The authors frame their analysis around the fundamental steps of the orthohantavirus infection cycle. Viral entry begins when the Gn glycoprotein docks onto host surface receptors, including β3-integrins and protocadherin-1 (PCDH1). Once internalized via endocytosis, the acidification of the endosomal compartment triggers a conformational change in the viral envelope that drives membrane fusion, releasing the viral genome into the cytoplasm. The viral RNA-dependent RNA polymerase (RdRp) then initiates both transcription and genome replication. Structural proteins and newly synthesized RNA genomes assemble at the Golgi complex or plasma membrane before budding out as mature infectious virions. Each of these stages presents a distinct molecular vulnerability that chemists and virologists can exploit with rationally designed small molecules, peptides, or biologics.

Among the most extensively characterized entry inhibitors are cyclic peptides identified through phage display libraries. Early work identified cyclic nonapeptides that bind the β3-integrin receptor used by pathogenic orthohantaviruses such as Sin Nombre virus (SNV), Hantaan virus (HTNV), Seoul virus (SEOV), and Puumala virus (PUUV) to enter human endothelial cells. Of seventy sequences screened, eleven showed inhibitory effects exceeding 80 percent against SNV in vitro, with the most potent, CPFVKTQLC, demonstrating high inhibitory potency when presented on phage particles. This peptide was subsequently refined through alanine substitution and deletion scanning to a minimal functional unit, the cyclic pentapeptide cyclo-[CPFVC]. However, because free monovalent peptides proved less effective than phage-displayed versions, researchers pivoted toward multivalent presentation on nanoparticles, targeting the viral glycoproteins directly rather than the host receptor. When the peptide CLVRNLAWC was displayed on 100-nanometer nanoparticles at a 4:1 ratio, inhibition of SNV rose from 9 percent as a free peptide to 32.5 percent. The peptide CQATTARNC achieved even greater efficacy, surpassing 50 percent inhibition when the nanoparticle-to-virus ratio was increased to 20:1.

Building on these strategies, investigators extended the approach to Andes virus (ANDV) by eluting peptide libraries from purified virions using monoclonal antibodies against the Gn and Gc glycoproteins. A key finding was that combining peptides targeting different Gn- and Gc-specific sequences produced synergistic inhibition significantly more powerful than single-peptide treatments, underscoring the value of simultaneously engaging multiple viral epitopes. To transition from biological peptides to drug-like molecules, researchers used the three-dimensional structure of the refined pentapeptide cyclo-[CPFVC] in a ligand-based virtual screening campaign evaluating over 600,000 small molecules. This effort identified compounds such as G319–0078 and 8012-0652, which were approximately 2,000 times more potent than the original cyclic peptides, achieving high-affinity inhibition in the nanomolar range with EC50 values between 1.3 and 2.5 nanomolar in vitro. These molecules represent a compelling proof of concept that computational screening can transform a modest peptide lead into a highly potent preclinical candidate warranting further in vivo evaluation.

A distinct strategy targets the membrane fusion process itself. The orthohantavirus Gc glycoprotein functions as a class II fusion protein, and completing membrane fusion requires a major structural relocation of Domain III (DIII) to achieve the post-fusion state. Exploiting this mechanical vulnerability, researchers demonstrated that viral entry can be blocked using recombinant fragments and synthetic peptides derived from the Gc DIII and stem region. These fragments allow initial Gc trimerization to proceed but ultimately prevent membrane hemifusion and fusion pore formation. The recombinant DIII fragment inhibited ANDV fusion by approximately 70 percent at 25 micromolar and demonstrated cross-reactivity by reducing PUUV-mediated fusion by 50 percent. Even greater potency was observed with the R2 peptide, derived from the C-terminal portion of the Gc stem region, which inhibited ANDV fusion by up to 95 percent in vitro. Although efficacy dropped to a 70 percent cross-reduction against PUUV-mediated fusion at 20 micromolar due to high genetic divergence between the two viruses, the results still demonstrate meaningful cross-protective potential.

Natural products have also yielded promising entry inhibitors. Griffithsin (GRFT), an algae-derived lectin with demonstrated efficacy against coronaviruses, retroviruses, lentiviruses, flaviviruses, hepaciviruses, paramyxoviruses, and herpesviruses, potently inhibits ANDV entry in vitro. A synthetic trimeric variant possessing nine oligosaccharide-binding sites showed even greater potency against ANDV and SNV. In vivo, GRFT inhibited HTNV and provided partial protection in suckling mice. Its scalability and ease of purification highlight its translational potential. Separately, bovine lactoferrin (LF) prevents initial viral adsorption and adhesion of SEOV, likely by interacting with cellular glycosaminoglycans such as heparin or by sterically hindering virus binding to host β-integrin and lectin receptors. Prophylactic administration of LF one day before orthohantavirus inoculation improved survival rates in mice up to 70 percent at 160 milligrams per kilogram, with a double-administration regimen enhancing survival to over 94 percent. When combined with subsequent ribavirin treatment, the two agents functioned synergistically, profoundly reducing viral shedding and completely inhibiting viral protein expression.

Discovered through high-throughput screening of over 2,300 approved drugs and traditional Chinese medicines, cepharanthine (CEP), a compound isolated from Stephania cephalantha Hayata, has emerged as a potent viral entry and membrane fusion inhibitor. In vitro, CEP inhibited HTNV pseudovirus with an EC50 of 0.9 micromolar and reduced viral nucleic acid copies by 73 percent at 20 micromolar. In vivo, administration to BALB/c mice five hours post-infection significantly reduced infection by 93 percent at 90 milligrams per kilogram per day and 94 percent at 180 milligrams per kilogram per day. Given its established safety profile, CEP represents a high-priority candidate for repurposing and advancement into human clinical trials against orthohantaviruses.

On the replication front, ribavirin remains the historical benchmark for anti-hantaviral therapy. Once transported into host cells, ribavirin is phosphorylated into metabolites including ribavirin-5′-monophosphate, which inhibits inosine monophosphate dehydrogenase and depletes intracellular GTP, and ribavirin-5′-triphosphate, which selectively interacts with the viral RdRp as its primary mechanism of action. Incorporation of ribavirin leads to accumulation of genomic errors and dramatic loss of virus viability through error catastrophe. Preclinical evaluation confirmed that early ribavirin administration reduces viral loads and prevents severe disease in suckling mouse models infected with HTNV and hamster models infected with ANDV. Clinically, intravenous ribavirin demonstrated efficacy in a double-blind, placebo-controlled trial of 242 patients with HTNV-induced HFRS, significantly reducing hemorrhagic and overall mortality. However, efficacy varies significantly across strains and clinical syndromes. In the context of HCPS caused by ANDV, ribavirin intervention eliminated mortality compared to a 35 percent historical case-fatality rate and reduced the incidence of the severe cardiopulmonary phase from 84 to 57 percent. Conversely, in a larger placebo-controlled, double-blinded trial evaluating intravenous ribavirin for HCPS, the drug failed to demonstrate overall therapeutic efficacy, revealing no significant difference in survival between groups. Trials in HFRS caused by PUUV also showed insufficient efficacy and safety, likely due to delayed drug administration and a weaker correlation between viral load and disease severity compared to HTNV infection. Furthermore, the clinical utility of ribavirin is constrained by dose-limiting toxicities, most notably reversible hemolytic anemia reported in 71 percent of recipients.

Favipiravir (T-705), originally discovered during a screening for anti-influenza compounds, has emerged as a potent viral replication inhibitor. Upon cellular entry, host enzymes convert favipiravir into its active form, favipiravir-RTP, a purine nucleotide analogue incorporated into nascent viral RNA that induces lethal mutagenesis. Favipiravir potently inhibited ANDV and SNV in vitro and provided 100 percent survival in lethal ANDV hamster models when treatment was initiated before viremia, making it an important candidate for post-exposure prophylaxis. Combination with ribavirin produced highly synergistic effects, with peak antiviral activity observed at a ratio of 12 micromolar favipiravir to 18.75 micromolar ribavirin, achieving greater than 99 percent overall inhibition of HTNV. Additionally, ETAR (1-β-D-ribofuranosyl-3-ethynyl-[1,2,4]triazole) demonstrated high potency in vitro against HTNV and ANDV, and in vivo studies using HTNV-challenged suckling mice showed approximately 25 percent protection at doses of just 12.5 and 25 milligrams per kilogram, mirroring ribavirin’s therapeutic outcomes at a fraction of the concentration. Beyond nucleoside analogues, the viral nucleoprotein has emerged as a promising alternative target. Terpene-isoindole conjugates such as compound 18 demonstrated potent anti-HTNV activity with exceptionally low toxicity, and molecular docking suggests they bind to the highly conserved RNA-binding site of the nucleoprotein, competing with viral RNA and effectively arresting intracellular replication. Similarly, morin, a natural flavonoid found in plants like mulberry and perilla, binds directly to the core assembly region of the viral nucleoprotein, sterically hindering RNA encapsidation and disrupting the formation of functional replication complexes.

