the ancient Chinese medical text Huang Di Nei Jing, which holds that food and medicine share a common origin. They compiled the complete list of 106 substances officially recognized by the Chinese National Health Commission, then mined the Traditional Chinese Medicine Systems Pharmacology database for their chemical constituents. Applying conventional filters of drug-likeness of at least 0.18 and oral bioavailability of at least 30 percent yielded 610 candidate active components. The critical step came next: rather than relying on static databases, the researchers used GraphBAN, a graph neural network trained on the BindingDB, BioSNAP and KIBA datasets, to predict compound–protein interaction pairs with probability scores above 0.7. Intersecting predictions from the three pretrained models produced 754 high-confidence pairs linking 95 candidate compounds to 113 candidate genes.

Not every predicted binder deserves attention, however, so the team subjected the 95 compounds to a two-stage gauntlet on the ADMETlab3 platform. First came physicochemical assessment against Lipinski’s Rule of Five, evaluating molecular weight, hydrogen bond donors and acceptors, and lipophilicity, plus topological polar surface area as a proxy for membrane permeability; 27 compounds passed without violations. Then came toxicology: predictions for hERG cardiac channel inhibition, hepatotoxicity, drug-induced liver injury, carcinogenicity and acute oral toxicity, with compounds retained only if every toxicity probability fell below 0.5. Just 8 components survived this screen, and 6 cleared both hurdles, a high elimination rate the authors say underscores the value of multistage safety filtering before any candidate is proposed for human use.

To distill the gene list further, the researchers deployed a three-algorithm machine learning pipeline. A random forest model ranked genes by importance, the Boruta algorithm confirmed features with statistically robust relevance, and LASSO regression with 10-fold cross-validation selected a third set. The intersection of all three feature sets yielded just five genes: DNAJC19, MRFAP1L1, TBPL1, RPS21 and NABP1. Strikingly, all five traced back through the interaction network to a single drug–food homologous substance, Panacis Quinquefolii Radix, better known as American ginseng, and to two of its polyacetylene constituents designated PQ-2 and polyacetylene PQ-2, a pair of epimers whose stereochemical differences subtly shifted their predicted lipophilicity and toxicity profiles.

External validation added nuance to the story. In two independent datasets comparing samples at moderate and high altitude, expression trends were consistent with the discovery cohort, but only RPS21 reached significance in one, while a random-effects meta-analysis across both external datasets confirmed significant downregulation of MRFAP1L1, TBPL1 and NABP1 with essentially no inter-study heterogeneity. DNAJC19 showed a consistent downward trend of marginal significance, and RPS21 proved heterogeneous across cohorts, suggesting its expression may be swayed by study-specific factors such as altitude gradient, exposure duration and genetic background. The authors are candid that this inconsistency is a limitation, not a footnote.

The candidate genes were not random stragglers; they converged on a coherent functional architecture. Gene set enrichment analysis showed all five participating in 32 core pathways spanning ribosome function, oxidative phosphorylation, the spliceosome, protein export, DNA repair and apoptosis, with pathways such as apoptosis upregulated and axon guidance downregulated in the altitude group. The authors propose a division of labor: DNAJC19, a mitochondrial inner membrane chaperone, anchors energy metabolism; RPS21, a small ribosomal subunit protein, governs translation; TBPL1, part of the transcription factor IID complex, links to pulmonary fibroblast biology; MRFAP1L1 connects to chromatin regulation and genome stability; and NABP1 ties into RNA processing and complement–coagulation pathways. A nomogram built from the five genes distinguished altitude samples from controls with an area under the curve of 0.95 and excellent calibration, hinting at diagnostic potential.

Immune infiltration analysis deepened the physiological picture. Comparing 28 immune cell types between groups revealed a Th1/Tc1-skewed response at altitude: gamma delta T cells, effector memory and activated CD4 T cells expanded, while central memory CD8 T cells, natural killer T cells and CD56dim natural killer cells contracted. All five candidate genes showed significant correlations with multiple differential immune cells, with correlations reaching as high as 0.905 between MRFAP1L1 and activated CD4 T cells. The pattern suggests a statistical link between these genes and excessive inflammatory activation, weakened immune memory and cytotoxic injury, precisely the immune imbalance that characterizes high-altitude pathogenesis.

Finally, the team tested whether the two ginseng polyacetylenes could actually engage their predicted targets. Molecular docking with AlphaFold-predicted protein structures showed binding energies below −5.0 kcal/mol for DNAJC19, NABP1 and TBPL1, ranging from −5.2 to −6.2 kcal/mol, while RPS21 and MRFAP1L1 bound more weakly, likely reflecting incompatible pocket geometries. Hundred-nanosecond molecular dynamics simulations in GROMACS then confirmed stable complexes for the strong binders, with root mean square deviations holding near 0.15 to 0.30 nanometers, and MM-PBSA calculations yielded strongly negative binding free energies across all complexes, the strongest at −198.2 kcal/mol for MRFAP1L1 with polyacetylene PQ-2. The authors also emphasize the safety caveats: low predicted hERG inhibition and hepatotoxicity probabilities for both PQ-2 epimers support druggability, though these remain computational estimates.

The researchers are appropriately measured about what their findings mean. Every result here is in silico: the gene–immune correlations are statistical associations from bulk transcriptomic data inferred with the ssGSEA algorithm, the binding predictions rely on modeled protein structures, and no cell, animal or clinical experiment has yet tested whether American ginseng polyacetylenes actually mitigate altitude illness. Still, the convergence of transcriptomics, machine learning, network pharmacology and simulation on a single well-known edible herb offers a genuinely novel framework, and previous hints that PQ-2 and its epimer have never been reported as antihypoxic agents makes them intriguing leads. If future experimental work confirms the gene–compound axis identified here, the remedy for thin mountain air could someday be as familiar as the produce aisle, a possibility that makes this study one of the more provocative computational explorations in environmental physiology this year.

Subject of Research: Computational identification of high-altitude illness genes and drug–food homologous compound targets

Article Title: Transcriptomic identification of high-altitude illness candidate genes and physiological mechanisms: a multidimensional bioinformatics study with 106 drug–food homologous substance target prediction

Article References: Liu, D., Yan, J., Luo, T., Guo, Y., Li, Y., Long, P., & Yang, L. (2026). Transcriptomic identification of high-altitude illness candidate genes and physiological mechanisms: a multidimensional bioinformatics study with 106 drug–food homologous substance target prediction. Frontiers in Physiology, 17, Article 1752620. https://doi.org/10.3389/fphys.2026.1752620

Image Credits: AI Generated

DOI: 10.3389/fphys.2026.1752620

Keywords: high-altitude illness, bioinformatics, transcriptomics, machine learning, drug–food homology, Panacis Quinquefolii Radix, molecular docking, molecular dynamics, hypoxia, immune infiltration, ADMET, biomarkers

The biological target at the heart of the study is the RAF–MEK–ERK signaling pathway, often called the MAPK pathway, a molecular relay system that governs cell growth, differentiation, and survival. When mutations strike key proteins such as RAS or BRAF, this pathway can become permanently switched on, driving the uncontrolled proliferation that defines cancer. Lung cancer is particularly devastating in this regard: it was the leading cause of cancer-related death worldwide in 2022, claiming roughly 1.8 million lives and accounting for 18.7 percent of all cancer deaths. Existing drugs that target this pathway, including the BRAF inhibitors vemurafenib and dabrafenib and the MEK inhibitors trametinib and selumetinib, can slow tumor growth, but resistance almost invariably emerges, often within six to eight months of treatment. This relentless problem motivates the search for new chemical entities that can attack the pathway at its most vulnerable junctions.

The researchers chose MEK1, the kinase sitting between RAF and ERK in the cascade, as their molecular target. Because MEK1 directly controls the phosphorylation and activation of ERK, blocking it can suppress abnormal MAPK signaling even when upstream oncogenic mutations are present. Indolinone derivatives, a class of compounds built around an indole-like ring system fused to a ketone, have long been recognized as privileged scaffolds in kinase drug discovery because their framework tolerates extensive chemical modification. Yet the precise structural features that determine how strongly these molecules inhibit MEK1 remained poorly understood, creating an opportunity for a systematic computational approach.

To bridge that knowledge gap, the team assembled a dataset of 33 hybrid 3-substituted 5-(phenylamino) indolone derivatives with experimentally measured anticancer activities, expressed as IC50 values and converted to their logarithmic counterpart, pIC50. They calculated a suite of molecular descriptors capturing physicochemical, topological, and electronic properties using Gaussian 03W and ChemBioOffice 3D software, then split the dataset randomly into 27 training compounds and 6 test compounds in an approximate 80:20 ratio. Using multiple linear regression, they distilled the model down to six descriptors: Bend, Stretch-Bend, 1,4 VDW, NHA (the number of hydrogen-bond acceptors), TC (total connectivity), and TVC (total valence connectivity). The final equation achieved a coefficient of determination of 0.741 with a root-mean-square error of just 0.144, indicating that a handful of geometric and connectivity features could explain much of the variation in biological activity across the series.

