The research team focused on 3-(pyridine-3-yl)-2-oxazolidinone derivatives, a family of compounds that earlier experimental work by Bo Jin and colleagues had shown to possess antibacterial activity comparable to linezolid, along with better solubility and reduced cytotoxicity. Rather than simply recording those biological observations, the new study converts them into predictive mathematical models that can forecast how structural changes will affect potency against four Gram-positive strains: Staphylococcus aureus (ATCC 25923), Streptococcus pneumoniae (ATCC 49619), Enterococcus faecalis (ATCC 29212), and Staphylococcus xylosus (ATCC 35924). Experimental minimum inhibitory concentration values were converted to pMIC values and used as the dependent variable in a series of regression models built with the genetic algorithm-multiple linear regression (GA-MLR) method, a technique that uses evolutionary search strategies to select the most informative molecular descriptors from a much larger pool.

The technical pipeline behind the models is rigorous. All thirty-one compounds, including linezolid itself, were drawn in 2D and then conformationally analysed with ORCA 6.0 before geometric optimization at the B3LYP/6-31G* level of density functional theory in Gaussian 09, a choice the authors justify because B3LYP delivers reliable equilibrium geometries for semi-rigid organic frameworks without overestimating intramolecular non-covalent interactions. From these optimized structures, the PaDEL-Descriptor software generated a staggering 1,875 molecular descriptors, numerical encodings of everything from branching patterns to electronic charge distribution. After removing intercorrelated descriptors with a cut-off of 0.99, the data were split into training and test sets using two complementary algorithms: the Kennard-Stone method, which deliberately selects the most chemically diverse compounds, and Euclidean distance-based sampling, which distributes compounds according to their average distance in descriptor space. Split ratios of 70:30 and 75:25 were tested for each strain, and the winning combination differed from organism to organism, an instructive reminder that the shape of a bioactivity landscape depends on the target.

The resulting four models all passed stringent statistical scrutiny. Coefficients of determination ranged from 0.7819 for the S. pneumoniae model to 0.9230 for S. xylosus, while leave-one-out cross-validated Q² values spanned 0.6847 to 0.8704, comfortably above the accepted minimum of 0.5. Adjusted R² values tracked closely with the raw R² values, signalling an absence of overfitting, and Y-randomization tests confirmed that the models were not capturing statistical noise. External predictivity was equally strong: the S. xylosus model achieved Q²(F1) and Q²(F2) values of 0.9682 and 0.9574 respectively, with a concordance correlation coefficient of 0.9777 and the lowest test-set mean absolute error of the entire set. Applicability domains were mapped with Williams plots using leverage thresholds, and nearly every compound fell safely within the reliable prediction region, with the few boundary cases retaining low leverage and therefore not undermining model robustness.

Beyond raw statistics, the models tell a coherent chemical story. In the S. aureus model, the Geary autocorrelation descriptor GATS5i, which captures the spatial distribution of atomic ionization potentials over five-bond distances, emerged as the strongest positive contributor to activity, while the count of fluorine atoms (nF) added a smaller but meaningful boost, consistent with fluorine’s ability to tune local electronic and lipophilic properties. Conversely, molecular branching (SC-5) and the WHIM electrotopological descriptor E3s acted as structural penalties, reducing potency. The E. faecalis model highlighted the minimum electrotopological state of oxygen atoms (mindO), pointing to the importance of accessible, charge-rich carbonyl oxygens for hydrogen bonding, while penalizing long-range charge autocorrelation. The S. xylosus model elevated maxHBa, the maximum hydrogen bond acceptor strength, reinforcing the same theme: polar oxygen atoms capable of donating hydrogen-bond acceptance are essential, whereas bulky, over-elongated, or excessively branched three-dimensional architectures are detrimental.

