The study, published as a Perspective in Frontiers in Drug Discovery by Elier E. Abreu-Martínez, David Ramírez-Palma, Gabriel Merino, and Karina Martinez-Mayorga, takes a well-established idea from medicinal chemistry and asks whether it applies far beyond biology. In structure–activity relationship studies, researchers have documented so-called activity cliffs: pairs or groups of compounds that are structurally almost identical yet differ enormously in biological activity. These cliffs are not rare curiosities. Analyses of the ChEMBL repository covering 27,610 bioactive compounds tested against 414 targets showed that activity cliffs account for roughly five percent of molecular pairs satisfying common structural similarity criteria, and because individual compounds can participate in many pairs, between 22 and 34 percent of compounds are involved in at least one cliff. The authors now argue that an analogous phenomenon, which they call property cliffs, exists for physicochemical properties, and that it deserves independent conceptual status rather than being treated as a loose extension of the activity-cliff idea.

The technical case begins with an apparent paradox. Many molecular properties behave in an approximately additive fashion with respect to chemical structure, which is precisely why fragment-based models work so well. The Ghose–Crippen model and the Moriguchi method estimate logP, the octanol/water partition coefficient, as the sum of local contributions from specific structural fragments. Topological polar surface area can likewise be approximated by adding up the contributions of polar fragments. If properties are essentially additive, one might expect structure–property landscapes to be smooth, with gradual changes responding to gradual structural modifications, leaving no room for cliffs at all. The authors contend that this conclusion is not justified, because protonation state, electronic structure, conformational preferences, intermolecular interactions, and crystal packing can all produce large property variations from small structural perturbations.

The evidence spans classical chemistry and cutting-edge materials. A textbook example is the chlorine oxyacid series at 25 degrees Celsius: hypochlorous acid has a pKa of about 7.4, chlorous acid about 1.94, and chloric acid about minus 2.7. Each added oxygen atom enhances charge delocalization in the conjugate base, driving acidity down precipitously. In drug-like molecules, the effect can be just as striking. In propylpiperidine-derived antimigraine agents, introducing a single fluorine atom lowers the pKa by approximately one unit relative to the parent compound, and a second fluorine produces another substantial decrease. These stereoelectronic effects alter molecular basicity enough to influence receptor affinity and oral bioavailability, meaning a property cliff can cascade directly into pharmacological consequences.

Lipophilicity, often assumed to be strictly additive, also shows cliff-like behavior. Phenol has a logP of approximately 1.5, while p-methylphenol sits at about 2.0 and p-tert-butylphenol at about 3.3. Replacing the phenol hydrogen with a tert-butyl group therefore increases lipophilicity by roughly 1.8 logP units, corresponding to nearly two orders of magnitude in the actual octanol/water partition coefficient. This reflects the combined influence of hydrophobic effects, molecular volume, and solvent reorganization, showing that logP is not always the simple sum of its parts.

Chemical reactivity provides perhaps the most extreme examples. In computational studies of diarylamine formation through a Smiles rearrangement, researchers identified a base-catalyzed concerted 3-exo-trig pathway and found that small changes in the substitution pattern of the S-aryl ring produce dramatic changes in reactivity. Progressive introduction of nitro groups decreases the activation barrier from 29.0 kcal per mole in the monosubstituted system to 21.0 and 18.6 kcal per mole in the di- and trisubstituted derivatives, causing the rate constants at 298.15 K to climb from 2.0 times ten to the minus nine per second to 2.4 times ten to the minus three and then 1.9 times ten to the minus one per second. Trifluoromethanesulfonyl substituents are even more powerful, lowering barriers to as little as 9.9 and 8.4 kcal per mole and raising calculated rate constants by more than twelve orders of magnitude relative to the parent system. Mechanistically, these trends can be rationalized through well-established electronic effects, yet viewed within a structure–property framework they appear as an extreme discontinuity in the reactivity landscape, exhibiting the defining characteristics of a property cliff.

The phenomenon extends into the solid state, where subtle structural perturbations trigger abrupt changes in electronic properties. In blue phosphorene bilayers, small variations in stacking arrangement or interlayer distance can induce a transition between semiconducting and metallic behavior. Most reported stacking modes are semiconducting, but the A1B-minus-one arrangement is metallic and is also the most energetically stable configuration. Remarkably, increasing the interlayer distance within that same arrangement from 3.01 to 4.93 angstroms restores semiconducting behavior. Covalent organic frameworks show analogous sensitivity: in Tp-BTD frameworks, switching the interlayer stacking from eclipsed AA to offset AB to ABC polymorphs produces substantial functional differences without altering chemical composition. In the photoinduced oxidation of amines to imines, the ABC polymorph achieves 72 percent conversion of amines to imines while the AA and AB structures manage only about 20 percent, differences arising from stacking-dependent pore size, relative stability, and tautomeric equilibrium.

Antiaromaticity and electronic transport supply a final molecular-scale illustration. HDHA and ODHA are closely related 12-pi-electron macrocycles that formally satisfy Hückel’s 4n rule and are therefore antiaromatic, yet they differ markedly in the strength of their paratropic ring currents. HDHA sustains a paratropic current of minus 15.6 nanoamperes per tesla, while ODHA reaches minus 95 nanoamperes per tesla, an approximately sixfold increase in antiaromatic character. This enhancement is accompanied by a substantial change in electronic transport: near the Fermi level, HDHA exhibits moderate transmission values of roughly 0.03 to 0.17, whereas ODHA reaches 0.66 to 0.76. Changes in electron delocalization and antiaromatic stabilization explain the chemistry, but within a structure–property framework the pair represents a pronounced discontinuity in which a modest structural modification produces disproportionately large changes in both magnetic and transport properties.