Rigorous validation separated this model from statistical noise. In a Y-randomization test, the researchers scrambled the activity values 500 times while keeping the descriptor matrix intact; the randomized models produced a mean R² of only 0.1871 and a mean leave-one-out cross-validation coefficient of −0.3801, both dramatically lower than the original model’s values of 0.8739 and 0.7736. Variance inflation factor analysis confirmed that multicollinearity among the six descriptors was not severe, with all values falling below the conventional threshold of 10. External validation against the six withheld test compounds met the criteria established by Golbraikh and Tropsha, with slopes and intercepts close to ideal. Together, these checks suggested the model captured genuine structure–activity relationships rather than chance correlations, though the authors caution that the relatively small dataset limits how far its predictions can be generalized.

Armed with the validated equation, the team designed ten new molecules, Pred 1 through Pred 10, by strategically modifying substituents on the indolinone scaffold while keeping descriptor values within the chemical space of the original dataset. Predicted pIC50 values ranged from 4.395 to 6.306, with Pred 9 (6.046) and Pred 10 (6.306) standing out as the most potent virtual candidates, exceeding even the most active compound in the original series. The researchers also proposed hypothetical synthetic routes for these compounds based on previously published procedures, although they emphasize that no actual synthesis has been attempted and the feasibility of the proposed pathways remains unproven.

To probe how the two lead candidates might engage MEK1 at the atomic level, the team turned to molecular docking against the MEK1 crystal structure deposited in the Protein Data Bank under accession code 3EQG, resolved at 2.50 angstroms with the clinical inhibitor mirdametinib (PD-0325901) bound in its active site. Before trusting the docking protocol, they validated it by extracting the co-crystallized ligand and re-docking it into the same site; the re-docked pose overlaid the experimental conformation with a root-mean-square deviation of just 0.90 angstroms, well below the accepted 2.0 angstrom threshold. Pred 9 then docked with a predicted binding energy of −10.27 kcal/mol, forming hydrogen bonds with Val-127 and Asp-190 at distances of 1.92 and 1.98 angstroms, along with pi-cation, pi-anion, pi-sigma, and pi-sulfur contacts involving Lys-97, Asp-208, Ile-216, Met-219, and Met-143. Pred 10 scored even better at −10.49 kcal/mol, weaving six hydrogen bonds with Val-211, Ser-212, Phe-209, Asp-208, Lys-97, and Ala-76.

Docking snapshots, however, capture only a frozen moment. To test whether the predicted complexes would hold together under realistic thermal motion, the researchers ran 100-nanosecond molecular dynamics simulations with the Desmond engine and the OPLS3e force field. The Pred 9–MEK1 complex settled into a stable regime after roughly 30 nanoseconds, with ligand RMSD fluctuating between 1.60 and 2.80 angstroms and averaging about 1.85 angstroms. The Pred 10 complex stabilized around 2.8 angstroms after the same equilibration period. Contact analysis revealed remarkably persistent interactions: Ile-126 held a hydrogen bond with Pred 9 for approximately 90 percent of the simulation, while Asp-190 maintained a water-mediated bridge throughout. For Pred 10, Asp-208 and Phe-209 each sustained hydrogen bonds for about 70 percent of the run. Radius of gyration and residue fluctuation profiles remained within normal ranges for both systems, suggesting the protein core stayed compact and stable while the ligands explored their binding pockets.

Beyond binding, a drug candidate must survive the journey through the body. In silico ADMET predictions using SwissADME and pkCSM painted an encouraging preliminary picture. Pred 9 and Pred 10 were predicted to achieve intestinal absorption above 82 percent, show moderate volumes of distribution, and—critically—be Ames-negative, meaning the models forecast no bacterial mutagenicity, in contrast to M31, the most active compound from the original dataset, which was predicted Ames-positive. Both new candidates also showed higher predicted aqueous solubility than M31. Molinspiration analysis confirmed that both molecules satisfy Lipinski’s Rule of Five with zero violations, with molecular weights of 428.52 and 444.52 g/mol and topological polar surface areas of 81.59 and 101.82 square angstroms respectively. POM analysis, combining Petra, Osiris, and Molinspiration tools, identified negatively charged carbonyl oxygens and positively charged NH hydrogens as potential pharmacophoric sites associated with antitumor activity. However, the analysis flagged predicted inhibition of the CYP1A2 and CYP3A4 metabolic enzymes as a potential liability, since blocking these enzymes could trigger drug–drug interactions in patients taking other medications.

The authors are careful to frame their results as hypothesis-generating rather than proof of efficacy. Pred 9 and Pred 10 exist only as computer models; they have never been synthesized, tested against MEK1 in enzymatic assays, or evaluated in cancer cells or animals. Every predicted activity, binding energy, pharmacokinetic parameter, and toxicity flag in the study is a computational estimate that must be confirmed experimentally. Still, the work exemplifies a modern drug discovery paradigm in which QSAR modeling, molecular docking, molecular dynamics, and ADMET prediction converge to triage thousands of possibilities down to a handful of synthesizable candidates before expensive laboratory work begins. The research team, led by Mohammed Er-rajy of Sidi Mohamed Ben Abdellah University in Fez alongside collaborators including Somdutt Mujwar of Chitkara University and Abdelouahid Samadi of United Arab Emirates University, plans to pursue chemical synthesis and in vivo testing of the two lead compounds. If those experiments bear fruit, the indolinone scaffold could join the arsenal of MEK1-targeted weapons being developed for lung and other cancers—provided the virtual promise survives contact with biological reality.

Subject of Research: Computational design of indolinone-based MEK1 inhibitor candidates for anticancer drug discovery

Article Title: In silico design of novel indolinone-based anticancer agents and proposal of synthetic routes

Article References: Er-rajy, M., El Fadili, M., Etabti, H., Aziz, A., Lenda, F., Mujwar, S., Chtita, S., Samadi, A., & Elhallaoui, M. (2026). In silico design of novel indolinone-based anticancer agents and proposal of synthetic routes. Frontiers in Bioinformatics, 6, Article 1964610. https://doi.org/10.3389/fbinf.2026.1964610

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1964610

Keywords: indolinone, MEK1, QSAR, molecular docking, molecular dynamics, ADMET, RAF-MEK-ERK pathway, lung cancer, drug discovery, anticancer agents, computational chemistry, drug design

Alzheimer’s disease remains the leading cause of dementia worldwide, and one of its defining features is the gradual collapse of the cholinergic system, the network of neurons that relies on acetylcholine to carry signals involved in learning and memory. Two enzymes, acetylcholinesterase and butyrylcholinesterase, break down acetylcholine in the brain. In healthy tissue, acetylcholinesterase does most of the work, but as the disease progresses, butyrylcholinesterase activity rises and accelerates neurotransmitter depletion. Blocking both enzymes at once has therefore been a validated therapeutic strategy for easing cognitive symptoms, and it is the strategy the new compounds were designed to pursue.

The scaffold at the heart of the study is the dihydropyrimidinethione, a nitrogen-rich ring system first accessible through the Biginelli reaction, a three-component condensation of an aldehyde, a beta-ketoester, and thiourea that Pietro Biginelli reported in 1891. The reaction is prized for its atom economy and the structural diversity it permits, but its efficiency depends heavily on the catalyst. Traditional protocols use strong Brønsted acids such as hydrochloric acid, which can over-protonate sensitive substrates and trigger side reactions. The Iranian research team, led by Sepideh Shirbina and colleagues at the University of Guilan and Shiraz University of Medical Sciences, turned instead to trimethylsilyl chloride, a milder Lewis acid that coordinates to the carbonyl oxygen of the aldehyde and enhances its electrophilicity without the harsh protonation that damages acid-sensitive molecules.

Under optimized conditions, the team mixed a substituted aromatic aldehyde, methyl or ethyl acetoacetate, and thiourea in refluxing ethanol with 10 mol% TMSCl. Within 30 minutes, the reaction delivered the target dihydropyrimidinethiones in isolated yields ranging from 70 to 92 percent. Thirteen derivatives were prepared, spanning electron-donating groups such as methyl and hydroxyl, electron-withdrawing halogens including fluorine, chlorine, and bromine, and combinations of both. The protocol tolerated all of these variations without significant loss of efficiency, confirming that the transformation is largely insensitive to electronic effects on the aromatic ring. Compared with previously reported Biginelli catalysts, the TMSCl method offers a practical balance of speed, yield, and substrate scope, with simple precipitation and recrystallization as the workup.