These descriptor-level insights dovetail neatly with the established structure-activity relationships of the oxazolidinone class, in which inhibition depends on specific electronic features, hydrogen-bonding capacity, and minimal spatial demands. The authors distill the message into a design rule for future analogues: combine available hydrogen bond acceptors and planar heterocyclic spacers while avoiding large bulky 3D structures and distant charge accumulations. Compounds bearing fluorine substitutions fared well in both the QSAR statistics and, as the docking results would later confirm, at the ribosomal binding site itself. The models do carry limitations, which the authors acknowledge candidly: predictions are only trustworthy within the applicability domain of the training chemicals, and the 3D descriptors were computed from a single energy-minimized conformation, ignoring solution-phase dynamics and the induced-fit changes that occur when a ligand actually nestles into its biological target.

To connect the statistical models to physical reality, the team docked the ten most potent derivatives into the 50S ribosomal subunit of Haloarcula marismortui, the archaeal crystal structure (PDB code 3CPW) whose peptidyl transferase center closely mirrors that of pathogenic bacteria and which has served as the canonical template for oxazolidinone binding since the linezolid co-crystal was solved. The docking protocol was validated by re-docking the native ligand, yielding an RMSD of 1.6 angstroms, well within the accepted threshold of 2 angstroms. The results were striking: every one of the ten derivatives bound more tightly than linezolid itself, with binding energies ranging from −9.89 ± 0.06 to −11.21 ± 0.04 kcal/mol against the reference drug’s −9.84 ± 0.01 kcal/mol.

Ligand 26 claimed the top spot with a binding affinity of −11.21 ± 0.04 kcal/mol, anchoring itself in the peptidyl transferase center through hydrogen bonds between its oxazolidinone group and the residues G2540 and C2487, carbon-hydrogen contacts with G2102, U2539, A2486, and U2620, pi-alkyl interactions between its methyl group and A2538, and halogen bonding of its fluorine atom with A2486 and C2487. Ligand 24 followed closely at −11.10 ± 0.13 kcal/mol with a nearly identical interaction fingerprint, while ligands 29, 15, 16, 19, 30, and 28 all posted scores between roughly −10.3 and −10.9 kcal/mol. Notably, ligand 26 had registered a high leverage value in one of the QSAR models, marking it as structurally peripheral, yet its excellent docking score suggests a novel scaffold genuinely worth pursuing rather than a statistical artifact. The consistency between the two approaches was one of the study’s most satisfying outcomes: compounds predicted by QSAR to be highly potent also docked favorably, and the fluorinated derivatives that scored well statistically also formed favorable halogen-mediated contacts at the ribosome.

The authors are careful to frame their findings as a starting point rather than a finish line. Static docking, however well validated, cannot capture induced-fit conformational changes, entropic contributions, or solvent-mediated interactions, so the lead candidates will next be subjected to molecular dynamics simulations and binding free energy calculations using MM/PBSA or MM/GBSA methods. There is also a species caveat: although the peptidyl transferase center is highly conserved across archaea, bacteria, and eukaryotes, subtle sequence differences in the ribosomal proteins and rRNA nucleotides of H. marismortui may influence binding affinities in true pathogens such as S. aureus, so future optimization efforts will turn to cryo-EM structures of eubacterial 50S subunits. What the current work delivers, however, is a validated, interpretable computational filter that converts published in vitro screening data into a prioritized shortlist. Because the entire discovery pipeline rests on computational models built from experimental data, the flagged compounds are best regarded as priority candidates for synthesis and subsequent in vitro and in vivo testing, but in a field where every new oxazolidinone represents years of medicinal chemistry, a reliable mathematical shortcut to the most promising structures is a genuinely valuable advance.