In the absence of licensed antiviral treatments, clinical management remains primarily supportive, focusing on intensive monitoring and treatment of respiratory, cardiac, and renal complications. Achieving early diagnosis remains a primary clinical challenge, as the efficacy of supportive care is substantially reduced if initiated after the onset of severe disease. Host-targeted and anti-inflammatory strategies have recently been explored to mitigate the characteristic vascular leak and cytokine storm, most notably during the 2026 cruise ship outbreak, where the JAK inhibitor baricitinib and the bradykinin B2 receptor antagonist icatibant were administered in a combined therapeutic regimen alongside ribavirin and favipiravir. Passive immunotherapy continues to be a major investigative focus, with convalescent plasma showing potential to reduce mortality and newer candidates such as the quadrivalent human polyclonal antibody SAB-163 and highly potent monoclonal antibodies demonstrating up to 100 percent protection in lethal animal models. Regarding prevention, Hantavax has been used to prevent HFRS in South Korea, and China routinely administers inactivated bivalent vaccines targeting HTNV and SEOV in endemic regions. However, a globally licensed orthohantavirus vaccine does not currently exist, leaving New World strains and HCPS without an approved preventative countermeasure. The review’s authors argue that expanding preclinical testing to include globally neglected variants, combined with modern in silico screening of extensive chemical and natural product libraries, will be essential to develop broad-spectrum hantaviral therapeutics capable of mitigating future epidemic threats.

Subject of Research: Antiviral drug discovery targeting the orthohantavirus life cycle

Article Title: Hantaviral therapeutics: life cycle targets and chemotherapeutic interventions

Article References: Kocabiyik, D. Z., de Araujo, J., Durigon, E. L., & Wrenger, C. (2026). Hantaviral therapeutics: life cycle targets and chemotherapeutic interventions. Frontiers in Cellular and Infection Microbiology, 16, Article 1972830. https://doi.org/10.3389/fcimb.2026.1972830

Image Credits: AI Generated

DOI: 10.3389/fcimb.2026.1972830

Keywords: orthohantavirus, antiviral discovery, viral entry inhibitors, RNA-dependent RNA polymerase, ribavirin, favipiravir, griffithsin, cepharanthine, nucleoprotein inhibitors, HFRS, HCPS, virtual screening

The research tackles a well-known bottleneck. Traditional experimental screening is expensive, slow, and constrained by the chemical diversity of available compound libraries. Computational models that estimate biological activity directly from molecular structure—known as quantitative structure–activity relationship, or QSAR, models—promise a faster route, but their reliability depends heavily on how they are built. The authors note that many published QSAR efforts suffer from insufficient feature optimization and ambiguity about whether the task is regression, predicting a continuous activity value, or classification, sorting compounds into active and inactive categories. That ambiguity undermines both interpretability and the consistency of performance evaluation.

To address these problems, the team assembled a dataset of 532 structurally diverse compounds with experimentally reported analgesic activity values drawn from published literature. Each molecule was characterized by 227 molecular descriptors spanning constitutional, topological, geometrical, electronic, and physicochemical properties. The activity score was treated as a continuous target for regression, while a statistically derived threshold converted it into a binary active-or-inactive label for classification. Before training, all inputs were normalized with min–max scaling so that no single feature dominated the learning process.

The most consequential step was feature selection. Using a stepwise elimination procedure, the researchers iteratively removed low-importance descriptors while monitoring model performance, combining the Pearson correlation coefficient with a relative prediction error metric as the selection criterion. The descriptor set shrank from 227 variables to 105, and the effect was unambiguous: predictive performance improved steadily as weakly correlated features were pruned, reaching an optimum at 105 descriptors before declining beyond that point. The retained features describe molecular connectivity, polarity, and hydrogen-bonding potential—properties known to modulate receptor binding affinity and pharmacodynamic behavior. The result is a leaner model that generalizes better, trains faster, and resists overfitting, a chronic hazard in high-dimensional neural modeling.

The network itself is a feed-forward multilayer perceptron implemented in MATLAB, with an input layer of 72 neurons feeding three hidden layers of 22, 9, and 2 neurons, each using the hyperbolic tangent sigmoid activation function, and a single linear output neuron. Hyperparameters, including the number of hidden neurons, learning rate, and regularization factors, were tuned through grid search to minimize the average absolute relative error. Training relied on the Levenberg–Marquardt algorithm, a second-order optimization method prized for its rapid convergence and stability in nonlinear parameter spaces. The final model converged in fewer than 50 iterations, outperforming conventional gradient-descent training in both speed and stability, and regression plots showed near-linear agreement between predicted and experimental values across both training and validation sets.

The headline numbers place the model among the best-performing approaches reported for comparable QSAR tasks. Comparable studies using shallow or single-hidden-layer networks typically report correlation coefficients between 0.85 and 0.93. This framework achieved R = 0.959 for regression and 98.01 percent accuracy for classification. In their comparative analysis, the authors contrast their results with deep neural networks used for triple-negative breast cancer inhibitor and GPCR agonist discovery, multilayer perceptrons classifying lung surfactant inhibitors, principal-component neural networks applied to 95 analgesic compounds, and multiple linear regression models built on small sets of pyrimidine derivatives. They attribute their edge to the joint optimization of architecture and feature selection, which together improved the model’s capacity to capture complex nonlinear structure–activity relationships.

What elevates the work beyond raw predictive power is its attention to interpretability. The descriptor classes retained by the model—molecular connectivity indices, polarity-related parameters, hydrogen-bonding capacity, and lipophilicity descriptors—are tied to receptor accessibility, membrane permeability, and ligand–target interaction potential, the very factors that govern analgesic potency. This gives medicinal chemists a foothold for rational compound optimization rather than a black-box score. The authors are candid about the limits, however: because neural networks are inherently nonlinear, complete mechanistic interpretability remains out of reach, and they propose incorporating explainable AI methods or fragment-level attribution analysis in future iterations to extract deeper structural insight.

The team also acknowledges a gap in the competitive landscape. Modern ensemble methods such as Random Forest and gradient boosting can achieve competitive performance on moderate-sized datasets while offering better interpretability, but benchmarking against them fell outside the scope of the present study—a limitation the authors explicitly flag, with comparative evaluation planned as future work. They are equally clear that the model is computational and does not replace experimental validation. Its purpose is to reduce experimental burden by prioritizing compounds before laboratory testing, and prospective validation on newly designed or synthesized molecules is identified as the critical next step for translational applicability.

There is also a sustainability dimension. By cutting dependence on exhaustive laboratory screening, the framework aligns with eco-efficient pharmacological innovation—minimizing experimental waste while enabling broader exploration of chemical space. The authors frame the hybrid feature-selection-plus-neural-network strategy as a reliable, interpretable, and resource-conscious approach to predictive pharmacology, one that complements rather than replaces experimental workflows and remains compatible with translational drug development strategies.

Looking ahead, the researchers outline several extensions: adapting the optimized framework to other pharmacological classes, implementing transfer learning for multitarget predictions, and integrating explainable AI tools to illuminate mechanism. For a field where the vast majority of early-stage candidates never reach the clinic, a model that can reliably flag the most promising analgesic candidates before synthesis—while explaining, at least in part, which molecular features drive its predictions—represents a meaningful step toward faster, cheaper, and more rational drug discovery. The dataset itself is restricted by licensing agreements and cannot be redistributed, but the methodological blueprint—rigorous descriptor reduction, explicit task definition, and systematic architecture optimization—offers a template other labs can follow as artificial intelligence continues to reshape how new medicines are found.

Subject of Research: Deep learning-based QSAR modeling for predicting analgesic activity in early-stage drug discovery

Article Title: Integrative deep learning strategies to enhance early-stage drug discovery: optimizing computational structure–activity modeling for pharmacotherapeutic innovation

Article References: Rezazi, S., Si-Moussa, C., & Hanini, S. (2026). Integrative deep learning strategies to enhance early-stage drug discovery: optimizing computational structure–activity modeling for pharmacotherapeutic innovation. Journal of Pharmacy & Pharmaceutical Sciences, 29, Article 16155. https://doi.org/10.3389/jpps.2026.16155

Image Credits: AI Generated

DOI: 10.3389/jpps.2026.16155

Keywords: artificial intelligence, drug discovery, QSAR, neural networks, analgesics, molecular descriptors, feature selection, Levenberg-Marquardt algorithm, virtual screening, pharmacology, predictive modeling, medicinal chemistry

The review, authored by researchers working at the interface of computational chemistry and virology, takes aim at a quiet but consequential problem in the application of machine learning to antiviral discovery. Performance claims for AI models in drug design are frequently inferred from general protein-ligand benchmarks, datasets dominated by well-studied human targets such as kinases and proteases. Viral targets, and respiratory RNA virus polymerases in particular, are underrepresented in these benchmarks. The consequence is that a model may appear excellent on paper while failing to capture the specific structural and mechanistic complexities that govern whether a molecule actually inhibits a viral polymerase in an infected cell.