With the library in hand, the researchers tested each compound against both cholinesterases using the modified Ellman assay. The results revealed a clear structure-activity relationship. On the acetylcholinesterase side, replacing the methyl ester with an ethyl ester consistently improved potency, and halogenated aromatic rings outperformed unsubstituted ones in the order bromine greater than chlorine greater than fluorine. The two best inhibitors were compound 1l, bearing a para-methyl group on the ethyl ester scaffold, with an IC50 of 65.30 micromolar, and compound 1j, carrying a meta-bromo substituent, with an IC50 of 67.33 micromolar. Ortho-substitution, by contrast, was detrimental, likely because steric crowding near the ring junction prevents proper alignment within the enzyme’s narrow active-site gorge.

Against butyrylcholinesterase, the pattern shifted. Ethyl derivatives again dominated, but the standout was compound 1m, which carries both a hydroxyl and a bromine on its aromatic ring. This polar-halogen combination produced an IC50 of 10.35 micromolar, placing it within reach of donepezil, the reference inhibitor used in the assay, which showed an IC50 of 10.6 micromolar under the same conditions. The finding is notable because it suggests that a carefully positioned polar group can enhance binding to butyrylcholinesterase even when hydrophobic bulk alone would predict otherwise, possibly by forming additional hydrogen bonds within the enzyme’s spacious pocket.

To understand how compound 1m achieves its potency, the team turned to enzyme kinetics. Lineweaver-Burk analysis showed that the inhibition is competitive, meaning the molecule occupies the same active site as the natural substrate and blocks it directly. The derived inhibition constant, Ki, was 10.92 micromolar, closely matching the IC50 value and confirming tight, specific binding rather than nonspecific denaturation of the enzyme.

Computational studies then provided a structural rationale. Docking simulations using the induced-fit protocol in Schrödinger’s suite, validated by redocking the co-crystallized ligand tacrine with an RMSD below 2.0 angstroms, placed compound 1m in the butyrylcholinesterase active site with a binding energy of minus 7.64 kilocalories per mole. The pyrimidine-5-carboxylate core formed a hydrogen bond with His438, the 2-hydroxy-5-bromophenyl group engaged in pi-pi stacking with Tyr322, and the ethyl ester carbonyl hydrogen-bonded with Tyr332. Molecular dynamics simulations over 100 nanoseconds showed the complex remained stable, with backbone RMSD fluctuations of about 1.5 angstroms compared with 1.7 angstroms for the unbound enzyme. Throughout the trajectory, compound 1m maintained persistent contacts with the catalytic active site, the peripheral anionic site, the oxyanion hole, and the choline-binding site, anchoring itself through a network of hydrogen bonds involving Gly119, Tyr128, Gly439, Thr120, Ser198, and Asp70, alongside pi-pi stacking with Trp82. The Prime/MM-GBSA calculation yielded a binding free energy of minus 41.19 kilocalories per mole, further supporting the stability of the complex.

Beyond potency, the team evaluated whether compound 1m could plausibly become a drug. In silico predictions using PKCSM and ADMETmesh tools showed the molecule complies with Lipinski’s rule of five and the GSK rule, with a molecular weight of 371.256 daltons, a topological polar surface area of 70.59 square angstroms, a logP of 3.372, and high predicted intestinal absorption of 89.51 percent. The compound is not predicted to inhibit major cytochrome P450 isoforms, reducing the risk of drug-drug interactions, though it is predicted to be a substrate of CYP2A4. Predicted hepatotoxicity, genotoxic carcinogenicity, and skin sensitization were all absent. Crucially, an MTT cytotoxicity assay on SH-SY5Y human neuroblastoma cells showed that even at 100 micromolar, cell viability remained above 88 percent, indicating a favorable safety margin for neuronal tissue.

The authors caution that compounds 1l and 1m are preliminary hits requiring further structural optimization and biological validation before any therapeutic application. Nevertheless, the study demonstrates that a mild, operationally simple TMSCl-catalyzed Biginelli reaction can rapidly generate a coherent series of cholinesterase inhibitors whose activity trends are fully rationalized by docking, dynamics, and free-energy calculations. As the search for next-generation Alzheimer’s treatments intensifies, the ability to synthesize and screen such libraries in a single afternoon, using inexpensive reagents and straightforward purification, offers a practical template for medicinal chemists working at the interface of classical heterocyclic chemistry and modern computational drug design.

Subject of Research: Synthesis and cholinesterase inhibitory activity of dihydropyrimidinethione derivatives prepared by TMSCl-catalyzed Biginelli reaction for Alzheimer's disease drug discovery

Article Title: TMSCl-promoted Biginelli synthesis of dihydropyrimidinethione derivatives as cholinesterase inhibitors: SAR, molecular docking, and molecular dynamics studies

Article References: Shirbina, S., Abedini, M., Mahmoodi, N. O., Taherpour, H., & Iraji, A. (2026). TMSCl-promoted Biginelli synthesis of dihydropyrimidinethione derivatives as cholinesterase inhibitors: SAR, molecular docking, and molecular dynamics studies. Results in Chemistry, 31, Article 103950. https://doi.org/10.1016/j.rechem.2026.103950

Image Credits: AI Generated

DOI: 10.1016/j.rechem.2026.103950

Keywords: Alzheimer's disease, cholinesterase inhibitors, Biginelli reaction, dihydropyrimidinethione, TMSCl catalysis, molecular docking, molecular dynamics, structure-activity relationship, butyrylcholinesterase, acetylcholinesterase, drug discovery, ADMET

The classical limitations of step-growth polymerization are well documented. Even minor stoichiometric imbalances, caused by monomer loss or side reactions, drastically reduce the number-average degree of polymerization. The most probable molecular weight distribution yields a dispersity approaching 2, with no built-in mechanism for narrowing it. Many reactive functional groups, such as amines and alcohols, interfere with catalysts or trigger unwanted side reactions, and conventional polycondensations often require extended reaction times, elevated temperatures, vacuum, or azeotropic distillation. Previous strategies to escape these limits include converting step-growth into chain-growth behavior by increasing end-group reactivity, as demonstrated by Yokoyama and Yokozawa, and Friedel-Crafts polycondensation approaches developed by Zou and co-workers that achieve molecular weight control without strict stoichiometric balance.

CuAAC, discovered independently by Sharpless and Meldal in 2002, represents the first generation of click polymerization. The accepted mechanism involves two copper atoms: copper first coordinates to the terminal alkyne and, after base-assisted deprotonation, forms a copper-acetylide complex in the rate-determining step with an activation energy of 50 to 70 kilojoules per mole. The azide then coordinates to a second copper atom in a bent geometry of roughly 120 degrees, the copper-acetylide attacks the terminal nitrogen to form a six-membered metallacycle, and ring contraction followed by protonolysis releases the 1,4-disubstituted 1,2,3-triazole product with greater than 99 percent regioselectivity. Ligands such as PMDETA and TBTA stabilize Cu(I) against oxidation, increase solubility, and prevent the formation of inactive clusters, while polar aprotic solvents like DMF and DMSO give the highest rates. El-Hamshary and colleagues showed that CuAAC click polymerization achieves more than 90 percent functionalization efficiency within four hours at room temperature, although metal residues exceeding 500 parts per million of copper remain a serious concern for biomedical applications.

Thiol-yne click polymerization, the second generation, eliminates the metal catalyst entirely. Under UV irradiation between 250 and 365 nanometers, the reaction proceeds through a radical chain mechanism: a photoinitiator such as DMPA undergoes homolytic cleavage to generate radicals, the resulting thiyl radical adds to the alkyne in an anti-Markovnikov fashion with an activation energy of 40 to 60 kilojoules per mole, and the vinyl sulfide radical abstracts a hydrogen from another thiol molecule to propagate the chain. Crucially, the vinyl sulfide intermediate still contains a double bond, and a second, slower thiyl radical addition follows, with the rate ratio between the first and second additions typically between 3 and 10. This sequential addition kinetics is a conceptual breakthrough, because it shows that rate differentiation enables controlled network formation with uniform structures and higher crosslink density than thiol-ene chemistry allows. The radical pathway produces anti-Markovnikov addition with 60 to 80 percent trans stereocontrol, since the trans isomer sits 5 to 10 kilojoules per mole lower in energy than the cis form.