Beyond existence, the analysis draws two further distinctions that shape how property cliffs should be studied. First, the authors propose that activity cliffs are probably more prevalent than property cliffs, a hypothesis grounded in mechanism rather than statistics. Biological activity depends on conformational effects, desolvation processes, specific ligand–receptor interactions, active-site reorganization, alternative binding modes, and the coexistence of orthosteric and allosteric sites, all of which are intrinsically nonlinear and context dependent. Many physicochemical properties, by contrast, are governed by local structural contributions and behave more additively. Second, property cliffs tend to be more mechanistically interpretable. Activity cliffs frequently reflect the combined action of multiple interacting factors that are difficult to disentangle; in crystallographic analyses of protein–ligand complexes, one category accounting for roughly 13 to 23 percent of analyzed cliffs involves several simultaneous contributions with no dominant factor, and another category of about ten percent involves structurally similar ligands with nearly indistinguishable binding modes yet activity differences of at least two orders of magnitude, with no obvious structural explanation. Property cliffs, by contrast, usually originate from identifiable phenomena such as nonlinear electronic effects, steric and volume effects, interaction coupling, conformational changes, changes in dominant state, solvation effects, switches in the dominant reaction pathway, or supramolecular organization effects.

The implications reach well beyond conceptual tidiness. The authors argue that recognizing property cliffs as a distinct class of structure–property discontinuities could improve QSPR modeling, computational chemistry, materials science, and data-driven molecular design, especially as modern predictive models are increasingly applied to electronic, optical, catalytic, spectroscopic, and transport properties far beyond their traditional medicinal-chemistry role. Because the significance of a property change is inherently property dependent, universal numerical thresholds are unlikely to work; a change of one pKa unit, a tenfold variation in a rate constant, a large conductivity shift, or a semiconducting-to-metallic transition may each represent a discontinuity of practical significance on different scales. Instead, the authors advocate property-specific criteria, molecular similarity metrics, matched molecular pair analysis, error quantification, and applicability domain assessment as a more robust foundation for identifying discontinuities. They also caution that apparent cliffs can arise from measurement error, data-quality issues, limitations of the molecular representation, or extrapolation beyond a model’s applicability domain, so identifying a discontinuity and interpreting its origin are conceptually distinct steps. Systematic characterization of property cliffs, the authors conclude, may reveal new insights into molecular function and materials performance while exposing exactly where machine-learning models for molecular properties are most likely to fail.

Subject of Research: The existence and nature of property cliffs, abrupt physicochemical property changes caused by small structural modifications, in structure–property relationships

Article Title: Do property cliffs exist? A conceptual analysis of structural changes and molecular properties

Article References: Abreu-Martínez, E. E., Ramírez-Palma, D., Merino, G., & Martinez-Mayorga, K. (2026). Do property cliffs exist? A conceptual analysis of structural changes and molecular properties. Frontiers in Drug Discovery, 6, Article 1899616. https://doi.org/10.3389/fddsv.2026.1899616

Image Credits: AI Generated

DOI: 10.3389/fddsv.2026.1899616

Keywords: property cliffs, activity cliffs, QSAR, QSPR, physicochemical properties, cheminformatics, molecular machine learning, materials discovery, chemical space, property, cliffs, exist

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 urgency behind this work is difficult to overstate. The World Health Organization has warned that the world is effectively running out of antibiotics, listing the surge in antimicrobial resistance among the top ten threats to global public health. Multidrug-resistant bacteria, defined as those resistant in vitro to at least one agent in three or more antimicrobial categories, are emerging so quickly that existing drugs are losing their clinical value. Against that backdrop, the discovery of entirely new chemical scaffolds with measurable antibacterial activity and acceptable predicted safety is a notable step forward.

To measure activity, the researchers turned to a resazurin-based microdilution assay, a sensitive colorimetric technique performed in 96-well plates. Resazurin is a blue dye that living bacteria convert to a pink, reduced form as they metabolize; if a well stays blue after incubation, the bacteria in it have been inhibited. Each of the nine derivatives was serially diluted across the plate, and minimum inhibitory concentrations (MICs) were recorded alongside minimum bactericidal concentrations (MBCs), which capture the concentration needed to kill rather than merely stall the bacteria. The assay itself was validated against the standard antibiotic gentamicin, with results matching Clinical Laboratory Standards Institute and EUCAST reference values.

The headline result came from derivative 6, which achieved an MIC of just 0.0048 mg/mL against S. typhimurium—the lowest value in the entire series and, notably, lower than the MIC of gentamicin against the same organism. All nine derivatives showed broad-spectrum activity in the range of 0.0048 to 0.620 mg/mL, and derivatives 1, 4, 6, 8, and 9 were active against both Gram-negative species such as Escherichia coli, Salmonella typhimurium, and Klebsiella aerogenes and Gram-positive organisms including Bacillus cereus and Enterococcus faecalis. Statistical analysis using geometric means confirmed derivative 6 as the most active synthesized compound, followed by derivative 4, while derivative 7 ranked as the weakest.

One intriguing pattern emerged from the structure–activity data: potency did not track neatly with substituent chemistry. Derivatives sharing the same benzyl substituent at the R2 position, for example, varied widely in efficacy, and compounds with nearly identical R1 and R2 groups differed in activity. The authors attribute this to structural differences interacting with variations in bacterial cell wall architecture and pathogen-specific resistance mechanisms. Across all compound–organism pairs, the MBC was roughly five times the MIC, indicating that the molecules are primarily bacteriostatic at inhibitory concentrations and require a modest dose increase to deliver a killing blow.

To probe how these molecules might work at the molecular level, the team performed molecular docking against peptidoglycan glycosyltransferase, an enzyme central to bacterial cell wall assembly. Peptidoglycan forms the rigid backbone of the bacterial cell wall, and glycosyltransferase domains within penicillin-binding proteins polymerize glycan chains from lipid-linked precursors. Blocking this process causes osmotic instability and, ultimately, cell lysis—a mechanistically attractive target that many successful antibiotics already exploit. The docking protocol was validated by redocking the crystal structure’s native ligand, which reproduced the original binding pose and its key hydrogen bonds with residues including Glu83, Ser116, and Gln121.