RNA-dependent RNA polymerases occupy a special position among antiviral targets. They are among the most conserved components of RNA viruses, which means that a compound active against the polymerase of one strain has a reasonable chance of retaining activity against related strains, and the enzyme has no direct human homolog, reducing the risk of host toxicity. These features make RdRps pharmacologically attractive, and the clinical success of nucleoside analogs such as remdesivir and the influenza polymerase inhibitor baloxavir marboxil confirms that the target class is druggable. Yet the same enzymes are notoriously difficult computational subjects. Their ligandable states depend on the presence of RNA templates and products, on divalent metal ions such as magnesium that coordinate the catalytic chemistry, on cofactors and accessory proteins that remodel the polymerase architecture, and on conformational transitions that shuttle the enzyme between initiation, elongation, and proofreading states. A static crystal structure captures only one frame of a dynamic molecular machine.

The review synthesizes the current landscape of computer-aided drug design as applied to these targets, covering classical molecular docking, machine learning-assisted virtual screening, modern molecular representations, pose-generation models, generative approaches to molecular design, and the benchmarking frameworks used to assess protein-ligand affinity prediction. Geometric deep learning, a branch of machine learning that respects the three-dimensional symmetry and structure of molecular data, features prominently as a technology that has advanced pose prediction and affinity estimation. Generative models that propose entirely novel chemical structures have likewise expanded the design space accessible to computational screening campaigns. The authors acknowledge this progress while insisting that progress and validation are not the same thing.

At the heart of the critique is a set of metrics that the authors consider insufficient on their own. Docking scores, predicted binding affinities, and root-mean-square error values on affinity benchmarks are the currencies in which computational models are typically evaluated. The review contends that these numbers become meaningful only when they are linked to a chain of downstream evidence: demonstration that the compound engages the intended target, evidence of enzymatic inhibition in biochemical assays, measurable antiviral activity in cell-based infection models, acceptable cytotoxicity profiles, characterization of resistance mutations that arise under drug pressure, and pharmacological feasibility including absorption, distribution, metabolism, excretion, and toxicity considerations. A model that ranks compounds well by docking score but produces candidates that fail at the first cell-based hurdle has not solved the problem it claims to address.

Part of the difficulty, the authors suggest, lies in biases embedded in molecular docking itself. Docking algorithms and the scoring functions they employ were largely developed and calibrated on datasets enriched for non-viral targets with different binding-site characteristics. Viral polymerase active sites present unusual challenges: they are often larger and more flexible than typical enzyme pockets, they accommodate nucleic acid substrates alongside small molecules, and their catalytic chemistry depends on metal coordination that many scoring functions do not model explicitly. When these biases propagate into AI systems trained on docking outputs or on benchmarks derived from non-viral proteins, the resulting models inherit systematic blind spots that are invisible to standard evaluation metrics.

To address these shortcomings, the review proposes an RdRp-centered AI pipeline that reorganizes the discovery workflow around virological reality. The pipeline begins with curated structural ensembles of polymerase complexes that capture the conformational diversity of the target rather than a single static structure. Benchmarking is designed to be leakage-resistant, meaning that the evaluation sets are constructed to prevent models from succeeding through memorization of training data rather than genuine generalization. Evaluation tasks are antiviral-specific rather than borrowed wholesale from general protein-ligand prediction. Computational prioritization is then filtered through ADME and toxicity screens before candidates proceed to biochemical validation against the polymerase, cell-based infection assays, and resistance profiling that reveals whether a compound’s activity is durable or easily circumvented by viral mutation. The results of each experimental round feed back into iterative model refinement, closing the loop between prediction and measurement.

The emphasis on leakage-resistant benchmarking deserves particular attention because it addresses a problem that extends well beyond virology. Machine learning models in drug discovery can achieve impressive benchmark results by exploiting subtle statistical regularities in how datasets are constructed, for example when highly similar compounds appear in both training and test sets, or when assay artifacts correlate with activity labels. In the antiviral context, where the ultimate question is whether a computational pipeline enriches for compounds with experimentally actionable antiviral activity, such artifacts can produce a misleading picture of readiness. The review’s proposal to validate whether computational prioritization actually enriches active compounds, rather than merely whether it scores well on held-out affinity data, reframes the evaluation question in terms that matter to medicinal chemists and virologists.

The polymerase-centered framing also carries strategic significance for pandemic preparedness. Because RdRps are conserved across many RNA viruses, advances in computational methods validated on one polymerase may transfer more readily to related enzymes than structure-specific approaches would. Benchmarking AI models against a panel of respiratory virus polymerases, rather than against generic protein-ligand datasets, would produce evaluation infrastructure that is directly relevant to the pathogens most likely to drive the next therapeutic emergency. The review positions respiratory RNA virus polymerases not only as high-value drug targets in their own right but as a complementary test case for the broader discipline of AI-guided drug discovery, one whose peculiarities expose weaknesses that conventional benchmarks conceal.

The broader message is a call for epistemic discipline in a field moving at remarkable speed. Generative AI and deep learning have genuinely expanded what computational drug discovery can attempt, and the urgency of respiratory viral threats gives that capability real public health weight. But the review insists that the measure of success is not the elegance of the model or the magnitude of the docking score. It is whether a computationally prioritized compound, when placed in a biochemical assay, an infected cell culture, and eventually a pharmacologically informed development pathway, demonstrates target engagement, antiviral activity, an acceptable safety margin, and a resistance profile that permits durable use. Moving beyond docking scores, the authors conclude, requires benchmarking models under virologically realistic conditions, and the respiratory RNA virus polymerase is where that discipline can be built.

Subject of Research: Artificial intelligence methods for antiviral drug discovery targeting respiratory RNA virus RNA-dependent RNA polymerases

Article Title: Virology-aware artificial intelligence for respiratory antiviral discovery: from molecular docking biases to RdRp-centered benchmarking

Article References: Ramos-Cortes, L. R., Santos-Mena, A. O., Flores-Padilla, L. E., Miranda-Lizárraga, K. P., Cañedo-Figueroa, D. M., Ontiveros-Zúñiga, L. F., Lira-Hernández, F. I., Osuna-Ramos, J. F., Noriega, S., Cardoso-Ortiz, J., Alcalá-Ramírez, V., & De Jesús-González, L. A. (2026). Virology-aware artificial intelligence for respiratory antiviral discovery: from molecular docking biases to RdRp-centered benchmarking. Frontiers in Pharmacology, 17, Article 1940098. https://doi.org/10.3389/fphar.2026.1940098

Image Credits: AI Generated

DOI: 10.3389/fphar.2026.1940098

Keywords: artificial intelligence, antiviral drug discovery, RNA-dependent RNA polymerase, respiratory RNA viruses, molecular docking, machine learning, geometric deep learning, virtual screening, benchmarking, SARS-CoV-2, influenza, drug resistance

Tuberculosis remains the leading cause of death from infectious diseases worldwide, with an estimated 10.6 million new cases and 1.6 million associated deaths each year. The disease is caused by Mycobacterium tuberculosis, a bacterium that forms granulomatous lesions in the lungs and can exploit the body’s own protective structures to spread. Chronic pulmonary aspergillosis, a progressive fungal infection acquired through inhalation of conidia, affects an estimated 1.8 million people annually. Meanwhile, Escherichia coli has become the pathogen associated with the highest mortality rate linked to antimicrobial resistance, ranking in the highest quartile of the WHO’s 2024 Bacterial Priority Pathogens List. The researchers chose quinoline derivatives because this class of aromatic nitrogen heterocycles has demonstrated antimicrobial, antifungal, and antituberculosis properties, making them privileged structures in medicinal chemistry capable of modulating multiple biological targets.

The four compounds studied, designated QNL1 through QNL4, are derivatives of 1,4-dihydro-4-oxo-quinoline-3-carbohydrazide. QNL1 and QNL2 carry a chlorine atom at carbon 7 of the quinoline ring, while QNL3 and QNL4 have it at carbon 6. The phenyl ring attached through the carbohydrazide linker bears either two methoxy groups at positions C3 and C4 (QNL1 and QNL3) or three methoxy groups at C3, C4, and C5 (QNL2 and QNL4). These subtle differences in halogen position and methoxy substitution pattern proved to have measurable consequences for electronic behavior, molecular polarity, and predicted biological activity.

To characterize the electronic structure of each derivative, the team employed Density Functional Theory calculations using the B3LYP hybrid functional and the 6-311++G(d,p) basis set, implemented in the ORCA 5.0.4 program. The molecules were optimized both in vacuum and in dimethyl sulfoxide (DMSO) solvent, with the Conductor-like Polarizable Continuum Model (CPCM) used to account for solvation effects. From the optimized geometries, the researchers extracted frontier molecular orbital energies, the HOMO-LUMO energy gap, electronegativity, ionization potential, electron affinity, chemical hardness, softness, electrophilicity index, and nucleophilicity index. These conceptual DFT descriptors provide a quantitative framework for understanding molecular reactivity and predicting how the compounds might behave in biological environments.