For thick samples or large-scale production exceeding one kilogram, the UV requirement limits scalability, so the review highlights an alternative base-catalyzed pathway using organobases such as DMAP or triethylamine at 1 to 10 mole percent loading. Here the base deprotonates the thiol to form a thiolate anion, which adds to the electron-deficient alkyne via Michael addition, and the resulting vinyl sulfide anion abstracts a proton from another thiol to regenerate the thiolate. DMAP proves more effective than triethylamine because its conjugate acid is more stable, making it a better nucleophile. The base-catalyzed route works in open air and reaches 80 to 85 percent conversion, somewhat below the greater than 90 percent achieved under UV, but it avoids the oxygen exclusion that the radical pathway demands, since oxygen scavenges radicals and cuts reaction rates by 50 to 80 percent in air.

The third generation, spontaneous amino-yne click polymerization, requires no catalyst, initiator, or external energy input at all. Tang’s group demonstrated the synthesis of regio- and stereospecific poly(beta-aminoacrylate)s by reacting activated propiolates with aliphatic amines at room temperature. The mechanism, elucidated through DFT calculations and kinetic studies, begins when the amine lone pair attacks the beta-carbon of the electron-deficient alkyne, whose electron-withdrawing group lowers the LUMO energy from roughly minus 0.5 electronvolts for an unsubstituted alkyne to between minus 1.5 and minus 2.0 electronvolts. A zwitterionic intermediate forms and is stabilized by resonance, a fast proton transfer of only 10 to 20 kilojoules per mole occurs through a six-membered transition state, and tautomerization yields the enamine product with greater than 95 percent trans selectivity. The trans preference arises from minimized electrostatic repulsion in the zwitterionic intermediate and a thermodynamic advantage of 10 to 15 kilojoules per mole over the cis isomer. Reported activation energies of 35 to 45 kilojoules per mole are significantly lower than CuAAC’s 50 to 70, and electron-withdrawing groups increase reaction rates by 10 to 50 fold. The reaction reaches more than 95 percent conversion in 0.5 to 2 hours at room temperature in open air with a strict 1:1 amine-to-alkyne ratio and no excess required.

The reversible enamine bonds formed in amino-yne chemistry carry particular significance for materials design. Hydrogels produced through this route undergo reversible sol-gel transitions induced by pH, because the enamine bonds are acid-sensitive and preserve an equilibrium between amine, enamine, and aldehyde groups, suggesting applications in tissue engineering and stimuli-responsive biodegradable systems. Zhang’s group extended the chemistry to metallacycle-crosslinked supramolecular polymers with elastomer characteristics, enhanced mechanical strength, and self-healing behavior. The review also covers RAFT step-growth copolymers, which combine the functional group tolerance of controlled radical polymerization with step-growth versatility for drug delivery applications, and one-pot hyperbranched star copolymers synthesized by Bang and co-workers, who used rigorous mathematical derivation and proton NMR spectroscopy to quantify intramolecular cyclization defects in a single batch.

Perhaps the most consequential application discussed is the photodegradable precision polyketone. Commercial polyketones typically contain fewer than 5 ketone groups per 1000 methylene units, but ADMET step-growth polymerization can incorporate one ketone per 19th methylene unit, equivalent to 52 ketones per 1000 methylene units, with every ketone isolated from its neighbors by at least 18 methylene units. This regular spacing prevents crosslinking side reactions and ensures that Norrish type II cleavage occurs at every ketone, leading to complete backbone scission under appropriate conditions. The theoretical basis lies in orbital energetics: ketones exhibit a HOMO-LUMO gap of roughly 4.0 to 4.5 electronvolts, below the 4.5 electronvolt threshold needed for photochemical excitation, whereas esters exceed 5.0 and amides exceed 5.5 electronvolts, explaining why polyketones are photodegradable while polyesters and polyamides are not. These ADMET-derived polymers degrade under UV irradiation of at least 300 watts per square meter with half-lives of 7 to 20 days under accelerated conditions, and their glass transition temperature can be tuned from minus 20 to 150 degrees Celsius by varying monomer structure, from aliphatic to aromatic diamines.

Yet the review delivers a pointed caution on the popular claim of microplastic-free degradation. Complete backbone scission occurs only under controlled accelerated UV conditions with intensity of at least 300 watts per square meter, wavelengths of 254 to 365 nanometers, an aerobic atmosphere, and temperatures of at least 15 degrees Celsius, yielding small-molecule products such as aldehydes, ketones, and carboxylic acids with molecular weights below 1000 daltons. Under natural weathering, where UV intensities typically range from 10 to 50 watts per square meter, Norrish type II cleavage may be incomplete, potentially leaving higher molecular weight fragments that qualify as microplastics. Anaerobic environments such as marine sediments and landfills would substantially slow or prevent complete degradation, and comprehensive ecotoxicological testing of the degradation products according to OECD guidelines has not yet been performed. The authors conclude that regular chromophore spacing matters more than chromophore concentration for complete photodegradation, that metal-free click polymerizations achieve efficiency comparable to CuAAC while eliminating cytotoxicity, and that external energy input can be systematically reduced to zero through monomer electronic design. Before industrial adoption, however, natural weathering studies, standardized toxicity assays, and cost reduction strategies, including cheaper alternatives to Grubbs catalysts that currently exceed 500 dollars per gram, must take priority.

Subject of Research: Chemistry

Article Title: Thiol-yne and amino-yne click polymerizations advance step-growth polymerization toward sustainable and photodegradable polymers

Article References: Samada, L. H., & Daulay, A. (2026). Thiol-yne and amino-yne click polymerizations advance step-growth polymerization toward sustainable and photodegradable polymers. Discover Chemistry, 3(1), Article 434. https://doi.org/10.1007/s44371-026-00901-4

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00901-4

Keywords: click chemistry, step-growth polymerization, thiol-yne, amino-yne, CuAAC, polyketones, photodegradable polymers, ADMET, microplastics, sustainable polymers, Norrish type II, green chemistry

The team’s approach rests on the hybrid-molecule concept, in which two distinct bioactive pharmacophores are covalently joined into a single chemical skeleton so that their complementary strengths reinforce one another. The first fragment is indole, a ring system found throughout nature in molecules such as tryptophan and serotonin, and in approved drugs including indomethacin and sumatriptan. Indole derivatives are known to combat oxidative damage through several routes: directly scavenging reactive oxygen species, chelating pro-oxidant metal ions, boosting endogenous antioxidant enzymes, and dampening inflammation. Because the indole core tolerates chemical modification at many positions, chemists can fine-tune its lipophilicity, solubility, and metabolic stability with relative precision.

The second fragment, thiosemicarbazide, is a sulfur- and nitrogen-rich unit whose electron-donating heteroatoms readily surrender hydrogen atoms and electrons to neutralize free radicals. It can also coordinate metal ions through its sulfur and nitrogen atoms, blocking the Fenton-like reactions that generate highly destructive hydroxyl radicals. Thiosemicarbazide derivatives have separately shown anti-inflammatory, anticancer, and antiviral activity, and the moiety often improves how candidate drugs bind their biological targets and survive metabolism. Fusing these two fragments was therefore a deliberate bet that the whole molecule would exceed the sum of its parts.

Synthesis proceeded from commercially available 3-indolepropionic acid in three steps: esterification in methanol with sulfuric acid catalysis, conversion to a hydrazide using hydrazine monohydrate, and final condensation with a series of isothiocyanates under reflux. The ten products, labeled 4a through 4j, were obtained as brown crystalline solids in yields of 51 to 62 percent and were fully verified by proton and carbon-13 nuclear magnetic resonance spectroscopy together with high-resolution mass spectrometry. For the champion compound, designated 4i, the mass spectrometric peak for its sodium adduct matched the calculated value for the formula C18H17ClN4OS to within five parts per million, confirming the proposed structure unambiguously.

Radical-scavenging assays delivered the first headline result. In the ABTS test, every derivative inhibited the radical in a concentration-dependent fashion, and at six micrograms per milliliter six of the ten compounds achieved complete scavenging. The half-maximal inhibitory concentrations spanned 0.96 to 2.21 micrograms per milliliter, several of which undercut vitamin C’s benchmark of 1.87. Compound 4i, which carries a 4-chlorophenyl substituent on the thiosemicarbazide nitrogen, led the field at 0.96 micrograms per milliliter, followed closely by the allyl-substituted 4e at 1.00. In the more demanding DPPH assay, 4i again performed best among the series at 2.95 micrograms per milliliter, approaching vitamin C’s 1.99. The pattern suggests that the para-chloro aromatic group enhances the electron distribution across the molecule in a way that favors radical capture.