When the synthetic derivatives were docked, derivative 6 again stood out, achieving a binding energy of −6.958 kcal/mol, the strongest among the series. Its benzene and pyridine rings, along with the indole heterocycle, packed into the binding pocket via hydrophobic interactions with Lys62, Tyr65, and Leu159, supplemented by a π–cation contact with Lys62 and π–π stacking with Tyr65. The nitrogen and oxygen atoms of the oxazepine ring are thought to facilitate hydrogen bonding within glycosyltransferase active sites, while related indole compounds have been shown to disrupt membrane potential, deplete ATP pools, interfere with exopolysaccharide production, and suppress biofilm formation—multiple complementary mechanisms wrapped into a single scaffold.

Potency means little if a drug candidate poisons the patient, so the team deployed a comprehensive in silico safety workup. The CLC-Pred platform screened all nine derivatives for cytotoxicity across a panel of 198 human cell lines, and none exceeded the 0.5 probability-of-activity threshold conventionally associated with likely toxicity. Mean activity probabilities ranged only from 0.219 to 0.253, and normal cell lines such as skin and lung fibroblasts showed significantly lower predicted activity (Pa = 0.151) than tumor-derived lines (Pa = 0.249), hinting at a selectivity margin in favor of healthy tissue. Quantitative structure–activity/toxicity modeling predicted every derivative to be inactive for cytotoxicity, mutagenicity, and carcinogenicity.

Pharmacokinetic predictions proved equally encouraging. SwissADME analysis showed that most derivatives are highly absorbed in the gastrointestinal tract, and derivative 5 emerged as the standout from a drug-likeness perspective, with a Log P of 2.50—comfortably within the lipophilicity window defined by Lipinski’s Rule of Five—and zero violations. Derivative 5 was therefore flagged as the safest, most balanced lead for future structural optimization, while derivative 6 remains the potency champion. Compared with gentamicin, derivative 6 still required roughly 3.7-fold higher concentrations by geometric-mean MIC, so there is room for medicinal chemists to close the gap.

As with all early-stage discovery work, the caveats matter: these safety and mechanistic conclusions rest on computational prediction and in vitro assays, and the authors emphasize that in vitro cytotoxicity validation must confirm the predicted margins before any move toward preclinical development. Still, the study delivers exactly what the antibiotic pipeline desperately needs—a validated new chemical class with demonstrable broad-spectrum activity, a plausible cell-wall-targeting mechanism, and a computational safety profile clean enough to justify further investment. Whether derivative 6 or its optimized descendants eventually reach the clinic, the oxazepino–indole hybrid scaffold has now earned its place on the list of structures worth watching in the fight against drug-resistant bacteria.

Subject of Research: Antibacterial activity, molecular docking, and in silico safety profiling of novel indole-fused oxazepine derivatives

Article Title: Antibacterial activity, molecular docking, and in silico safety profiling of novel 3,10-dihydro- 2H -1,3-oxazepino[7,6- b ]indole derivatives

Article References: Sammor, M. S., Abu-Halaweh, M. M., Al Abrouni, K., Chandrasekaran, B., Dahadha, A., & Khazaleh, N. (2026). Antibacterial activity, molecular docking, and in silico safety profiling of novel 3,10-dihydro-2H-1,3-oxazepino[7,6-b]indole derivatives. Pharmacia, 73, Article e199777. https://doi.org/10.3897/pharmacia.73.e199777

Image Credits: AI Generated

DOI: 10.3897/pharmacia.73.e199777

Keywords: antibiotic resistance, indole, oxazepine, minimum inhibitory concentration, molecular docking, peptidoglycan glycosyltransferase, QSAR, SwissADME, drug-likeness, Salmonella typhimurium, cytotoxicity prediction, drug discovery

The review’s central argument is that toxicity prediction is no longer a single-technique discipline but a layered pipeline in which every stage matters. Traditional safety assessment relies on in vitro assays and animal studies, methods the authors describe as resource-intensive, slow, and limited by interspecies physiological differences that can mask human-specific toxicity. Machine learning and deep learning models, by contrast, can extract complex nonlinear patterns from high-dimensional chemical and biological datasets, enabling rapid screening of large compound libraries during early discovery. This shift builds on decades of quantitative structure-activity relationship (QSAR) modeling, which links chemical structure to biological activity, but modern frameworks go further by learning molecular features directly from data rather than relying on hand-crafted rules.

At the foundation of any predictive model lies the question of how to represent a molecule for computation. The review catalogs the dominant approaches: molecular fingerprints such as MACCS structural keys and extended connectivity fingerprints (ECFPs), which encode substructures as bit strings; numerical descriptors calculated by tools like RDKit, Mordred, PaDEL, and the FDA-developed Mold2; and graph-based representations that treat atoms as nodes and bonds as edges. Graph representations have become especially important because they preserve molecular connectivity and allow graph neural networks (GNNs) to learn directly from structure. More recently, pre-trained chemical language models such as ChemBERTa, SMILES-BERT, MolBERT, and MoLFormer generate learned embeddings that capture richer contextual information than fixed fingerprints ever could.

The authors critically compare conventional machine learning algorithms, including random forests, support vector machines, decision trees, k-nearest neighbors, naive Bayes, XGBoost, and LightGBM, against deep learning architectures spanning deep neural networks, convolutional neural networks, GNNs, and transformer-based models. Their conclusion is deliberately unsentimental: no single architecture consistently wins across toxicity tasks. Conventional methods remain competitive on small to medium datasets with informative descriptors, while deep learning approaches benefit from larger datasets and richer representations. Performance differences reported across studies often reflect variations in dataset composition, endpoint definition, class distribution, and validation strategy rather than genuine algorithmic superiority.

One of the review’s most technically significant sections addresses validation rigor, an issue that quietly undermines many published benchmarks. Random train/test splitting can produce optimistic results when structurally similar compounds or close analogues appear in both sets, a problem compounded by data leakage when preprocessing, feature selection, or hyperparameter optimization occurs before the final split. Scaffold-based, cluster-based, temporal, and independent external validation provide far more honest assessments of generalization to novel chemical space. The authors also stress that accuracy alone is insufficient for imbalanced datasets, where toxic compounds are typically rare; they recommend complementary metrics including sensitivity, specificity, F1-score, balanced accuracy, Matthews correlation coefficient, ROC-AUC, and precision-recall AUC, with calibration considered when predicted probabilities inform risk assessment.