Subject of Research: QSAR modelling and molecular docking of pyridine-substituted oxazolidinone derivatives as potential antibacterial agents against Gram-positive pathogens

Article Title: QSAR modelling and molecular docking studies of 3-(Pyridine-3-yl)-2-oxazolidinone derivatives as potential antibacterial agents against gram-positive pathogens

Article References: Kaur, R. P., & Manjh, S. (2026). QSAR modelling and molecular docking studies of 3-(Pyridine-3-yl)-2-oxazolidinone derivatives as potential antibacterial agents against gram-positive pathogens. Discover Chemistry, 3(1), Article 546. https://doi.org/10.1007/s44371-026-00987-w

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00987-w

Keywords: QSAR, molecular docking, oxazolidinone, linezolid, antibiotic resistance, MRSA, Gram-positive bacteria, GA-MLR, 50S ribosomal subunit, medicinal chemistry, drug discovery, antimicrobials

The study, led by Dmytro Butov of Kharkiv National Medical University together with Stefan Niemann and Viola Dreyer of the Research Center Borstel in Germany, enrolled 4,828 patients and successfully sequenced 4,162 isolates after quality-control exclusions. Participants were divided into two groups: a drug-resistant tuberculosis cohort of 3,112 patients with rifampicin-resistant, multidrug-resistant, pre-extensively drug-resistant, or extensively drug-resistant disease, and a comparator cohort of 1,050 patients with non-MDR or drug-susceptible tuberculosis. Within the drug-resistant group, 62.3 percent of isolates were classified as multidrug-resistant, nearly a third as pre-XDR, and 2.5 percent as extensively drug-resistant.

The population structure of the circulating bacteria tells a striking story. Fully 70.6 percent of all sequenced isolates belonged to Lineage 2, the so-called Beijing lineage, which predominates across Eastern Europe and is associated with enhanced transmissibility and a greater propensity to acquire resistance mutations. Among drug-resistant cases, Lineage 2 accounted for 83.7 percent of isolates, rising to 89.7 percent among extensively drug-resistant infections. By contrast, Lineage 4 Euro-American strains, which are more genetically diverse, dominated the comparator cohort, particularly among patients with drug-susceptible disease in western Ukraine.

Even more striking is how little diversity exists within the drug-resistant Lineage 2 population. More than 82 percent of MDR, pre-XDR, and XDR isolates belonged to just four previously described Beijing clades: the Europe/Russia W148 outbreak clone, which alone made up nearly 40 percent of the resistant cohort, the Ukraine outbreak clone, the Central Asia outbreak clone, and the broader Central Asia clade. Geospatial analysis showed Lineage 2 dominance in almost every participating region, with the highest proportions in the central, southern, northern, and eastern parts of the country, while Lineage 4 remained comparatively more common in the west.

Genomic clustering analysis, which identifies isolates separated by five or fewer allelic differences at 2,891 core-genome loci, revealed that nearly half of all isolates belonged to clusters, a signature of recent or ongoing transmission. Clustering was far more frequent in the drug-resistant cohort, affecting 55 percent of isolates, than in the comparator group at 28.9 percent. The three largest clusters, all exclusively Lineage 2, contained 671 drug-resistant isolates, over a fifth of that cohort. Cluster 1, assigned to the Ukraine outbreak clone, comprised 307 isolates, more than half of them pre-XDR or XDR. Cluster 2, the W148 clone, included 296 isolates, mostly MDR, and Cluster 3 contained 76 Central Asia outbreak isolates.

The resistance mutations embedded in these clusters indicate that resistance was largely fixed in common ancestors and propagated through clonal expansion rather than acquired independently. Canonical mutations such as KatG S315T and RpoB S450L, which confer resistance to isoniazid and rifampicin at low fitness cost, dominated all three major clusters. Fluoroquinolone resistance, present in roughly a third of drug-resistant isolates, was driven mainly by GyrA D94G and A90V substitutions, while bedaquiline and clofazimine resistance was linked primarily to loss-of-function variants in Rv0678, notably the 192_ins_g and 141_ins_c frameshift insertions.