To understand what such a molecule might do inside living systems, the researchers turned to computational modeling against human peroxiredoxin 5, a thioredoxin-dependent peroxidase that defends cells against oxidative damage. Molecular docking placed 4i deep in the enzyme’s catalytic pocket, anchored by conventional hydrogen bonds to the residues Gly92 and Val94, while its two aromatic rings engaged in amide-pi stacking with Gln68 and Ala90 and additional alkyl and pi-alkyl contacts with Val70 and Arg86. A 100-nanosecond molecular dynamics simulation in GROMACS then tested whether this pose would hold up under realistic thermal motion. The protein backbone and the ligand equilibrated within roughly 20 nanoseconds and thereafter fluctuated around stable averages of about 0.25 and 0.30 nanometers respectively, the protein’s radius of gyration held near 1.44 nanometers, and the complex maintained one to five hydrogen bonds throughout the trajectory. Energy decomposition singled out Val70 and Val69 as the dominant contributors to binding, and the calculated binding free energy of approximately minus 45 kilojoules per mole remained favorable across the simulation.

The authors are careful to frame these simulations as predictions rather than proof. Molecular dynamics energy estimates do not constitute experimental validation of true binding affinity, and no enzyme-level assay has yet tested whether 4i actually modulates peroxiredoxin 5’s catalytic function. Computational ADMET predictions from the Deep-PK platform, alongside a SwissADME drug-likeness analysis, suggested favorable absorption, distribution, metabolism, excretion, and safety characteristics, and the compound satisfies all four criteria of Lipinski’s rule of five, a widely used screen for oral bioavailability potential. But the researchers themselves stress that such in silico results are preliminary and cannot substitute for pharmacological or toxicological experiments.

Where the compound produced genuinely tangible results was in meat. The team dissolved 4i in edible alcohol at nominal concentrations of 0.005, 0.01, and 0.015 percent by meat weight, mixed it into minced beef tenderloin from cross-bred yellow cattle, and shaped the meat into 50-gram patties stored at 4 degrees Celsius for seven days alongside a vitamin C-treated group and an untreated control. Discoloration in fresh beef is driven largely by oxidation of myoglobin, the heme protein responsible for meat’s red hue. When the oxygenated form, oxymyoglobin, is oxidized to brown metmyoglobin, shoppers perceive the meat as stale. By day seven, metmyoglobin accounted for 76.39 percent of the myoglobin in the untreated controls, whereas patties treated with 0.015 percent 4i accumulated only 49.76 percent, a statistically significant improvement that also beat vitamin C at the same concentration.

Instrumental color measurements reinforced the visual evidence. The redness parameter, a*, declined in every group over storage, but the decline was dose-dependently blunted by 4i, leaving the highest-treated patties with the brightest redness on day seven. pH, which rises during storage as microbes proliferate and proteins break down into alkaline products, climbed less steeply in the 4i groups than in either the control or the vitamin C group, an important secondary benefit because elevated pH correlates with poorer color stability. Photographs captured the difference plainly: untreated samples began browning visibly by day three and were severely discolored by day seven, while the 0.015 percent 4i patties retained the most appealing appearance of any treatment.

The dual promise of this single small molecule, as both an antioxidant lead for drug development and a candidate protective agent for fresh meat, is what makes the study notable, but the authors are equally clear about the distance to any practical application. No residue data, acceptable daily intake values, or toxicological results exist yet for 4i, so consumer exposure and dietary risk cannot be evaluated, and comprehensive food-safety assessment is mandatory before any real-world use. The meat findings may also reflect factors beyond direct radical scavenging, including pH shifts, microbial metabolism, and oxygen availability. What the study firmly establishes is that fusing indole with thiosemicarbazide is a viable blueprint for designing potent antioxidants, and that compound 4i, with its vitamin C-surpassing ABTS activity, stable predicted binding to a human antioxidant enzyme, and demonstrable color-protecting power in beef, has earned a place in the pipeline for further structural optimization, enzyme validation, and in vivo evaluation.

Subject of Research: Synthesis and antioxidant evaluation of novel indole-thiosemicarbazide derivatives for drug leads and beef preservation

Article Title: Novel indole derivatives containing a thiosemicarbazide moiety as potential antioxidant agents for fresh beef preservation

Article References: Wang, T., Xiao, L., Ren, Y., Wu, M., Liu, Y., Tang, J., Wan, S., & Peng, J. (2026). Novel indole derivatives containing a thiosemicarbazide moiety as potential antioxidant agents for fresh beef preservation. Frontiers in Chemistry, 14, Article 1994186. https://doi.org/10.3389/fchem.2026.1994186

Image Credits: AI Generated

DOI: 10.3389/fchem.2026.1994186

Keywords: antioxidants, indole derivatives, thiosemicarbazide, free radical scavenging, molecular docking, molecular dynamics, peroxiredoxin 5, ADMET, meat preservation, myoglobin oxidation, beef color stability, medicinal chemistry

The study begins with a deceptively simple transformation. Each of the fifteen drug molecules, ranging from the classic estrogen-blocker tamoxifen to the PARP inhibitor olaparib, was converted into a molecular graph, a network in which atoms become vertices and chemical bonds become edges. Once a drug is a graph, the full arsenal of chemical graph theory becomes available. The researchers focused on so-called resolving sets, a concept first introduced by Peter Slater in 1975 and independently explored by Frank Harary and Robert Melter. A resolving set is a small collection of vertices whose distances to every other vertex in the network are unique, allowing every atom to be unambiguously identified by its pattern of distances alone. The minimum size of such a set is called the metric dimension, and it acts as a fingerprint of structural complexity.

Finding these sets is computationally hard, so the team wrote a brute-force algorithm that exhaustively tests vertex combinations until it identifies all minimal resolving sets. The approach is efficient for the molecules considered here, which have fewer than sixty carbon atoms, but the authors acknowledge that it becomes prohibitively slow as molecular size grows. For each drug, the algorithm returned a metric dimension, with fulvestrant and anastrozole scoring five, tamoxifen, toremifene, megestrol acetate, alpelisib, and ribociclib scoring four, and smaller, less branched molecules such as elacestrant scoring just two.

From these resolving sets, the researchers derived a new family of descriptors they call resolving topological indices, computed via a two-variable polynomial that generalizes the well-known M-polynomial framework of Deutsch and Klavžar. Traditional degree-based indices, such as the Randić and Zagreb indices, summarize branching and connectivity but lose information about how vertices are positioned relative to one another. Resolving indices add a second layer, weighting each vertex by its contribution to uniquely identifying the rest of the molecule. In principle, this captures structural nuances that conventional descriptors overlook, which is precisely why the authors believe these indices could sharpen future Quantitative Structure–Property Relationship models, the statistical machinery that links molecular architecture to biological behavior.

The next step was to connect the mathematics to pharmacology. Using data drawn from the pkCSM database, the team assembled five ADMET properties for each drug: water solubility, Caco-2 permeability, skin permeability, oral acute toxicity, and oral chronic toxicity. These parameters describe whether a drug dissolves, crosses the gut wall, penetrates membranes, and remains safe in the short and long term. Quadratic regression models then tested how well the resolving indices predicted each property. The results were sobering but informative. Water solubility, acute toxicity, and chronic toxicity showed weak-to-moderate correlations, with chronic toxicity performing best: one index achieved a correlation coefficient of 0.670, and the strongest regression model for that property explained roughly 45 percent of the variance with statistical significance. Caco-2 permeability and skin permeability, by contrast, correlated poorly, with coefficients often below 0.55, a reminder that topology alone cannot capture every pharmacokinetic subtlety.

The real innovation came when the researchers stopped trying to predict individual properties and instead asked which drug performs best across all of them simultaneously. This is the domain of multi-criteria decision-making, or MCDM, a family of operations-research techniques usually applied to engineering and energy planning. To avoid subjective judgment, the team used the entropy method, which assigns weights to each criterion based purely on how much the data vary across drugs; criteria with greater variability carry more information and receive larger weights. The resulting weights hovered around 0.15 to 0.21 for each of the five properties, with water solubility, skin permeability, and both toxicity measures weighted slightly above Caco-2 permeability.

Two ranking engines were then set to work. The first, TOPSIS, the Technique for Order Preference by Similarity to Ideal Solution, positions each drug in a normalized multidimensional space and measures its Euclidean distance to an idealized best-case drug and a worst-case drug; the winner is the molecule geometrically closest to ideal. The second, Simple Additive Weighting, or SAW, is even more direct: each drug’s normalized, weighted scores are simply summed, and the largest total wins. Because the two methods rest on different mathematical logic, agreement between them is a useful robustness check, and a comparison plot showed strong consistency across the fifteen drugs.