The geometric optimization revealed that none of the four molecules adopts a completely planar conformation. The quinoline nucleus itself remains nearly planar, with an average angular deviation of only 0.266 degrees between the fused rings, but significant conformational variations arise from rotation of the substituent groups. The researchers validated their computational approach by comparing calculated bond lengths and angles against crystallographic data for a related quinolinium compound. The calculated C8-C7 bond lengths were 1.452 angstroms in DMSO and 1.459 angstroms in vacuum, compared to an experimental value of 1.368 angstroms, with the difference attributed to the replacement of a hydrogen atom by a carbonyl group in the derivatives. Average deviations between calculated and experimental parameters ranged from 0.010 to 0.091 angstroms in vacuum and 0.005 to 0.084 angstroms in DMSO, confirming the reliability of the computational method.

Analysis of the frontier molecular orbitals showed that the HOMO isosurface is predominantly distributed over the methoxy-substituted benzene ring and the N’-methylidenoformohydrazide chain in all derivatives, regions associated with electron density donation during electrophilic attack. The position of the chlorine atom on the quinoline ring exerts little influence on the most reactive region, since the HOMO is not significantly localized over the quinoline portion. However, the number of methoxy groups plays a critical role: as methoxy substitution increases, electronic density disperses more homogeneously over the benzene ring, reducing susceptibility to localized chemical attack. In DMSO, the solvent’s high dielectric constant spreads the HOMO over a broader region of the molecule and lowers its energy. The LUMO, conversely, is distributed mainly over the quinoline region in vacuum, with derivatives bearing more methoxy groups showing lower LUMO energies and greater capacity to accept electron density.

The ADMET evaluation employed multiple computational platforms including SwissADME, ADMETlab 3.0, admetSAR 3.0, pkCSM, Pred-hERG 5.0, StopTox, and the ADMET Prediction Service from the Laboratory of Molecular Modeling. QNL1 and QNL3 emerged as the leading compounds, both achieving oral bioavailability scores of 0.55 and demonstrating high intestinal absorption exceeding 95 percent. Their Caco-2 permeability values of approximately -4.92 in units of 10⁻⁶ cm/s indicate intermediate membrane penetration. Both compounds share identical molecular weights of 385.08 g/mol, topological polar surface areas of 92.78 square angstroms, and hydrogen bond donor counts of 2. Their logP values of 2.76 and 2.86 reflect a favorable balance between hydrophilic and lipophilic characteristics conducive to passive membrane diffusion.

Toxicity predictions revealed low cardiotoxic risk for both lead compounds, with hERG channel inhibition probabilities of approximately 0.08 for QNL1 and 0.06 for QNL3. The subtle difference in cardiotoxic risk between the two was attributed to the shift in chlorine position from ortho to meta on the quinoline ring. Both compounds showed moderate potential for CYP3A4 inhibition and moderate risk for drug-induced liver injury, warranting further experimental investigation. Acute toxicity predictions indicated low toxicity via inhalation, oral, and dermal routes, though both compounds showed a 65 percent probability of causing eye irritation or corrosion. The AMES toxicity test produced the highest predicted probability among all toxicological parameters evaluated, suggesting potential for DNA damage that must be confirmed experimentally before these compounds can advance.

Virtual target prediction using SwissTargetPrediction identified vascular endothelial growth factor receptor 2 (KDR) as the primary biological target for both QNL1 and QNL3, with association scores of 1300 and 1447 respectively. KDR is a high-affinity receptor tyrosine kinase crucial for angiogenesis and vascular permeability. In tuberculosis, granuloma formation creates local hypoxia that drives peripheral angiogenesis, and modulating the VEGF pathway may improve granuloma perfusion and reduce bacterial spread. The kinase class dominated the predicted target profile, increasing from 56 percent of predicted targets for QNL1 to 60 percent for QNL3, suggesting that the latter compound may exhibit greater selectivity while maintaining its primary mechanism of action.

The study concludes that QNL3 represents the priority candidate for further development as an oral drug, owing to its superior balance between systemic distribution and cardiovascular safety compared to QNL1. While QNL1 exhibits favorable physicochemical properties for absorption, QNL3’s metabolic profile and lower predicted cardiotoxic risk position it ahead in the ADMET framework. The preservation of pharmacophore characteristics across both lead compounds suggests structural elements compatible with interaction with VEGFR-2, justifying their inclusion in comparative analyses and additional screening. The researchers emphasize that these findings contribute to the rational design of new therapeutic strategies against tuberculosis, aspergillosis, and bacterial infections, highlighting the importance of integrating quantum mechanical calculations with predictive pharmacokinetic modeling in early-stage drug discovery.

Subject of Research: Computational mapping of electronic structure and ADMET properties of halogenated quinoline derivatives as potential antimicrobial and antituberculosis drug candidates

Article Title: Mapping the electronic and biological landscape of halogenated aryl quinoline derivatives via computational approaches

Article References: Sales, M. A., do Nascimento Gonçalves, C., dos Santos, A. L. S., da Rocha, M. N., Marinho, M. M., & Marinho, E. S. (2026). Mapping the electronic and biological landscape of halogenated aryl quinoline derivatives via computational approaches. Discover Chemistry, 3(1), Article 436. https://doi.org/10.1007/s44371-026-00886-0

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00886-0

Keywords: quinoline derivatives, DFT calculations, ADMET prediction, tuberculosis, drug discovery, KDR receptor, virtual screening, medicinal chemistry, antimicrobial resistance, hERG cardiotoxicity, molecular orbital analysis, oral bioavailability

The rationale for a multitarget approach stems from the sheer complexity of Alzheimer’s pathology. The disease involves protein aggregation of amyloid-beta outside neurons and tau inside neurons, cholinergic neurodegeneration, oxidative stress, neuroinflammation, and synaptic dysfunction, all interconnected. Familial cases, which account for less than 5 percent of the total, are driven by mutations in the amyloid precursor protein or the gamma-secretase subunits presenilin 1 and 2, leading to overproduction of aggregation-prone amyloid-beta fragments. Sporadic cases, representing more than 95 percent, are strongly influenced by the apolipoprotein E4 allele, which impairs clearance of insoluble amyloid-beta and disrupts the blood-brain barrier. Because no single molecular driver dominates, the authors argue that compounds modulating several targets at once may be more effective than the conventional single-target strategy that has repeatedly failed in clinical trials.

To identify the most relevant protein targets, the team employed a literature-mining strategy based on normalized PubMed association scores. They retrieved a list of 20,594 protein-coding human genes from NCBI’s gene resource, counted citations for each gene, and then counted co-citations with Alzheimer’s disease using MeSH terms. Dividing the co-citation count by the total citation count produced a normalized association score, which was then adjusted with a penalty factor to reduce the contribution of genes with very low citation counts. An in-house Python script automated the citation counts for all protein-coding genes. The top ten proteins most strongly associated with Alzheimer’s disease emerged as presenilin 1 and 2, amyloid precursor protein, beta-secretase 1, microtubule-associated protein tau, apolipoprotein E4, acetylcholinesterase, butyrylcholinesterase, cyclin-dependent kinase 5, glycogen synthase kinase 3 beta, and brain-derived neurotrophic factor.

With targets in hand, the researchers turned to structure-based virtual screening. They retrieved a virtual library of 2,847 phytochemical compounds from the PubChem database classification of KEGG phytochemicals, prepared the ligands by adding polar hydrogens and Gasteiger charges, and docked them against representative PDB structures of each target using AutoDock Vina. Crucially, they employed blind docking, with grid boxes covering the entire volume of each protein rather than a predefined binding site, allowing the compounds to explore the full protein surface. The docking protocol was validated by redocking co-crystallized ligands into their native receptors, including donepezil with acetylcholinesterase, verubecestat with BACE1, and osugacestat with presenilin 1. Redocked poses yielded RMSD values of 2.1 angstroms or less, and the correlation between predicted and experimental free-energy values was moderately strong at R = 0.61, attesting to the precision of the setup.

The screening results were striking. Retaining the top 100 ligands for each target produced 362 unique phytochemicals, indicating that only a portion of the library binds to multiple targets simultaneously. Applying a multitarget filter that required binding to three or more Alzheimer’s-related proteins narrowed the selection to 96 compounds. A further filter based on heavy atom count, keeping only ligands with 33 heavy atoms or fewer, reduced the final dataset to 36 compounds. The heavy-atom criterion reflects ligand efficiency metrics, where smaller compounds with good potency are preferred, and correlates with better predicted blood-brain barrier penetration using a cubic polynomial function developed by Weaver and coworkers. Of these 36 top multitarget ligands, 11 fulfill Lipinski’s rule of five with no violations, while 25 have a single logP violation.