The review then organizes its evidence around five organ-specific toxicity domains, drawing on 40 primary studies published between 2021 and 2026. In respiratory toxicity, random forest models using PubChem fingerprints achieved 93.3% accuracy, while a LightGBM model for chemical lung toxicity reached 86.88% accuracy with a ROC-AUC of 0.931. In cardiotoxicity, transformer models using Morgan fingerprints hit a ROC-AUC of 0.93 for hERG channel interactions, and a multi-view GNN framework called CardiacTox achieved a ROC-AUC of 0.938 for CaV1.2 prediction. Nephrotoxicity models reached a ROC-AUC of 0.93 in consensus approaches, while hepatotoxicity studies reported ResNet18 deep neural networks achieving a test ROC-AUC of 0.958 for drug-induced liver injury. Neurotoxicity studies spanned blood-brain barrier permeability, neuronal cytotoxicity, and acetylcholinesterase inhibition, with hybrid deep learning models reaching ROC-AUC values up to 0.964.

These headline numbers, however, come with caveats the authors refuse to bury. Dataset sizes varied enormously, from 2,670 chemicals in a neurotoxicity study to just 83 breast cancer patients in a radiotherapy cardiotoxicity analysis, and KidneyTox_v1.0 relied on only 565 compounds. Class imbalance, uncertain negative labels, limited chemical-space coverage, and incomplete external validation recurred across all five domains. The authors emphasize that a higher ROC-AUC in one organ domain does not indicate a superior modeling approach, because biological endpoints, dataset characteristics, and prediction tasks differ fundamentally across systems. Hepatotoxicity studies typically target drug-induced liver injury, cardiotoxicity studies focus on specific molecular targets like hERG and CaV1.2, and neurotoxicity encompasses heterogeneous endpoints from blood-brain barrier permeability to mammalian neurotoxicity.

Interpretability receives equally critical treatment. Explainable artificial intelligence techniques such as SHAP, attention mechanisms, saliency methods, and structural-alert identification can reveal which molecular features drive a prediction, but the authors draw a sharp line between explaining a model and establishing biological causation. High SHAP values or atom-level attribution maps identify features associated with a prediction without proving those features cause the toxic effect, and attribution results can shift with molecular representation, correlated descriptors, and model architecture. True mechanistic interpretation requires independent evidence from experimental validation, target-interaction data, toxicogenomics, pathway analysis, or adverse outcome pathways. The review also highlights the applicability domain concept, which defines the region of chemical space where a model can be trusted; predictions for compounds far from training data may be unreliable even when internal performance metrics look impressive.

On the regulatory front, the authors argue that strong retrospective performance alone cannot establish suitability for real-world safety assessment. Regulatory confidence depends on independent external validation, clearly defined endpoints, transparent and reproducible workflows, applicability-domain assessment, uncertainty evaluation, and adequate documentation of input data, preprocessing, algorithms, and known limitations. The review catalogs major toxicity databases including SIDER, FAERS, ADReCS, Pneumotox, PubChem, DrugBank, ChEMBL, ICE, LINCS L1000, and LiverTox, noting that clinical adverse-event sources differ fundamentally from experimental toxicity resources and should not be treated as interchangeable. Data quality, endpoint harmonization, and duplicate removal emerge as prerequisites before any model development begins.

Looking forward, the review identifies active learning and federated learning as strategies to overcome limited labeled data, foundation models as a path to stronger molecular representation, and multimodal learning as a way to combine molecular graphs, SMILES strings, structural images, and spectroscopic information. The authors call for improved negative-data construction, assay and endpoint harmonization, causal and mechanistic modeling, and prospective validation to build tools that are reliable, interpretable, and generalizable enough for regulatory applications. Their overarching message is measured but firm: artificial intelligence has demonstrated competitive and sometimes improved predictive performance in toxicology, but it should not be considered inherently superior to conventional methods. The technology’s real value depends on disciplined data curation, rigorous validation, and honest acknowledgment of where predictions can and cannot be trusted.

Subject of Research: Artificial intelligence methods for predicting chemical and drug toxicity across organ-specific endpoints

Article Title: A review on advances in chemical compound toxicology prediction

Article References: Hagag, A., Elsayed, N. A., & Abdelbaky, I. (2026). A review on advances in chemical compound toxicology prediction. Frontiers in Bioinformatics, 6, Article 1974935. https://doi.org/10.3389/fbinf.2026.1974935

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1974935

Keywords: toxicity prediction, machine learning, deep learning, graph neural networks, QSAR, hepatotoxicity, cardiotoxicity, nephrotoxicity, neurotoxicity, explainable AI, drug safety, molecular fingerprints

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

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

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

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

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

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

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

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

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

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

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

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

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1964610

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

Mohammed Alsharafi and Yusuf Zeren of Yildiz Technical University, publishing in the journal Discover Chemistry, focused on three benzenoid systems: the hexabenzocoronene series, the hexa-cata-hexabenzocoronene series, and the dodeca-benzo-circumcoronene series. Each family is built by adding successive sixfold-symmetric layers of hexagonal cells to a central core, producing molecules whose carbon skeletons grow in a perfectly predictable way. The familiar molecule C42H18, hexabenzocoronene itself, is the second member of the first family, while the circumcoronene-type molecule C84H24, containing thirty-one fused benzene rings, is the second member of the third.

The study rests on a simple but powerful idea from chemical graph theory: represent each carbon atom as a vertex and each carbon-carbon bond as an edge, then compress the resulting network into numbers called topological indices. These numerical invariants have been a staple of quantitative structure-property and structure-activity research since the Zagreb indices were introduced by Gutman and Trinajstić in 1972, originally to probe the relationship between total pi-electron energy and molecular structure. The forgotten index followed in 2015, the hyper-Zagreb index in 2013, and the Y-index more recently, each capturing a different aspect of how atoms branch and connect.