Resistance to the newer Group A agents central to modern all-oral therapy remained uncommon, offering a measure of reassurance. WGS-inferred bedaquiline resistance was detected in only 90 isolates overall, about 2.8 percent of the drug-resistant cohort, and linezolid resistance in just 33 isolates, or 1.1 percent. Yet the researchers identified 11 isolates carrying variants associated with combined resistance to fluoroquinolones, bedaquiline, and linezolid, a combination that would compromise both BPaL and BPaLM regimens. Notably, seven bedaquiline-resistant XDR isolates carried atpE variants, six of them in Lineage 2 strains scattered across different clades, suggesting independent acquisition. Comparative analysis showed the Ukrainian atpE-mutant isolates were separated by at least 28 SNPs from publicly available Russian genomes, ruling out a single shared clone and pointing to a possible regional propensity for this resistance mechanism that the authors say warrants urgent investigation.

Temporal analysis across four consecutive 12-month intervals revealed significant declines in resistance to moxifloxacin and levofloxacin, each falling from around 41 percent in 2019–2020 to roughly 28 percent in 2022–2023, with pyrazinamide resistance also declining modestly. The fluoroquinolone trends held up in sensitivity analyses restricted to the 12 consistently sampled regions and within the pooled major genomic clusters. In parallel, recorded treatment success rose from 59.6 percent to 73.4 percent over the study period. The authors, however, caution strongly against causal interpretation: the trends coincided with changes in regional sampling after the February 2022 invasion, population displacement, diagnostic disruptions, evolving treatment policies, and the pandemic, and individual-level regimen data were unavailable.

The study also highlights the social epidemiology of the epidemic. Patients with drug-resistant disease were younger and more often male, and more frequently reported recurrent tuberculosis, previous treatment failure, homelessness, drug use, incarceration, HIV infection, and viral hepatitis. Lineage 2 infection was independently associated with rifampicin-resistant or MDR status, fluoroquinolone resistance, and genomic clustering in multivariable models adjusted for demographic, clinical, social, temporal, and regional factors.

The authors conclude that sustained genomic surveillance of drug-resistant tuberculosis in Ukraine and neighbouring countries is essential, particularly for dominant Lineage 2 clones and for resistance to fluoroquinolones, bedaquiline, and linezolid. They call for systematic fluoroquinolone testing before deployment of shorter all-oral regimens, targeted phenotypic confirmation of bedaquiline and linezolid susceptibility, integration of whole-genome sequencing into routine care, and cross-border data sharing to track tuberculosis among displaced, migrant, and refugee populations. As Ukraine continues to rebuild its health system amid ongoing conflict, the genomic record preserved in this cohort provides both a warning about the entrenched clones fuelling the epidemic and a template for surveillance elsewhere in Eastern Europe.

Subject of Research: Genomic surveillance of drug-resistant Mycobacterium tuberculosis in Ukraine from 2019 to 2023

Article Title: Drug-resistance profiles, population structure, genomic clustering, and temporal trends in drug resistance among Mycobacterium tuberculosis complex isolates in Ukraine, 2019–2023: a multicentre cohort study

Article References: Butov, D., Butova, T., Miasoiedov, V., Feshchenko, Y., Nakonechna, O., Kuzhko, M., Rosenthal, A., Grinev, A., Hoppes, D., Kilmnick, J., Levandovska, D., Hryhorieva, A., Vekshyn, V., Abramova, L., Niemann, S., & Dreyer, V. (2026). Drug-resistance profiles, population structure, genomic clustering, and temporal trends in drug resistance among Mycobacterium tuberculosis complex isolates in Ukraine, 2019–2023: a multicentre cohort study. The Lancet Regional Health – Europe, 70, Article 101865. https://doi.org/10.1016/j.lanepe.2026.101865

Image Credits: AI Generated

DOI: 10.1016/j.lanepe.2026.101865

Keywords: tuberculosis, drug resistance, whole-genome sequencing, Lineage 2 Beijing, MDR-TB, Ukraine, fluoroquinolone, bedaquiline, linezolid, genomic clustering, outbreak clones, public health surveillance