The 36 compounds fall into five chemical families. Alkaloids include rutaecarpine, sanguinarine and strychnine. Steroidal alkaloids comprise tomatidine, cyclopamine, jervine, verticine and verticinone. Steroidal sapogenins include diosgenin, hecogenin, yamogenin, tigogenin, smilagenin, sarsasapogenin, neogitogenin and ruscogenin. Triterpenoids and sterols form the largest group, with alpha-spinasterin, fucosterol, stigmasterol, beta-sitosterol, gamma-sitosterol, isofucosterol, germanicol, lupeol, beta-amyrin, alpha-amyrin, taraxerol, friedelin, tetrahymanol, tingenone, parkeol, methylenecycloartanol, ursolic acid, oleanolic acid and obacunone. A single sesquiterpene, lactucopicrin, rounds out the list. Several of these compounds have been independently shown in the literature to inhibit amyloid aggregation, tau phosphorylation, neuroinflammation and cholinergic dysfunction, lending credibility to the computational predictions.

To probe binding mechanisms in detail, the team performed interaction analysis using the Protein-Ligand Interaction Profiler on three representative compounds that commonly target both acetylcholinesterase and GSK3β: lactucopicrin, oleanolic acid and rutaecarpine. Lactucopicrin formed hydrogen bonds with TYR72, TYR124 and SER293 of acetylcholinesterase, residues part of the peripheral anionic site at the entrance of the active site gorge. Oleanolic acid formed a hydrogen bond with ARG247, part of the acyl loop door, suggesting a non-competitive inhibition mechanism. Rutaecarpine formed pi-pi stacking interactions with TRP286 and TYR341, similarly to the native ligand donepezil. On GSK3β, lactucopicrin formed hydrogen bonds with ASP200 and LYS85, while oleanolic acid formed salt bridges with ARG141 and ARG144, all residues within the ATP-binding pocket previously reported as consistent interaction hotspots in small-molecule inhibitors.

Molecular dynamics simulations of 50 nanoseconds for the same three compounds in complex with acetylcholinesterase and GSK3β demonstrated stable protein-ligand complexes. The acetylcholinesterase backbone remained globally stable with maximum RMSD values of approximately 2.5 angstroms, consistent with structurally stable complexes. In GSK3β, ligand binding displayed lower backbone RMSD compared to the unbound form, indicating reduced conformational fluctuations upon ligand binding. Notably, lactucopicrin demonstrated greater structural stability than the co-crystallized control ligand QQA throughout the simulation. RMSF analysis revealed residue-level fluctuations generally below 1.0 angstrom for both complexes, and radius of gyration analysis confirmed preserved global compactness of both protein structures. Rutaecarpine derivatives have been shown to inhibit acetylcholinesterase, oleanolic acid has been found to inhibit the same enzyme, and lactucopicrin has been shown to inhibit it as well, providing experimental corroboration for the computational findings.

The study is not without limitations. Molecular docking is inherently limited by the accuracy of scoring functions, and the beneficial or deleterious effects of these phytochemicals in Alzheimer’s treatment remain to be validated experimentally and clinically. The literature-mining method is subject to the current state of published research and may be biased against new or poorly studied proteins. Multitarget binding does not necessarily indicate a better chance at treatment; it could reflect less discriminate selectivity and greater potential toxicity, as seen with strychnine and sanguinarine, which are primarily associated with cytotoxic rather than therapeutic effects. Molecular dynamics simulations were performed for 50 nanoseconds in a single run for each complex, and replicate or extended simulations could provide additional confidence. Nevertheless, the authors conclude that several of the identified structurally diverse phytochemicals, including rutaecarpine, diosgenin, oleanolic acid, ursolic acid, lactucopicrin, hecogenin, sarsasapogenin, fucosterol, beta-sitosterol and stigmasterol, represent a valuable source of multitarget-directed lead compounds for further development in Alzheimer’s therapeutics, pending experimental validation.

Subject of Research: Virtual screening of phytochemicals against multiple protein targets of Alzheimer's disease

Article Title: Virtual screening of phytochemicals against multiple protein targets of Alzheimer’s disease

Article References: Mesfin, S., Bloch, N., & Samson, A. O. (2026). Virtual screening of phytochemicals against multiple protein targets of Alzheimer’s disease. Frontiers in Drug Discovery, 6, Article 1922953. https://doi.org/10.3389/fddsv.2026.1922953

Image Credits: AI Generated

DOI: 10.3389/fddsv.2026.1922953

Keywords: Alzheimer's disease, phytochemicals, virtual screening, molecular docking, molecular dynamics, polypharmacology, drug discovery, natural products, Virtual, screening, against, multiple

The study, published in Discover Chemistry, focused on an enzyme called phospholipase A1, or PlaF, a membrane-bound virulence factor first characterized in P. aeruginosa by Bleffert and colleagues in 2022. PlaF sits embedded in the cytoplasmic membrane, where it remodels glycerophospholipids through the so-called Lands cycle, generating medium-chain fatty acids that alter the biophysical properties of the bacterial membrane and regulate signaling cascades. This remodeling allows the pathogen to adapt to nutrient limitation, stress, and host-mediated attacks, and it coordinates iron acquisition through the pyoverdine pathway, a key element of virulence. Crucially, infection models have shown that bacteria lacking PlaF display dramatically reduced virulence without any change in growth kinetics, meaning the enzyme acts as a regulator of pathogenesis rather than a survival determinant.

That distinction matters enormously for drug design. Traditional antibiotics exert strong selective pressure by killing bacteria or halting their growth, which inevitably breeds resistance. Anti-virulence strategies, by contrast, aim to disarm the pathogen while leaving it metabolically viable, theoretically reducing the evolutionary incentive to develop resistance. PlaF also offers a structural advantage for selective targeting: although it shares the classical alpha/beta-hydrolase fold and the conserved Ser-His-Asp catalytic triad with human ABHD6, showing 27 percent sequence identity and 49 percent similarity, its membrane-associated substrate-access tunnels and a unique dimerization-dependent regulatory mechanism diverge substantially from the human enzyme. Recent work confirming that repurposed human phospholipase inhibitors such as GW4869 can sensitize P. aeruginosa to imipenem in a PlaF-dependent manner validated the enzyme as druggable, but those existing inhibitors are bulky, lipophilic molecules designed for mammalian systems, not optimized for bacterial membrane penetration or tunnel-specific engagement.

To fill that gap, the researchers assembled a library of 114 small molecules spanning seven chemical classes, drawn from their previous work on anticancer and anti-tuberculosis compounds, on the rationale that serine hydrolase inhibitors often exhibit cross-domain activity. The collection included thiophene-2-carboxamides, methyl 4-(4-amidoaryl)-3-methoxythiophene-2-carboxylates, urea and thiourea bearing thiophene derivatives, pyrimidine-5-carboxamides, triazinyl carboxamides, thienopyrimidine ureas, and pyrrolopyridine carboxamides. Using the crystal structure of PlaF available in the Protein Data Bank under the identifier 6I8W, the team deployed the Schrodinger Suite to prepare ligands with the OPLS4 force field, generate ionization states and tautomers with Epik, and filter candidates through Lipinski’s rule of five using the QikProp module. One hundred seven compounds survived drug-likeness screening and advanced to molecular docking.

The docking campaign used Glide’s extra precision protocol, ranking compounds by binding affinity and prioritizing those with scores of −6.0 kcal/mol or better. Subsequent MM/GBSA free energy calculations, which combine molecular mechanics energies with solvation models, flagged compounds with binding free energies of −50 kcal/mol or lower. Standouts emerged across the series: compound F11 from the thienopyrimidine class achieved an MM/GBSA score of −56.72 kcal/mol, while D17 from the pyrimidine carboxamides posted the highest binding free energy of the entire screen at −67.22 kcal/mol, attributed to robust halogen bonding. Structural interaction fingerprint analysis then converted residue-level contacts into comparable binary codes, revealing a consistent pharmacophoric pattern across high performers: roughly seven to ten hydrophobic contacts, three to four aromatic stacking interactions with residues such as PHE174 and TYR236, and limited but strategic hydrogen bonding with ARG233 or LEU232. Notably, compounds with excessive polar substitution or bulky aromatic expansion increased total contact counts without improving predicted stability, indicating that geometric complementarity within the narrow substrate-access tunnel matters more than interaction density.

The team then moved from computation to the bench, testing all 107 drug-like compounds against P. aeruginosa strain ATCC 27853 using a micro broth dilution assay with resazurin dye as a viability indicator, with ciprofloxacin as a positive control at a measured MIC of 0.25 micrograms per milliliter. Several compounds showed measurable activity, with minimum inhibitory concentrations of 100 to 200 micrograms per milliliter: B4 and B5 from the methoxythiophene series, C2 from the urea-thiourea thiophenes, D6 and D7 from the pyrimidine carboxamides, E6 and E8 from the triazines, F4 from the thienopyrimidines, and G2, G3, G11, and G16 from the pyrrolopyridines. While these MIC values are far weaker than conventional antibiotics, the researchers emphasize that PlaF inhibition is an anti-virulence strategy, so modest growth inhibition is expected and does not negate mechanistic relevance.