The verdicts were clear. Fulvestrant, the estrogen-receptor degrader, earned the number-one rank under both methods, followed by neratinib, an irreversible HER-family kinase inhibitor, and ribociclib, a CDK4/6 inhibitor. Toremifene, megestrol acetate, anastrozole, and alpelisib occupied the middle of the table, while letrozole and exemestane, two aromatase inhibitors, fell to the bottom. Notably, the ranking partially echoes clinical reality: ribociclib combined with fulvestrant has been shown in the MONALEESA-3 trial program to improve overall survival in advanced hormone-receptor-positive, HER2-negative breast cancer, a convergence the authors cite as evidence that the method has heuristic value.

Yet the researchers are emphatic that the league table is a mathematical artifact, not a clinical prescription. The ranking reflects only computed topological indices and predicted ADMET values, with no experimental or clinical data feeding the model. The entropy weighting scheme is blind to biology: it cannot know that toxicity matters more than skin permeability in real-world oncology, regardless of statistical variability. The study also covers only fifteen approved drugs and a handful of ADMET endpoints, and the brute-force resolving-set algorithm limits scalability to larger molecular systems. The authors recommend that future iterations incorporate expert judgment and biologically informed weighting, and suggest extending the framework to larger datasets, entropy-based topological indices, and additional physicochemical properties.

Even with those caveats, the study sketches a provocative vision of drug evaluation in which molecules are judged as networks, properties are forecast by polynomials, and treatment candidates are sorted by distance from a mathematical ideal. As computational screening becomes ever more central to drug discovery, frameworks like this one, blending chemical graph theory, regression modeling, and decision science, may help researchers triage vast candidate libraries before a single experiment is run. For breast cancer, a disease whose therapies already span hormones, kinases, microtubules, and DNA repair, having a common mathematical language to compare such disparate drugs is a genuinely novel contribution, even if the final arbiter remains the clinic.

Subject of Research: Computational ranking of breast cancer therapeutics using resolving topological indices, ADMET analysis, and multi-criteria decision-making methods

Article Title: Integration of resolving topological indices and ADMET analysis with MCDM methods (TOPSIS and SAW) for ranking breast cancer therapeutics

Article References: Pandeeswari, E., & Ravi Sankar, J. (2026). Integration of resolving topological indices and ADMET analysis with MCDM methods (TOPSIS and SAW) for ranking breast cancer therapeutics. Frontiers in Bioinformatics, 6, Article 1913227. https://doi.org/10.3389/fbinf.2026.1913227

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1913227

Keywords: breast cancer, topological indices, chemical graph theory, ADMET, QSPR, TOPSIS, SAW, multi-criteria decision-making, fulvestrant, neratinib, ribociclib, metric dimension

The research team, led by Renuka Ekka and supervised by Bharti Ahirwar, began by collecting fresh leaves from Kunkuri in the Jashpur district and having the plant formally identified by botanists at the university’s Department of Botany, where a voucher specimen was deposited for future reference. Before any biological testing could begin, the leaves underwent a full pharmacognostic workup. Thin sections of leaf and petiole were stained with safranin, mounted in glycerin and examined under the microscope, revealing a dorsiventral lamina with a single-layered upper epidermis, two to three layers of tightly packed palisade tissue and a spongy mesophyll riddled with air spaces. The petiole showed a thick collar of collenchyma, seven to nine cell layers deep, wrapped around V- or U-shaped vascular bundles sheathed in sclerenchyma, with calcium oxalate crystals scattered through the cortex.

These anatomical details matter more than they might first appear. Microscopic fingerprints, together with physicochemical parameters such as ash values, moisture content, swelling index, foaming index and extractive values, constitute the quality-control toolkit of herbal medicine. Because a plant can degrade in storage or be adulterated with inferior material, establishing a reproducible anatomical and physical profile is the first step toward turning a folk remedy into a standardized medicine. The team’s measurements of total ash, acid-insoluble ash and water-soluble ash now provide a benchmark against which future batches of kachnar leaf material can be authenticated and graded.

With the plant material authenticated, the researchers defatted the powdered leaves with petroleum ether and then ran an exhaustive ethanol extraction in a Soxhlet apparatus at 40 to 60 degrees Celsius for eight to ten hours. The dark green extract represented a yield of roughly 6.46 percent of the starting plant material. Preliminary screening confirmed the presence of alkaloids, flavonoids, saponins, tannins, proteins and carbohydrates, while glycosides and steroids were absent. Quantification proved more striking: the extract contained 446.85 milligrams of flavonoids per gram, expressed as quercetin equivalents, and 137.04 milligrams of phenolics per gram, expressed as gallic acid equivalents. Flavonoids and phenolic compounds are the chemical workhorses of plant antioxidant defense, and their abundance here hinted at potent biological activity.

Two complementary assays put that hint to the test. In the ABTS radical decolorization assay, which measures a compound’s ability to quench a stable blue-green radical cation, the extract achieved half-maximal scavenging at a concentration of 218.76 micrograms per milliliter, compared with 203.52 micrograms per milliliter for the ascorbic acid standard. The FRAP assay, which tracks the reduction of ferric iron to its ferrous form at low pH, gave a nearly identical half-maximal value of 217.59 micrograms per milliliter against 185.26 for the standard. The extract therefore fell only modestly short of pure vitamin C, a remarkable result for a crude mixture, and one that the authors attribute to the hydroxyl groups on its flavonoid payload, which donate electrons and hydrogen atoms to neutralize destructive free radicals before they can peroxidize lipids or damage DNA.

The antimicrobial results were more measured but still encouraging. Using the agar well diffusion method, the extract produced inhibition zones of 5 to 30 millimeters against Escherichia coli, and follow-up testing against Pseudomonas aeruginosa showed clear concentration-dependent activity: zones grew from 2.5 millimeters at 100 milligrams per milliliter to 8.5 millimeters at 400 milligrams per milliliter for E. coli, and from 1.0 to 5.5 millimeters for P. aeruginosa. Estimated minimum inhibitory concentrations came out at 100 milligrams per milliliter for E. coli and 50 for P. aeruginosa. The standard antibiotic tetracycline outperformed the extract, as expected, but the dose-responsive pattern supports the plant’s traditional use against infection, and the authors note that broader testing against Gram-positive organisms is needed to map the full antibacterial spectrum.

Anti-inflammatory activity was assessed with the bovine serum albumin denaturation assay, a widely used surrogate in which protection of protein structure stands in for suppression of inflammatory tissue damage. At 100 micrograms per milliliter the extract inhibited denaturation by 16.45 percent, rising to 23.28 percent at 200 and 34.21 percent at 400 micrograms per milliliter. Diclofenac sodium, the reference drug, achieved 24.6, 33.86 and 47.67 percent at the same doses. The extract trailed the drug at every concentration, yet the steep, dose-dependent curve confirmed appreciable in vitro anti-inflammatory potential, providing the experimental half of the validation the study set out to achieve.

The computational half proved even more provocative. The team docked five major kachnar flavonoids, rutin, quercetin, apigenin, kaempferol and catechin, into the catalytic pocket of cyclooxygenase-2, the enzyme whose overactivity drives prostaglandin-mediated inflammation and which diclofenac targets clinically. Every flavonoid outscored the drug. Rutin led with a binding affinity of minus 9.6 kilocalories per mole, followed by quercetin at minus 9.2, apigenin at minus 9.1 and kaempferol at minus 9.0, all well ahead of diclofenac’s minus 6.7. Rutin’s forest of hydroxyl groups forged multiple conventional hydrogen bonds with residues including Gln192, Gly354, His351, Tyr355 and Phe580, while quercetin and kaempferol anchored themselves through hydrogen bonding to Tyr130, Gly45 and Cys47 supplemented by pi-alkyl contacts with hydrophobic residues. Surface mapping of hydrogen bond donors and acceptors showed that the plant compounds complemented the binding pocket more thoroughly than diclofenac did.

Binding energy alone does not make a drug, so the researchers also ran the flavonoids through ADMET and drug-likeness prediction using SwissADME and pkCSM, screening against Lipinski’s Rule of Five. Apigenin, catechin, quercetin and kaempferol all passed, with molecular weights below 500 daltons and balanced lipophilicity between 0.53 and 3.22, and all four showed intestinal absorption above 70 percent, comparable to diclofenac. Apigenin emerged with the most favorable profile, combining moderate polarity with the highest predicted blood-brain barrier score among the flavonoids. Rutin, despite its stellar docking score, stumbled pharmacokinetically: its 610-dalton mass, high polar surface area and negative logP predicted poor absorption and negligible brain penetration, leading the authors to suggest that nano-delivery systems might rescue its bioavailability. Encouragingly, none of the compounds were predicted to inhibit the CYP3A4 or CYP2D6 metabolic enzymes, and all were flagged AMES-negative, indicating low risks of drug-drug interactions and mutagenicity.