What makes the new work distinctive is not the indices themselves, which are all established invariants, but the unified treatment. Alsharafi and Zeren partitioned every edge in each molecular graph according to the degrees of its two endpoint atoms. In a benzenoid hydrocarbon, degree-two vertices sit on the molecular boundary and carry a hydrogen atom, while degree-three vertices are fused carbons buried in the interior network. Edges joining two boundary atoms, one boundary and one interior atom, or two interior atoms therefore encode different aspects of the boundary-to-interior balance, and every index can be evaluated exactly by summing or multiplying over these three edge classes.

From that partition, the authors derived explicit quadratic formulas for the first and second Zagreb indices, the forgotten index, and the Y-index across all three families, alongside multiplicative versions, modified reciprocal versions, degree-weighted graph entropies, generating functions, and coindices that measure pairs of non-adjacent vertices. For the hexabenzocoronene family, for example, the first Zagreb index works out to 162s squared minus 222s plus 84, where s counts the layers, and the Y-index to 1458s squared minus 2238s plus 876. Similar expressions cover the other two families, giving chemists exact structural descriptors for any member of any series without ever drawing the molecule.

The comparative analysis delivers perhaps the most striking result. For every principal additive descriptor, the hexabenzocoronene family attains the minimum value among the three systems for all sizes, while the maximum flips depending on scale: the dodeca-benzo-circumcoronene family leads for small molecules, up to layer count six, but the hexa-cata-hexabenzocoronene family overtakes it from layer count seven onward. The proof is disarmingly clean, reducing to a handful of quadratic differences whose signs change at a single root between six and seven. In the asymptotic limit of very large molecules, the hexa-cata descriptors converge to exactly four-thirds of their hexabenzocoronene counterparts, while the circumcoronene-type values converge to parity.

That crossover has a physical reading. The hexa-cata construction adds more boundary atoms and more boundary-sensitive edges per layer than the circumcoronene annulus, so its descriptors, which weight fused interior connectivity most heavily, eventually dominate as the molecules grow. The multiplicative indices tell the same story through their logarithms, with the hexabenzocoronene family proven minimal and the ratios of the other families’ logarithms approaching the same four-thirds and one limits. These are exact mathematical statements, verified numerically for the first three members of each series.

The entropy calculations add an information-theoretic layer. Defined from the weighted edge distributions underlying each index, these quantities summarize how heterogeneous the bond environment is across the molecule. The authors are careful to stress that these are graph-information measures, not thermodynamic entropies, and that larger index values primarily reflect compact fused regions rather than any direct experimental property. That candor matters, because the temptation to over-interpret topological numbers is a perennial hazard in mathematical chemistry.

Why should anyone outside graph theory care? Hexabenzocoronene derivatives are serious business in materials research. They have been selectively functionalized, incorporated into conjugated copolymers for organic field-effect transistors and polymer solar cells, studied as nanographene model compounds for aromatic character, and investigated in stacked complexes with fullerene. Graphene-like polycyclic aromatic systems more broadly are prized for their electronic properties, and the boundary-to-interior distinction that drives these indices is structurally meaningful in exactly those systems. Exact formulas for how descriptors scale with molecular size could eventually help screen candidate structures computationally.

The authors are equally explicit about the limits of the present contribution. No property-prediction model is fitted here; the formulas are offered as candidate graph-theoretical inputs for future quantitative structure-property and structure-activity investigations, where their predictive value must be established against measured or quantum-chemical data through statistical validation. Even so, the work sets a high bar for rigor: a complete, closed-form, cross-family characterization of three chemically important molecular series, with every extremal claim proved rather than asserted. For a field increasingly reliant on machine-learning descriptors of molecular structure, having exact ground truth for entire homologous families is a quietly valuable resource.

Subject of Research: Degree-based topological indices and graph entropies of large benzenoid hydrocarbon systems

Article Title: Topological characterization of large benzenoid hydrocarbon systems and their chemical significance

Article References: Alsharafi, M., & Zeren, Y. (2026). Topological characterization of large benzenoid hydrocarbon systems and their chemical significance. Discover Chemistry, 3(1), Article 574. https://doi.org/10.1007/s44371-026-01023-7

Image Credits: AI Generated

DOI: 10.1007/s44371-026-01023-7

Keywords: chemical graph theory, topological indices, benzenoid hydrocarbons, hexabenzocoronene, polycyclic aromatic hydrocarbons, Zagreb indices, graph entropy, QSPR, QSAR, nanographene, molecular descriptors, circumcoronene

The review, conducted by Abir Hamze and Emran Alotaibi of Canadian University Dubai, is structured as a PRISMA-ScR-informed evidence map rather than a conventional narrative survey. The authors searched bibliographic databases, publisher platforms, Google Scholar and citation-chasing sources, identifying 134 corpus entries before duplicate removal. After screening, 126 unique records formed a broad map of AI applications to PFAS, spanning detection, environmental fate, treatment, exposure and toxicity. The team then applied a stricter, endpoint-based reclassification to isolate the toxicological core of the literature. Of 46 records initially coded as toxicity or human and ecological health, only 39 survived as genuine AI-PFAS toxicology studies, and detailed full-text extraction was possible for 35 of them. The distinction matters: many studies that mention PFAS and health in the same breath are actually modeling environmental occurrence or exposure classification, not biological hazard.

Within that core subset, the methodological landscape is strikingly heterogeneous. Molecular simulation combined with machine learning emerged as the family producing the strongest-validated examples, while QSAR, QSPR and read-across studies, the traditional workhorses of computational toxicology, often clustered at intermediate validation levels. Tree ensembles, boosted-tree algorithms, Bayesian probabilistic models, neural networks and graph-based approaches all appear in the literature, each suited to different tasks. The authors emphasize that no algorithm is inherently decision-ready: a random forest may serve tabular biomonitoring data well if externally validated, a graph neural network may excel at structure-based prediction when scaffold-split validation is reported, and Bayesian models are particularly attractive for sparse exposure data because they make uncertainty explicit rather than hiding it behind a single deterministic number.