To probe how ligands would fare against a shifting target, the investigators engineered point mutations that replaced residues lining the T2 substrate-access tunnel—Asp74, Arg217, Ala218, Ala221, and Asn225—with bulky tryptophan, physically constricting the passage. Docking against these mutants confirmed the T2 tunnel as the principal route for ligand binding and revealed that some mutations actually enhanced affinity for certain scaffolds. Compounds D6 and E6 achieved improved docking scores of −10.55 and −9.38 kcal/mol against the TRP74 and TRP217 variants, while others, such as G16 against the TRP218 mutant, suffered sharp losses in binding, likely from steric clashes. The mutational blueprint suggests that halogenated, aromatic, and amide-bearing scaffolds capable of diverse non-covalent interactions are best positioned to remain effective even if the tunnel architecture shifts.

Molecular dynamics simulations provided the final filter. Each of fourteen protein-ligand complexes, embedded in a POPC lipid bilayer with explicit water and physiological salt, was simulated for 100 nanoseconds under semi-isotropic pressure coupling appropriate for membrane proteins. Six compounds—A3, B4, C2, D6, E6, and F4—maintained backbone RMSD values between 1.5 and 2.8 angstroms, preserved tunnel architecture, and showed restricted ligand mobility throughout the trajectory, outperforming even ciprofloxacin, which drifted beyond 4.5 angstroms in later simulation stages. Radius of gyration, solvent-accessible surface area, molecular surface area, and polar surface area analyses all confirmed that these six ligands kept the tunnel compact and the catalytic electrostatic environment stable, both in the wild-type enzyme and in the more permissive Ala218Trp and Ala221Trp mutant backgrounds.

Toxicity predictions from the ProTox 3.0 server, which applies machine-learning models across 61 toxicity endpoints, narrowed the field further. Compounds A3, D6, E6, and F4 fell into toxicity class 4, with predicted oral LD50 values between 1000 and 1502 milligrams per kilogram and flagged respiratory, immunotoxic, and neurotoxic endpoints. Compounds B4, a methyl 3-methoxythiophene derivative bearing a fluorinated benzamido group, and C2, a thioureido-substituted compound, landed in the more favorable class 5, with predicted LD50 values of 4000 and 4500 milligrams per kilogram respectively, though mild hepatotoxicity risk means neither can be considered non-toxic pending experimental validation. The authors are careful to note that computational binding stability does not guarantee whole-cell target engagement, and that efflux pumps may limit intracellular concentrations. Future work, they argue, should include biochemical PlaF inhibition assays, lipidomic confirmation that glycerophospholipid remodeling is disrupted, complementation studies with the delta-plaF mutant to verify target engagement, and in vivo infection models. If those steps succeed, B4 and C2 could seed a genuinely novel class of anti-virulence agents—one that fights the superbug by stripping away its weapons rather than threatening its life, and in doing so may sidestep the resistance cycle that has defeated so many antibiotics before.

Subject of Research: Structure-based discovery of inhibitors targeting the phospholipase A1 virulence factor of Pseudomonas aeruginosa

Article Title: Structure-based discovery of phospholipase A1 (PlaF) inhibitors in Pseudomonas aeruginosa using integrated in silico and experimental approaches

Article References: Vemula, D., Gulipalli, K. C., Bodige, S., Seelam, N., & Bhandari, V. (2026). Structure-based discovery of phospholipase A1 (PlaF) inhibitors in Pseudomonas aeruginosa using integrated in silico and experimental approaches. Discover Chemistry, 3(1), Article 457. https://doi.org/10.1007/s44371-026-00802-6

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00802-6

Keywords: Pseudomonas aeruginosa, phospholipase A1, PlaF, anti-virulence, antimicrobial resistance, molecular docking, molecular dynamics, virtual screening, drug discovery, MM/GBSA, virulence factor, computer-aided drug design

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

The study, published in Discover Chemistry, screened the Pan-African Natural Products Library, known as p-ANAPL, a curated collection of compounds derived from African medicinal plants. Rather than relying on docking scores alone, the researchers built a multi-stage pipeline that begins with ADMET filtering, which predicts how a molecule is absorbed, distributed, metabolized, excreted, and whether it poses toxicity risks. This approach acknowledges a hard truth in drug discovery: a compound that binds tightly but fails Lipinski’s Rule of Five, or blocks the hERG potassium channel critical for heart rhythm, is a dead end no matter how impressive its binding energy appears on paper.

The target protein was murine COX-2, taken from the Protein Data Bank under identifier 4PH9 at a remarkably high resolution of 1.81 angstroms. Crucially, this crystal structure contained ibuprofen bound in the active site, giving the team a known answer to validate their docking protocol. When they re-docked ibuprofen and compared the result to the original crystallographic pose, the heavy-atom root-mean-square deviation came out at just 0.85 angstroms, well below the commonly accepted 2.0 angstrom threshold. This gave the researchers confidence that their computational setup was faithfully reproducing the physics of drug binding in the COX-2 channel.

With the protocol validated, all 520 compounds in the p-ANAPL library were screened using a hierarchical approach: high-throughput virtual screening first, then standard precision docking to refine the top 33 candidates, and finally extra precision docking on the best five. Two molecules rose above the rest: 152CSB2 and Thix6g, both of which outscored ibuprofen in the extra precision ranking. The researchers emphasized that selection was not based on docking scores alone but on a combination of binding scores, interaction patterns with key residues, and ADMET acceptability, a more holistic criterion that reduces the risk of false positives.

Both lead compounds anchored themselves in the NSAID-binding channel through contacts with Arg-120 and Tyr-355, the catalytic gatekeepers that normally admit arachidonic acid. 152CSB2 formed three hydrogen bonds, including donor interactions with Arg-121 and Tyr-356 plus an acceptor bond with Met-523, along with a salt bridge to the guanidinium group of Arg-120, mirroring the classic binding mode of competitive NSAIDs. Thix6g, meanwhile, relied on two hydrogen bonds and a battery of aromatic interactions, including stacked pi-pi contacts with Tyr-356 and pi-amide contacts with Gly-527, suggesting a different but equally stabilizing strategy for occupying the pocket.

To confirm these poses were not fleeting artifacts of a static snapshot, the team ran 200-nanosecond molecular dynamics simulations using the Desmond package. The protein backbone of both complexes remained stable, with RMSD values hovering between 2.8 and 3.2 angstroms for 152CSB2 and around 3.0 to 3.2 angstroms for Thix6g. The ligand RMSD for Thix6g stayed remarkably close to its initial binding mode throughout the simulation, while 152CSB2 showed an initial adjustment period before settling into a stable configuration. Hydrophobic contacts between 152CSB2 and residues Tyr-356, Val-524, and Tyr-386 persisted for 32, 62, and 88 percent of the simulation time respectively, providing a dynamic picture of sustained binding that no single docking frame could capture.

Binding free energy calculations using the MM-GBSA method, performed on 400 equally spaced frames from the final 100 nanoseconds of trajectory, delivered the study’s most striking result. 152CSB2 posted a binding free energy of minus 32.12 kilocalories per mole, while Thix6g managed only minus 0.29 kilocalories per mole, a dramatic gulf that reflects fundamental differences in how the two molecules fit their target. The van der Waals contribution for 152CSB2 reached minus 16.97 kilocalories per mole against just minus 6.30 for Thix6g, pointing to superior steric complementarity. Thix6g was further penalized by high ligand strain energy and unfavorable desolvation costs, meaning the molecule had to contort itself into a less natural shape to squeeze into the active site.

The researchers then deployed density functional theory calculations at the B3LYP/6-311++G(d,p) level to probe the electronic personalities of the lead compounds. Both 152CSB2 and Thix6g showed lower ionization potentials than ibuprofen, meaning they donate electron density more readily, a property that could enhance their interactions with electron-poor residues in the COX-2 pocket. 152CSB2 also exhibited the smallest HOMO-LUMO gap at 0.28 atomic units, the highest softness, and the largest electrophilicity index, collectively marking it as the most electronically reactive of the three molecules. The frontier molecular orbital distribution for 152CSB2 spread across an extended aromatic system with a conjugated ethylenic bond, whereas Thix6g’s orbitals localized around oxygen-bearing phenyl groups, hinting at distinct mechanisms of electronic engagement with the protein.

Despite the promise, the authors are careful to frame these findings as computational hypotheses rather than proven drug candidates. MM-GBSA values are semi-quantitative ranking estimates, not experimentally measured affinities, and predicted ADMET profiles must be validated in the laboratory before any lead optimization can begin. The team also notes that the hierarchical HTVS-to-SP-to-XP screening workflow, though perhaps computationally unnecessary for a library of only 520 compounds, was retained as a scalable demonstration that can be applied to much larger African natural product collections in the future.