Taken together, the study offers something rarer than another plant extract with activity in a dish: a mechanistic bridge between ethnobotanical tradition and molecular pharmacology. The same flavonoids that dominate the leaf chemistry also dominate the docking rankings, and the in vitro antioxidant, antimicrobial and anti-inflammatory results align with the computational predictions rather than sitting in isolation. The authors are careful to frame the work as a starting point rather than a finish line, calling for wider antimicrobial panels, preclinical studies and formulation development before any of these compounds reach the clinic. But the message for drug discovery is clear: when tribal healers in central India spent centuries refining their use of kachnar, they were running an experiment whose results modern science is only now learning to read, and the molecules hidden in its leaves may yet yield therapeutic leads that no laboratory would have thought to synthesize from scratch.

Subject of Research: Scientific validation of the traditional anti-inflammatory, antimicrobial and antioxidant uses of Bauhinia variegata through phytochemical analysis, in vitro assays and molecular docking

Article Title: Scientific validation of tribal medicinal claims of Bauhinia variegata (L.) through integrated In-Silico and experimental approaches

Article References: Ekka, R., & Ahirwar, B. (2026). Scientific validation of tribal medicinal claims of Bauhinia variegata (L.) through integrated In-Silico and experimental approaches. Discover Chemistry, 3(1), Article 455. https://doi.org/10.1007/s44371-026-00908-x

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00908-x

Keywords: Bauhinia variegata, ethnomedicine, flavonoids, molecular docking, COX-2, ADMET, antioxidant, antimicrobial, anti-inflammatory, phytochemistry, drug discovery, tribal medicine

A new computational study published in Discover Chemistry by Zara Javaid, Hafiza Salaha Mahrosh, Saad Serfraz and Amer Jamil of the University of Agriculture, Faisalabad, set out to close this gap by screening plant-derived molecules against two of the virus’s most strategically important proteins. The team assembled a library of 1,000 phytochemicals with documented antiviral, antioxidant or anticancer activity, drawn from the IMPPAT database, Dr. Duke’s Phytochemical and Ethnobotanical Database and published literature, and docked them against glycoprotein 42, the envelope protein that mediates viral entry into B cells, and Epstein–Barr nuclear antigen 1, the latency protein that maintains and partitions the viral episome during cell division.

The choice of targets reflects a deliberate dual strategy. Glycoprotein 42 is a surface-exposed glycoprotein that binds HLA class II molecules and works alongside the gH and gL glycoproteins to drive membrane fusion, making it an accessible point of attack during the earliest stages of infection. Peptide inhibitors directed at this protein have already shown nanomolar antiviral activity in prior work, validating its therapeutic potential. EBNA1, by contrast, is a 641-amino-acid nuclear protein expressed in every EBV-linked cancer and is essential for viral genome replication and persistence; no FDA-approved inhibitor of it exists. By targeting both an entry factor and a latency factor simultaneously, the researchers aimed to identify compounds with combined antiviral and antineoplastic potential, an approach that distinguishes their workflow from earlier studies focused on single targets or synthetic libraries.

The docking screen, performed with the AutoDock Vina engine inside PyRx, produced clear front-runners. Against glycoprotein 42, the steroidal saponin hecogenin achieved the strongest binding energy of −8.9 kcal/mol, forming a hydrogen bond with glutamine 92 and hydrophobic contacts with tyrosine 167, phenylalanine 129 and histidine 191. Other strong performers included baicalin, islandicin, swertisin, hortensin, pedalitin, epigallocatechin, pinoresinol, hesperetin and verbenalin. Against EBNA1, the plant sterol cyclobranol led with a score of −8.2 kcal/mol, anchoring itself in a hydrophobic pocket through contacts with phenylalanine 508, tyrosine 510 and leucine 539. Notably, acyclovir, used as a comparator control, scored only −4.7 kcal/mol against glycoprotein 42 and −4.9 kcal/mol against EBNA1, placing it last among the screened ligands and suggesting that the top phytochemicals bind both viral proteins more favorably than the standard clinical antiviral.

The authors took care to validate their docking protocol. The native co-crystallized ligand of EBNA1 was extracted and redocked under identical parameters, reproducing the experimental binding geometry with a heavy-atom root-mean-square deviation of 0.958 angstroms, well within the accepted threshold of 2 angstroms. Redocking validation was not possible for glycoprotein 42, which was solved in its unbound form without a co-crystallized ligand, a structural limitation the team acknowledged openly. Docking scores, they stressed, are estimates of binding compatibility rather than predictions of biological potency, and experimental confirmation remains essential.

From the top-ranked compounds, the researchers applied Lipinski’s rule of five as an initial filter for oral drug-likeness. Hesperetin, hecogenin, islandicin, hortensin, pedalitin, pinoresinol and verbenalin passed with no violations, while brassicasterol, swertisin, caftaric acid and epigallocatechin each violated a single criterion. Compounds with more than two violations, including cyclobranol, stigmasterol, baicalin and taraxerol, were excluded. The authors noted that natural products often carry greater structural complexity than synthetic drugs, so broader physicochemical descriptors were reserved for the later pharmacokinetic assessment rather than used as absolute exclusion criteria at this early stage.

The study then moved into network pharmacology, linking the surviving compounds to their predicted human gene targets through SwissTargetPrediction and the STITCH database, and cross-referencing these against EBV- and EBV-cancer-associated genes retrieved from GeneCards and DisGeNET. The intersection yielded 132 shared targets, which were mapped into a protein–protein interaction network using STRING and visualized in Cytoscape. The network contained 132 nodes and 827 edges, and degree-based analysis with the CytoHubba plugin identified ten hub genes: TP53, HSP90AA1, AKT1, SRC, BCL2, EGFR, TNF, ESR1, ERBB2 and PIK3CA. Gene Ontology and KEGG enrichment showed these genes clustered in apoptosis regulation, kinase signaling, the PI3K-Akt pathway and viral oncogenesis pathways, including EBV infection itself and Kaposi’s sarcoma-associated herpesvirus infection, consistent with the virus’s known strategy of suppressing host cell death and hijacking kinase cascades to maintain latency and drive proliferation.

When the eight drug-like phytochemicals were docked against the EBV-associated cancer proteins, hecogenin again dominated, achieving the best score against nine of the ten targets. Its strongest interaction was with the TP53 DNA-binding domain at −10.9 kcal/mol, stabilized by hydrogen bonds with glycine 112, phenylalanine 113 and serine 116, followed by −10.6 kcal/mol against PIK3CA and −10.4 kcal/mol against SRC. Pinoresinol, swertisin and hesperetin also performed well. To test whether the hecogenin–TP53 interaction could survive realistic physical conditions, the team ran a 200-nanosecond molecular dynamics simulation in GROMACS using the CHARMM36 force field on an NVIDIA H100 GPU. The protein backbone remained stable throughout, with RMSD values of 2.0 to 2.8 angstroms and a nearly constant radius of gyration, while the ligand’s internal geometry barely changed, with a ligand RMSF of only about 0.35 angstroms. The ligand did, however, wander considerably relative to its docked pose, and hydrogen bond analysis revealed mostly transient polar contacts, indicating that the complex is held together primarily by hydrophobic and van der Waals forces in a dynamic rather than rigid binding mode.

Predicted pharmacokinetics added further support. ADMET profiling with SwissADME and pkCSM indicated no AMES toxicity, hepatotoxicity or skin sensitization for the lead compounds, and both hecogenin and pinoresinol were predicted to cross the blood–brain barrier and show high gastrointestinal absorption, placing them in the favorable yolk region of the Boiled Egg plot. As P-glycoprotein substrates, both would be actively pumped out of the central nervous system, limiting CNS exposure but potentially improving peripheral safety. The authors were candid about the caveats: hecogenin’s broad affinity across structurally unrelated proteins raises the possibility of promiscuous, hydrophobicity-driven binding rather than genuine multitarget specificity, and although the compound triggered no PAINS alerts, orthogonal assays such as surface plasmon resonance and thermal shift experiments will be needed to separate true polypharmacology from assay interference.

Hecogenin itself is a steroidal saponin from Agave sisalana with a prior record of anti-inflammatory, antimicrobial and antitumor research, while pinoresinol is a lignan from sesame seeds and olive oil, and hesperetin and swertisin are flavonoids with documented anticancer and immunomodulatory properties. The study’s real contribution may lie as much in its framework as in its lead compound: an integrated pipeline that couples large-scale docking, network pharmacology, enrichment analysis, ADMET prediction and molecular dynamics into a reproducible workflow for discovering mechanism-based antiviral agents from natural sources. The researchers emphasize that all findings are computational and hypothesis-generating, and that in vitro and in vivo validation is required before hecogenin or its counterparts can be considered genuine therapeutic candidates against EBV infection and the cancers it leaves in its wake.