One of the review’s most valuable contributions is its grounding of AI prediction in mechanistic toxicology. Drawing on recent specialist reviews, the authors identify several biological anchors that any credible PFAS model must respect. Reproductive toxicity work points to reactive oxygen species as a molecular initiating event that cascades through oxidative stress, mitochondrial dysfunction, steroidogenic disruption and thyroid-hormone changes, ultimately impairing gametogenesis and fertility. Transporter-focused research highlights serum albumin, solute carrier and ATP-binding cassette transporters, and nuclear receptors as critical but underdeveloped determinants of where PFAS go in the body. Early-life studies identify the placenta as a sensitive target that may mediate latent disease decades after exposure. Omics research, mixture toxicology and underrepresented endpoints such as neurotoxicity and ocular toxicity round out the picture. An AI model whose predictions contradict these established pathways, or whose feature-importance scores ignore chain length, functional group or protein-binding chemistry, should raise immediate red flags.

The clearest practical role for AI, the authors argue, is prioritization. It is simply impossible to test thousands of PFAS comprehensively in animal studies or in vitro batteries, so QSAR models, read-across frameworks, molecular descriptors and generative-AI workflows can triage which compounds and endpoints deserve earlier experimental attention. But the review insists this only works as a triage tool, never as a substitute for evidence. Before any such model is built, chemical data curation must come first: structure standardization, salt stripping, explicit handling of acids, salts, branched isomers and tautomers, duplicate removal, harmonization of chemical identifiers and careful treatment of missing values. Because the same PFAS substance may appear in databases as an acid, a salt, a precursor or a transformation product, inconsistent representation can silently distort descriptor values and inflate apparent model performance.

Validation design receives particularly sharp criticism. The authors show that random train-test splitting, the default in much of the machine learning literature, can seriously overstate performance for PFAS because structurally similar homologues, samples from the same laboratory or geographically clustered cohorts can end up in both training and test sets. More informative alternatives include scaffold splits, leave-family-out or leave-subclass-out validation, temporal and spatial validation, and independent-cohort testing for biomonitoring models. Applicability-domain assessment should likewise move beyond vague disclaimers, using tools such as Williams plots, distance-to-model measures, conformal prediction and ensemble disagreement to specify whether a prediction actually applies to long-chain legacy compounds, short-chain acids, ether PFAS, polymers or specific biological matrices. These requirements align directly with the OECD’s long-standing QSAR validation principles, which the authors argue apply with full force to modern deep learning and graph-based models.

The audit of the broader 126-record corpus quantifies how far the field still has to go. Using the full map as denominator, the authors found that 73 records, or 57.9 percent, showed no or limited external validation; 49 records, or 38.9 percent, lacked clear experimental or field validation; 41 records, or 32.5 percent, reported no formal uncertainty or applicability-domain analysis; and 28 records, or 22.2 percent, had no clearly available code. Bias compounds these gaps in specific directions: training data concentrate heavily on PFOA, PFOS and a few legacy analogues; available assays cover hepatotoxicity and endocrine activity far better than immune, developmental, neurological or reproductive endpoints; and monitoring data cluster in known hotspots and wealthy jurisdictions, leaving children, pregnant people, occupational groups, private-well users and under-monitored communities underrepresented.

On the exposure side, the review acknowledges genuine promise alongside real peril. Machine learning models that predict which adults carry high PFAS burdens, or that integrate biomonitoring with environmental data, can help target confirmatory sampling, drinking-water treatment, dietary guidance and public-health follow-up. Yet serum concentrations reflect a tangle of drinking water, diet, dust, occupation, geography, renal function, pregnancy and protein binding, and a model may classify high exposure accurately while learning proxies for monitoring intensity rather than true exposure determinants. Interpretable association studies linking PFAS to diabetes and cardiovascular-kidney-metabolic outcomes illustrate both the analytical potential and the danger of causal overinterpretation. Mixture context adds another layer: because humans encounter shifting PFAS cocktails rather than isolated compounds, the authors argue that regulatory-grade models must support cumulative assessment and propagate uncertainty, with Bayesian frameworks offering a natural fit.

To translate these findings into practice, the review proposes a tiered regulatory-readiness framework that scales evidence requirements to decision consequence. Exploratory screening can tolerate uncertainty as long as it triggers further testing. Prioritization demands fit-for-purpose validation, descriptor transparency and domain checks. Supporting evidence requires external or biological validation, mechanistic plausibility and reproducible workflows. Decision-critical uses, such as regulatory read-across, testing waivers, restrictions, cleanup priorities or public-health communication, demand independent validation, calibrated uncertainty, explicit applicability domains, audit trails, expert review and stakeholder transparency. The authors also propose a minimum reporting checklist covering data provenance, chemical identifiers, validation, uncertainty, interpretability and code availability, and they sketch a closed-loop workflow in which AI generates hypotheses, human-relevant new approach methodologies such as organoids, receptor assays and omics platforms test them, and results feed back to update models and shrink uncertainty.

The review’s bottom line is neither technophobic nor uncritically enthusiastic. AI can genuinely accelerate PFAS hazard identification, bioactivity prediction, toxicokinetic inference, protein-binding assessment and ecological screening at a pace conventional testing cannot match. But prediction should never be mistaken for proof: a docking result suggests binding without demonstrating harm, an omics signature shows association rather than mechanism, and a low predicted risk is not evidence of safety. The authors are explicit about what AI must never be used to do, including declaring a compound safe because it falls below a predicted threshold, waiving testing outside a model’s domain, or treating absent monitoring as absent exposure. With transparent reporting, mechanistic anchoring, equity-sensitive validation and human oversight, they conclude, artificial intelligence can make PFAS toxicology faster, broader and more trustworthy, supporting rather than replacing the measurement, biological validation and expert judgment on which environmental-health protection ultimately depends.