Nevertheless, the study represents a meaningful step toward harnessing Africa’s largely untapped botanical diversity for modern drug discovery. By combining rigorous structural biology, molecular dynamics, quantum chemistry, and pharmacokinetic filtering into a single pipeline, the researchers have shown that natural compounds from the continent’s medicinal plants can compete with, and in some respects outperform, blockbuster synthetic drugs like ibuprofen at the molecular level. 152CSB2, with its combination of stable binding, favorable ADMET characteristics, and distinctive electronic profile, now stands as the most compelling candidate for the in vitro and in vivo studies that will determine whether a digital lead can become a real medicine for inflammatory disease.

Subject of Research: In silico screening of the Pan-African Natural Products Library for selective COX-2 inhibitors

Article Title: In silico evaluation of p-ANAPL library against inhibiting the cognate isoform cyclooxygenase Cox-2

Article References: Gawoua Teumen, D., Amana, B. A., Eyia Andiga, L. G., Moto Ongagna, J., Amoa, P. P., Govender, K. K., & Bikele Mama, D. (2026). In silico evaluation of p-ANAPL library against inhibiting the cognate isoform cyclooxygenase Cox-2. Discover Chemistry, 3(1), Article 478. https://doi.org/10.1007/s44371-026-00924-x

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00924-x

Keywords: COX-2, p-ANAPL, molecular docking, ADMET, molecular dynamics, MM-GBSA, DFT, natural products, virtual screening, anti-inflammatory, drug discovery, inflammation

The starting point for the study, published in The Journal of Antibiotics, is waldiomycin, an antibiotic originally isolated from a Streptomyces actinomycete. Waldiomycin is a striking molecule: a set of angucyclic rings bearing a naphthoquinone scaffold, connected through a tetraene linker to a dioxolan ring. Earlier nuclear magnetic resonance work had suggested that the angucyclic portion of the compound wedges itself into the H-box, a short stretch of amino acids conserved across bacterial histidine kinases, and thereby suppresses the autophosphorylation reaction that the catalytic ATP-binding domain depends on. Crucially, the H-box sequence is specific to histidine kinases and does not appear in the serine/threonine or tyrosine kinases of human cells, which makes it a potentially selective point of attack against dangerous pathogens, including the ESKAPE organisms that dominate hospital-acquired infections.

Previous attempts to drug this region have produced a long list of chemotypes, including trityles, benzoxazines, salicylanilides, triazoles, benzimidazoles, benzoxazoles, thiazolidiones, thiophenes, and adenine derivatives. None has advanced to clinical testing. The problem, in part, is that most medicinal chemistry campaigns against histidine kinases have started from structures that resemble the natural ligand, limiting the chemical space explored. The new work, led by Teruhiko Ishikawa, Yoko Eguchi, Toshihide Okajima, Masayuki Igarashi, and Ryutaro Utsumi, set out to break that pattern by using a screening method that deliberately ignores the three-dimensional structure of the protein-ligand complex altogether.

The method, called amino acid mapping, was proposed by Kyosuke Tsumura and colleagues. Instead of modeling how a compound sits inside a binding pocket, amino acid mapping asks a simpler question: which amino acid residues tend to cluster around the compound in aqueous solution? The answer is encoded as a spatial probability distribution of individual amino acid residues surrounding the molecule, calculated from molecular dynamics simulations and independent of any explicit protein structure. Two compounds with similar descriptors are likely to be recognized by similar binding environments, even if their chemical scaffolds look nothing alike. This makes the approach a form of scaffold hopping: it can carry the biological activity of a known ligand across to chemically distant molecules without requiring a crystal structure of the target.

To apply the method, the team first computed a high-precision reference descriptor for the naphthoquinone moiety of waldiomycin, the fragment that directly contacts the H-box. One hundred low-precision conformations were generated with Open Babel and then refined using density functional theory calculations with the B3LYP functional, a 6-31G* basis set, and a polarizable continuum solvation model in Gaussian16, yielding the most stable conformation. The researchers then compared this reference against a commercially available library of 11,973,825 compounds, using cosine similarity between descriptors as the measure of resemblance. Because a full comparison of millions of compounds is computationally punishing, the team applied a stepwise filter: compounds were first screened against a phenylalanine-only descriptor, then against descriptors for charged residue types, and only survivors of both stages underwent the complete 27-residue calculation. A similarity threshold of 0.75, chosen on the basis of earlier screening campaigns in which roughly one in ten compounds above that value showed activity comparable to the reference ligand, narrowed the field to 714 candidate molecules.

From those 714 hits, the team extracted rigid substructures, defined as condensed ring systems together with adjacent atoms connected through double or higher-order bonds, using an RDKit-based Python workflow. The analysis yielded 227 distinct scaffolds. After excluding frameworks smaller than a two-ring system, the eleven most frequent scaffolds were shortlisted, and the team settled on 9-hydroxy-4H-pyrido[1,2-a]pyrimidin-4-one, a scaffold ranked ninth by frequency but chosen for its novelty as an H-box inhibitor and its synthetic accessibility. Fifteen derivatives were synthesized through condensation reactions in polyphosphoric acid, nitration and reduction sequences, and a newly developed magnesium(II) triflate-mediated coupling, producing compounds designated FF-053 through FF-081.

The biological results were encouraging. When the fifteen compounds were tested at 100 micrograms per milliliter against the cytoplasmic regions of three histidine kinases, Bacillus subtilis WalK, Streptococcus mutans VicK, and Escherichia coli EnvZ, several showed more than 30 percent inhibition of autophosphorylation and were advanced to full dose-response analysis. Three derivatives, FF-054, FF-055, and FF-060, emerged as the standouts, with IC50 values ranging from 4.39 to 259 micromolar. Against WalK, none of the new compounds surpassed waldiomycin itself, which inhibits at 13.1 micromolar. But against VicK, the story reversed dramatically: FF-055 inhibited at 6.92 micromolar and FF-060 at 4.39 micromolar, both considerably more potent than waldiomycin’s 25.8 micromolar. The authors suggest that structural differences between the dimeric forms of WalK and VicK, which remain incompletely characterized, may explain the enhanced affinity of the new compounds for VicK.

The mechanistic evidence is where the study becomes particularly compelling. When the conserved H-box serine at position 242 of EnvZ, adjacent to the autophosphorylation site H243, was mutated to alanine, the potency of all three lead compounds dropped three- to six-fold, while a T247A mutation had no such effect. This pattern contrasts with waldiomycin, which loses potency against both mutants, and suggests that the new compounds bind on the S242 side of the phosphorylated histidine rather than the T247 side favored by the natural product. Nuclear magnetic resonance experiments with the isolated DHp domain of EnvZ, residues 223 to 289, confirmed direct physical interaction: addition of FF-054 perturbed the chemical shifts of V241, D244, and R246 within the H-box, while FF-060 produced dose-dependent perturbations at S242, R246, and T247. Affinity selection mass spectrometry added a further twist. At 100 micromolar, FF-060 increased the amount of waldiomycin bound to VicK from roughly 50 to 120 nanomolar, mirroring the behavior of previously described Type B H-box inhibitors, which bind a distinct site and allosterically enhance waldiomycin binding to both H-boxes of the dimeric kinase.

Perhaps the most elegant demonstration came from converting the descriptors themselves into a map. The team computed a ten-by-ten similarity matrix among the naphthoquinone reference, the three new pyridopyrimidinones, and six previously synthesized naphthoquinone-derived inhibitors, then applied classical multidimensional scaling to visualize the relationships in two dimensions. The compounds separated cleanly into two clusters that corresponded exactly to their experimentally determined modes of action: Type A inhibitors, which compete with waldiomycin for the same site, grouped with the natural product, while Type B inhibitors, including all three new FF compounds, grouped on the opposite side of the map. In other words, the amino acid mapping descriptor, computed without any knowledge of the protein, predicted which compounds share a binding site.

The implications extend beyond this single target class. The study represents the first successful application of amino acid mapping-based screening to identify histidine kinase inhibitors targeting the H-box, and it demonstrates that a descriptor derived from a natural product can be used to mine a library of nearly twelve million compounds for chemically unrelated hits with genuine biochemical activity. Because the method requires no structure of the protein-ligand complex, it could open doors against targets that have resisted traditional structure-based design, a category that includes many membrane-associated and dimeric signaling proteins. The authors caution that future work will be needed to determine whether the descriptor framework can be used prospectively to rationally select inhibitors with specific functional properties. But for a field in which no histidine kinase inhibitor has yet reached clinical trials, the arrival of three potent, structurally novel lead compounds, together with a computational tool that maps their binding modes, is a meaningful step toward antibiotics that disarm bacterial signaling rather than simply poisoning the cell.