Subject of Research: Computational screening of phytochemicals as multitarget inhibitors of Epstein–Barr virus infection and EBV-associated malignancies

Article Title: Network pharmacology and molecular docking-based screening of phytochemicals as multitarget inhibitors of Epstein–Barr virus infection and associated malignancies

Article References: Javaid, Z., Mahrosh, H. S., Serfraz, S., & Jamil, A. (2026). Network pharmacology and molecular docking-based screening of phytochemicals as multitarget inhibitors of Epstein–Barr virus infection and associated malignancies. Discover Chemistry, 3(1), Article 577. https://doi.org/10.1007/s44371-026-01029-1

Image Credits: AI Generated

DOI: 10.1007/s44371-026-01029-1

Keywords: Epstein–Barr virus, network pharmacology, molecular docking, hecogenin, phytochemicals, EBNA1, glycoprotein 42, molecular dynamics simulation, ADMET, oncogenic viruses, drug discovery, TP53

The cornerstone of current onchocerciasis control is mass drug administration with ivermectin, a regimen recommended for ten to fifteen years. The drug is microfilaricidal, meaning it kills the larval stage of the parasite, but it does not eliminate the adult worms that continue to reproduce. Long-term annual treatment alone has proven insufficient to interrupt transmission in several foci, including parts of Cameroon where transmission has continued despite more than fifteen years of community-directed treatment with ivermectin. The 2025 verification of Niger as the first African country to eliminate onchocerciasis demonstrates that elimination is achievable, but it also underscores the urgent need for safe, macrofilaricidal alternatives that can shorten elimination timelines and counter emerging resistance or sub-optimal drug response in the remaining endemic areas.

Enter a team of researchers led by Maraf Mbah Bake of the University of Yaoundé I, working with collaborators in Cameroon, Germany, South Africa, Iraq and Spain. Writing in the journal Discover Chemistry, the team describes a systematic computational hunt for inhibitors of two detoxification enzymes from O. volvulus, using the ConMedNP library, a curated collection of roughly 3,500 secondary metabolites isolated from Central African medicinal plants. Their strategy combined ADMET-based filtering, tiered molecular docking, MMGBSA binding-energy rescoring, pharmacological activity prediction and 200-nanosecond molecular dynamics simulations, a pipeline designed to separate genuine binders from computational noise.

The molecular targets were the sigma-class and pi-class glutathione S-transferases of the parasite, known as Ov-GST1 and Ov-GST2. These enzymes conjugate glutathione to hydrophobic electrophiles, shielding the worm from oxidative damage and facilitating the excretion of xenobiotics, including drugs. Three GST isoforms have been characterized in O. volvulus, but the sigma and pi classes are particularly attractive because they underpin the parasite’s survival under oxidative stress. Crucially, the two isoforms differ structurally: Ov-GST1 possesses a larger, more solvent-exposed and flexible substrate-binding pocket, whereas Ov-GST2 has a smaller, more rigid cavity with greater substrate selectivity. These differences offered the researchers a structural basis for ligand selectivity that their screening pipeline was designed to exploit.

The workflow began with the crystal structures of Ov-GST2, co-crystallized with the inhibitor S-hexylglutathione, and Ov-GST1, co-crystallized with glutathione, retrieved from the Protein Data Bank. Each structure was carefully prepared, with protonation states assigned at physiological pH and restrained energy minimization performed using the OPLS3 force field. All 3,500 ConMedNP compounds were then profiled with QikProp against descriptors calibrated to the properties of 95 percent of approved drugs, including solubility, permeability, polar surface area and oral absorption. Docking proceeded through three sequential tiers of increasing stringency, high-throughput virtual screening, standard precision and extra precision, ultimately shortlisting ten hits against Ov-GST2 and eight against Ov-GST1.

A critical validation step lent credibility to the entire exercise. The researchers redocked the co-crystallized native ligands into their respective receptors under identical extra-precision settings, obtaining heavy-atom RMSD values of 1.023 angstroms for S-hexylglutathione and 0.954 angstroms for glutathione, both comfortably below the 2.0-angstrom threshold conventionally accepted as evidence of an accurate docking protocol. The native ligands also reproduced the expected hydrogen bonds with key residues such as Ser63, Gln49, Gln62 and Arg95 in Ov-GST2, and Glu87, Ser88, Trp63 and Tyr32 in Ov-GST1, matching previously reported experimental findings. With the protocol thus anchored, the team applied MMGBSA rescoring, an implicit-solvent estimate of binding free energy more discriminating than the empirical docking score, to rank the shortlisted hits.

Four compounds with the most favorable combined docking, MMGBSA and ADMET profiles advanced to the molecular dynamics stage: AN_UY_186_1 and AN_UY_088 against Ov-GST2, and BMC_00091 and SO_UY_018 against Ov-GST1. The two Ov-GST2 hits are homoisoflavanones, a subclass of flavonoids whose 4H-chromen-4-one core packs against the hydrophobic floor of the enzyme’s H-site while their 7-hydroxyl and methoxy substituents donate hydrogen bonds to the backbone of Leu50, and their C3 benzyl group engages Phe38 through pi-pi stacking. Strikingly, this pharmacophore mirrors the binding mode reported for antibacterial homoisoflavanones isolated from Hyacinthaceae species in 2007, suggesting a recurring chemical logic rather than a computational accident.

The 200-nanosecond simulations, run in Desmond with the OPLS4 force field in physiological salt conditions, revealed clear winners. AN_UY_088 held its position within the Ov-GST2 active site with protein backbone RMSD oscillating between 1.4 and 2.4 angstroms and ligand RMSD between 3 and 5 angstroms, forming persistent hydrogen bonds with Tyr7 and pi-pi stacking with Phe38. On the sigma-class side, SO_UY_018 proved the most stable complex, maintaining hydrophobic contacts with Phe123 and Ile127 and a positively charged interaction with Arg38 across roughly a third of the simulation. A Bayesian activity-spectrum analysis using PASS further corroborated the scaffold-level plausibility of the hits, predicting antibacterial, antiparasitic, anthelmintic and anti-inflammatory probabilities consistent with known pharmacophores.

Perhaps the most intriguing finding is the convergence with the team’s own prior work. In a 2024 study, the same group screened the South African Natural Compounds Database, a chemically and geographically distinct library of about 600 compounds, against Ov-GST2 and again landed on homoisoflavanones. That the same scaffold class is independently selected against the same target from two different African natural-product collections suggests the homoisoflavanone signature is reproducible across libraries rather than an artifact of any one collection’s composition. Meanwhile, the best Ov-GST1 lead, SO_UY_018, is not a homoisoflavanone but a larger, more extended scaffold that exploits the sigma-class enzyme’s distinctive Phe33, Phe123 and Ile127 hydrophobic patch, indicating that scaffold selection is target-dependent rather than library-dependent.

The authors are careful to frame these results as computational predictions, not proven cures. They acknowledge that MMGBSA values overestimate absolute affinities, that virtual screening is prone to false positives, and that even 200 nanoseconds of dynamics may undersample slow protein motions and rare unbinding events. Notably, the natural-product hits bind non-glutathionyl pharmacophores, meaning they are unlikely to be competitively displaced by the high intracellular concentration of glutathione, an advantage over substrate-mimetic peptide inhibitors, and their drug-like ADMET profiles make them plausible oral candidates, unlike the peptide native ligands. What happens next is the decisive test: recombinant enzyme inhibition assays, anthelmintic assays on O. volvulus microfilariae and adult worms, and in vitro ADMET studies will determine whether these digital leads, born from the flora of Central Africa, can graduate into the macrofilaricidal medicines that river blindness elimination efforts so urgently need.

Subject of Research: Computational identification of natural-product inhibitors of Onchocerca volvulus glutathione S-transferases

Article Title: Discovering potent Onchocerca volvulus inhibitors from the ConMedNP library through virtual screening, MMGBSA, and molecular dynamics simulations

Article References: Mbah Bake, M., Tsahnang Fofack, H. M., Petry, S., Tsague Meupe Bahoken, B., Bebga, G., Singh, T., Mbouombouo Ndassa, I., Mohammad-Salim, H., & de Julián-Ortiz, J. V. (2026). Discovering potent Onchocerca volvulus inhibitors from the ConMedNP library through virtual screening, MMGBSA, and molecular dynamics simulations. Discover Chemistry, 3(1), Article 465. https://doi.org/10.1007/s44371-026-00910-3

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00910-3

Keywords: Onchocerca volvulus, river blindness, ConMedNP library, virtual screening, MMGBSA, molecular dynamics, glutathione S-transferases, homoisoflavanones, natural products, drug discovery, neglected tropical diseases, ADMET