Subject of Research: Artificial intelligence applications in PFAS toxicology and environmental-health risk assessment

Article Title: Artificial intelligence for PFAS toxicology and risk assessment

Article References: Hamze, A., & Alotaibi, E. (2026). Artificial intelligence for PFAS toxicology and risk assessment. Discover Artificial Intelligence, 6(1), Article 1291. https://doi.org/10.1007/s44163-026-02266-0

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02266-0

Keywords: PFAS, artificial intelligence, machine learning, toxicology, QSAR, read-across, risk assessment, biomonitoring, adverse outcome pathways, uncertainty quantification, regulatory science, environmental health

The economics alone make a compelling case for the computational turn. According to figures cited in the review, bringing a single new active ingredient to market has historically required the synthesis and evaluation of roughly 159,000 compounds, at a cost approaching 286 million US dollars and an average development timeline of about 11.5 years from first synthesis to commercial launch. Meanwhile, the environmental bill keeps mounting: more than 99 percent of applied pesticides never reach their intended target, dispersing instead into air, soil, and water, where they disrupt pollinators and other beneficial organisms. Human health concerns add urgency, with epidemiological studies linking pesticide exposure to elevated risks of neurodegenerative diseases such as Parkinson’s and Alzheimer’s, particularly among agricultural workers. Against this backdrop, the review’s authors, led by Mohamed El Ammari, position computational methods as essential instruments for finding compounds that are simultaneously effective, selective, and environmentally benign.

At the heart of the review is molecular docking, a structure-based technique that predicts how a small molecule—a ligand—binds to a macromolecular target such as a protein. The method rests on the physics of molecular recognition: hydrogen bonds, ionic interactions, hydrophobic effects, and van der Waals forces jointly determine the stability of a ligand–protein complex. A docking workflow typically involves preparing three-dimensional structures of both receptor and ligand, modeling their interactions, and scoring the resulting complexes by estimated binding energy to identify the most favorable binding conformation. Docking can be rigid, semi-flexible, or flexible, each mode trading computational cost against accuracy. In insecticide research, the technique supports virtual screening of chemical libraries, investigation of molecular targets, analysis of resistance-associated mutations, and even reverse docking, in which researchers start from a compound and search for the proteins it might bind—an approach that can flag potential off-target interactions with toxicological consequences.

The review is careful to emphasize that docking is not a crystal ball. Scoring functions remain imperfect, receptor flexibility is hard to capture, and ligand protonation, solvation effects, and protein preparation all introduce uncertainty. A favorable docking score, the authors stress, should be read as a comparative computational indicator, not as a direct measurement of binding affinity or biological activity. The most convincing studies therefore embed docking within multi-layered validation schemes that include enzyme inhibition assays, organism-level toxicity and behavioral bioassays, and, where possible, molecular dynamics simulations and free-energy calculations. When those elements converge, candidate prioritization becomes markedly more reliable, separating genuinely actionable leads from what the authors call pose-driven artifacts.

The case studies assembled in the review span an impressive range of pests, compounds, and targets, and many of them pair computational predictions with hard experimental numbers. In one study, homology modeling and virtual screening identified sulfonamide leads targeting the vacuolar-type ATPase of the oriental armyworm Mythimna separata; synthesis of 71 derivatives yielded seven with strong insecticidal activity. Another team used structure-based virtual screening to find azo-aminopyrimidine inhibitors of the chitinase OfChi-h, with two compounds outperforming the commercial insecticide hexaflumuron against the diamondback moth and the European corn borer under the reported assay conditions. High-throughput screening against the glutamate-cysteine ligase catalytic subunit of the red flour beetle Tribolium castaneum produced a hit with an IC50 of 19.70 micromolar that caused 63.8 percent larval mortality in follow-up bioassays. These are not abstract predictions; they are molecules that kill insects in the laboratory.

Botanical chemistry features prominently throughout the synthesis, reflecting intense interest in plant-derived insecticides. Essential oils from garlic and mustard, rich in diallyl disulfide and allyl isothiocyanate, showed the strongest fumigant toxicity among tested oils against stored-grain pests, with docking revealing high-affinity binding of allyl isothiocyanate to arylalkylamine N-acetyltransferase and of 2-pentenenitrile to juvenile hormone esterase. Carvacrol, a monoterpene from oregano and thyme, displayed multi-target activity in Tribolium castaneum, binding acetylcholinesterase, glutathione S-transferase, and cytochrome P450 with predicted affinities exceeding those of chlorpyrifos, while biochemical assays confirmed enzyme inhibition and oxidative stress. Cymbopogon essential oils, clove oil, and Artemisia extracts all received similar docking-supported evaluations, with median lethal doses reported alongside binding energies. The review also includes instructive failures: garden-waste compost tea showed weak insecticidal activity against aphids despite docking analyses, a reminder that matrix effects and volatile loss can decouple computational predictions from field-like performance.

Endocrine targets emerge as one of the most productive arenas for structure-guided design. Docking of 25 non-azadirachtin neem limonoids to the ecdysone receptor ligand-binding domain of the cotton bollworm Helicoverpa armigera identified six high-affinity candidates, including nimbolide and azadirone, that outperformed the commercial insect growth regulator tebufenozide, with binding energies between −10.54 and −12.22 kilocalories per mole. Equally important, the same modeling logic explains selectivity: homology models of the ecdysone receptor in lacewings, beneficial insects used in biological control, revealed a restricted binding pocket that sterically clashes with tebufenozide, consistent with bioassays showing the compound is harmless to these predators. On the juvenile hormone axis, virtual screening identified phytochemical inhibitors of farnesyl diphosphate synthase, and geranylgeraniol was validated as an inhibitor of farnesol dehydrogenase, causing developmental defects and roughly 63 percent mortality in bollworm larvae. Together, these workflows illustrate a coherent path from target identification through modeling to experimental confirmation.