Subject of Research: Amino acid mapping-based virtual screening for novel histidine kinase inhibitors targeting the bacterial H-box region

Article Title: Amino acid mapping–guided scaffold selection and hit identification of histidine kinase inhibitors from a virtual library of over 12 million compounds

Article References: Ishikawa, T., Eguchi, Y., Okajima, T., Igarashi, M., Ohira, S., Tsumura, K., Sakurai, K., Ishizaki, Y., Hayashi, C., Akter, S., Nakayama, J., Kohayakawa, C., Tani, A., Sawa, N., Katsumoto, T., Matsumoto, F., Haruta, J.-I., & Utsumi, R. (2026). Amino acid mapping–guided scaffold selection and hit identification of histidine kinase inhibitors from a virtual library of over 12 million compounds. The Journal of Antibiotics. https://doi.org/10.1038/s41429-026-00953-9

Image Credits: AI Generated

DOI: 10.1038/s41429-026-00953-9

Keywords: histidine kinase, amino acid mapping, virtual screening, waldiomycin, two-component system, antibiotic resistance, H-box, scaffold hopping, molecular dynamics, NMR, pyridopyrimidinone, drug discovery

The logic behind the study begins with the pharmacology of the starting material. Isoniazid is a prodrug that must be activated by the bacterial KatG enzyme before it can inhibit enoyl-acyl carrier protein reductase, or InhA, the enzyme responsible for building the mycolic acids that form the waxy protective coat of the tuberculosis bacillus. Mutations in KatG are a principal route by which the bacterium escapes the drug. Schiff bases, compounds defined by an azomethine linkage of the form C=N, have long attracted medicinal chemists because they are easy to synthesize, interact strongly with biological targets, and display a wide range of pharmacological activities. By attaching substituted aromatic aldehydes to the hydrazide portion of isoniazid, the researchers hoped to create molecules that could engage InhA directly and retain activity even where the classical activation pathway fails.

The team designed a focused virtual library of 30 novel isoniazid-based Schiff base derivatives by condensing isoniazid with structurally diverse substituted aromatic aldehydes. The aldehydes were chosen to span both electron-withdrawing and electron-donating substituents in different positional orientations, allowing a preliminary structure-activity relationship assessment of how these electronic changes influence lipophilicity and predicted interactions with the InhA active site. Structures were drawn in ChemDraw, converted into three-dimensional conformations, and geometry-optimized with the MMFF94 force field. Before any synthesis was attempted, the entire library was subjected to a sequential computational funnel designed to eliminate weak candidates early and concentrate laboratory effort on the most promising molecules.

That funnel is the methodological heart of the study. First, drug-likeness and pharmacokinetic properties were estimated with the SwissADME and pkCSM web servers, evaluating molecular weight, lipophilicity as LogP, hydrogen bond donors and acceptors, and topological polar surface area against Lipinski’s rule of five. Of the 30 designed compounds, 13 passed this filter and advanced. Next, toxicity prediction using the ProTox web server trimmed the field to 9 compounds with acceptable safety profiles. The Prediction of Activity Spectra for Substances, or PASS, analysis then estimated the probability of antitubercular activity, and 5 compounds emerged with favorable predicted activity. This progressive reduction, from 30 to 13 to 9 to 5, illustrates how an integrated in silico workflow can spare chemists the expense of synthesizing and testing compounds that are unlikely to survive downstream development.

The five survivors, designated SLB-1, SLB-3, SLB-5, SLB-7, and SLB-10, were then docked into the active site of InhA using AutoDock Vina. The crystal structure of the enzyme from M. tuberculosis, retrieved from the Protein Data Bank under accession 6EP8, was prepared by removing water molecules and co-crystallized ligands, adding polar hydrogens, and assigning Kollman charges. A 20 by 20 by 20 angstrom grid box centered on the active site defined the search space, and identical docking parameters were applied to all ligands, the reference drug isoniazid, and the co-crystallized ligand to ensure a fair comparison. The five Schiff base derivatives achieved binding affinities ranging from minus 8.7 to minus 9.5 kcal per mole, substantially more favorable than the minus 5.8 kcal per mole score recorded for isoniazid under the same conditions.

SLB-5, subsequently identified as N’,N”’-((5-nitro-1,3-phenylene)bis(methaneylylidene))di(isonicotinohydrazide), topped the ranking with a binding affinity of minus 9.5 kcal per mole. Interaction analysis revealed hydrogen bonds, hydrophobic contacts, and pi-pi stacking with key active-site residues including SER94, LYS165, and PHE149. The docking score also compared favorably with several known InhA inhibitors reported in the literature, including luteolin and piperine at minus 9.1 kcal per mole and tiliacorinine at minus 8.3 kcal per mole. A database and literature search across PubChem, Reaxys, and related resources found no previously reported identical structure, supporting the novelty of the compound and justifying its selection for synthesis. The authors are careful to note, however, that molecular dynamics simulations and RMSD-based redocking validation were not performed, leaving formal computational validation of the predicted binding modes for future work.

Synthesis of SLB-5 followed a classic Schiff base condensation. A substituted nitrobenzene-1,3-dicarbaldehyde was dissolved in ethanol, and a warm ethanolic solution of isoniazid was added dropwise with a few drops of glacial acetic acid as catalyst. The mixture was refluxed at 70 to 80 degrees Celsius for three to four hours, with reaction progress tracked by thin-layer chromatography. Cooling the reaction mixture produced a precipitate that was filtered, washed with cold ethanol, and dried. Structural characterization then confirmed the transformation: the FT-IR spectrum displayed a prominent azomethine C=N stretching band in the 1600 to 1650 per centimeter region alongside nitro group absorptions near 1550 and 1350 per centimeter, while the disappearance of the aldehydic C=O band confirmed consumption of the starting aldehyde.

Proton nuclear magnetic resonance spectroscopy provided further confirmation. A singlet at 8.80 parts per million corresponded to the azomethine proton, the signature of successful Schiff base formation, and a downfield singlet at 12.17 parts per million was assigned to the amide NH of the hydrazide moiety. Aromatic and pyridyl protons appeared between 7.59 and 8.68 parts per million, their deshielding consistent with the electron-withdrawing nitro and azomethine groups. Crucially, the absence of aldehydic proton signals around 9.5 to 10.0 parts per million verified complete conversion. High-resolution mass spectrometry sealed the case, showing a molecular ion at m/z 417.1722 and a characteristic fragment at m/z 371.1828 corresponding to loss of the nitro group, along with a stable base peak at m/z 285.1381 arising from cleavage of one pyridine carbohydrazide unit and the azomethine linkage.

The decisive test came in the biology. Using the Alamar Blue assay against M. tuberculosis H37Rv, SLB-5 demonstrated concentration-dependent growth inhibition, reaching 17.68, 35.48, 49.47, 60.16, and 75.14 percent inhibition at concentrations of 20, 40, 60, 80, and 100 micrograms per milliliter respectively. Isoniazid tested under identical conditions produced 16.27, 25.85, 43.95, 54.94, and 65.63 percent inhibition across the same range. At every concentration, the Schiff base derivative outperformed the standard drug, and at the highest dose the margin was nearly ten percentage points. Although complete inhibition was not achieved, the consistent superiority over a first-line agent suggests that Schiff base modification can genuinely enhance the antitubercular potential of the isoniazid scaffold rather than merely decorate it computationally.

The authors are appropriately measured about what these results do and do not prove. Enzyme inhibition assays and mechanistic studies are still needed to experimentally confirm InhA as the molecular target of SLB-5, since the docking evidence, however encouraging, remains predictive. Expanded testing against drug-sensitive and drug-resistant clinical strains, additional structural characterization by carbon-13 NMR, synthesis and evaluation of other high-ranking library members, and eventually in vivo studies of pharmacokinetics, bioavailability, and safety all lie ahead. Yet the study stands as a tidy demonstration of a workflow that many laboratories are now embracing: design broadly, filter computationally through drug-likeness, ADMET, activity prediction, and docking, and synthesize only the compound that survives every gate. In an era when multidrug-resistant and extensively drug-resistant tuberculosis continue to spread, that kind of efficiency is not just elegant chemistry; it is a practical strategy for getting new antitubercular candidates to the bench, and eventually perhaps to the clinic, faster than the bacterium can adapt.

Subject of Research: Computer-aided design and biological evaluation of isoniazid-based Schiff base derivatives as antitubercular agents targeting InhA

Article Title: Design and in silico screening of novel isoniazid-based Schiff base derivatives followed by synthesis and biological evaluation of the lead compound against Mycobacterium tuberculosis H37Rv

Article References: Bhuse, S. L., Patil, P. M., & Durbule, R. R. (2026). Design and in silico screening of novel isoniazid-based Schiff base derivatives followed by synthesis and biological evaluation of the lead compound against Mycobacterium tuberculosis H37Rv. Discover Chemistry, 3(1), Article 516. https://doi.org/10.1007/s44371-026-00909-w

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00909-w

Keywords: tuberculosis, isoniazid, Schiff bases, molecular docking, InhA, drug resistance, ADMET, virtual screening, medicinal chemistry, H37Rv, Alamar Blue assay, lead compound