Resistance biology is another domain where in silico tools deliver explanatory and predictive value. In the silverleaf whitefly Bemisia tabaci, analysis of acetylcholinesterase gene polymorphisms across 30 field populations in Egypt and Pakistan, combined with homology modeling and docking, revealed mutations that alter the peripheral anionic site architecture and could reduce sensitivity to organophosphates and carbamates. In mosquitoes, RNA interference knockdown of eight overexpressed cytochrome P450 genes significantly reduced permethrin resistance, while docking rationalized the substrate scope underlying detoxification. Synergism studies point toward practical stewardship strategies: benzyl alcohol potentiated deltamethrin in houseflies by roughly an order of magnitude, with docking showing the two molecules binding complementary sites on acetylcholinesterase and the voltage-gated sodium channel, and quercetin synergized organophosphates in flour beetles through predicted inhibition of detoxifying P450 enzymes. Such insights directly inform insecticide rotation, mixture design, and the deployment of synergists.

Safety assessment is woven into the computational pipeline through ADMET prediction—modeling absorption, distribution, metabolism, excretion, and toxicity—which the review identifies as critical given that pharmacokinetic and toxicological failures account for up to 40 percent of drug development failures. Free web servers such as SwissADME and pkCSM, alongside commercial platforms, allow researchers to profile lipophilicity, solubility, and drug-likeness before any synthesis begins, reducing animal testing and avoiding late-stage failures from unforeseen toxicity. The honeybee voltage-gated sodium channel AmNaV1 offers a concrete example of selectivity-by-design: functional expression, electrophysiology, and docking of pyrethroids established a joint in vitro and in silico toolkit for appraising pollinator risk from current and next-generation pesticides. The authors advocate extending such counter-screening to aquatic and soil organisms as lead optimization proceeds.

The review closes with candid reflection on the field’s blind spots. A marked taxonomic bias concentrates computational innovation on a handful of model pests—lepidopterans, beetles, hemipterans, and dipterans—whose genomes, protein structures, and rearing systems are well characterized, while many regionally important pests, mites, and beneficial insects remain underrepresented, leaving homology models less reliable and docking outputs more uncertain. The authors call for standardized, transparent computational protocols, early counter-screens on pollinators, deconvolution of botanical mixtures into component target networks, and tight iterative cycles in which computational hypotheses are immediately stress-tested by discriminating experiments. Coupled with emerging machine-learning approaches, graph neural networks, and rapid free-energy methods, they argue, these practices could finally deliver mechanism-based, species-selective insecticides—provided that computation never drifts far from the laboratory bench that must ultimately validate it.

Subject of Research: Application of molecular docking and in silico computational methods to insecticide discovery and toxicological assessment

Article Title: Molecular docking and in silico approaches for insecticide discovery and toxicological assessment: A review

Article References: El Ammari, M., Bouzakraoui, S., Mrabti, I., Fahad, K., Boukita, H., Idrissi, A., Brhadda, N., & Ziri, R. (2026). Molecular docking and in silico approaches for insecticide discovery and toxicological assessment: A review. Results in Chemistry, 31, Article 103920. https://doi.org/10.1016/j.rechem.2026.103920

Image Credits: AI Generated

DOI: 10.1016/j.rechem.2026.103920

Keywords: molecular docking, insecticide discovery, virtual screening, in silico, pesticide resistance, biopesticides, ADMET prediction, acetylcholinesterase, ecdysone receptor, essential oils, integrated pest management, QSAR

The review begins with a problem that has quietly plagued the field for years: the assays themselves are chemically artificial. DPPH, the most widely used decolorization test, relies on a stable purple radical that is reduced to a yellow product, with the drop in absorbance at 517 nanometers serving as the readout. It is cheap and fast, but the radical is poorly soluble in water, forcing the use of methanol or ethanol mixtures that distort the thermodynamic behavior of hydrophilic antioxidants. Worse, the radical site sits buried behind bulky phenyl rings, so large antioxidants may simply fail to reach it, producing false negatives or artificially slowed kinetics that bear little resemblance to real radical clearance in living tissue.

ABTS improves on solubility, dissolving in both aqueous and organic media and thus accommodating hydrophilic and lipophilic antioxidants alike. But the blue-green radical cation must be pre-generated with potassium persulfate, a step that takes twelve to sixteen hours of stabilization, and the probe is entirely non-physiological: its reduction does not reproduce the reactivity, localization, or lifetime of biologically relevant species such as hydroxyl, superoxide, or nitric-oxide-derived radicals. Steric hindrance around the nitrogen-centered radical also restricts its reaction with polymeric phenols. FRAP, which measures the reduction of ferric iron complexed with tripyridyltriazine at low pH, is rapid and inexpensive but is not a radical assay at all. It gauges reducing capacity rather than radical scavenging, underestimating antioxidants that act through metal chelation while potentially overestimating polyphenols that undergo secondary autoxidation or release free ferrous iron that accelerates Fenton chemistry.

Even the hydrogen-atom-transfer assays, which are mechanistically closer to how chain-breaking antioxidants actually halt lipid peroxidation, have stumbled. The ORAC assay, long considered a gold standard, tracks the protection of a fluorescent probe from peroxyl radicals and integrates the area under the fluorescence decay curve. But it is exquisitely sensitive to temperature fluctuations across microplate readers, and natural product matrices full of endogenous pigments and fluorescent compounds interfere with the probe’s emission. These problems contributed to the United States Department of Agriculture officially discontinuing its public validation databases for ORAC values in foods. The authors’ comparison table makes the trade-offs explicit: every one of the four principal acellular assays carries known interferents, and correlations with cell-based or in vivo markers are not consistently reported across independent studies for any of them.

The review then climbs the biological ladder. Cell-based antioxidant activity assays preload living cells, such as Caco-2 or HepG2 lines, with a cell-permeable probe like DCFH-DA, which cellular esterases convert into a trapped, non-fluorescent molecule. When an oxidative stressor is applied alongside a candidate antioxidant, reduced fluorescence signals scavenging of intracellular reactive oxygen species. These assays capture cellular uptake, metabolism, and bioavailability, but they demand sterile culture facilities, expensive imaging instruments, and produce results that vary with cell type, insult, and probe, which can itself be pro-oxidant. At the top sit in vivo models in rodents, zebrafish, and nematodes, which capture absorption, distribution, metabolism, and excretion, along with the endogenous antioxidant enzyme network of superoxide dismutase, catalase, and glutathione peroxidase. They are indispensable but ethically constrained, slow, costly, and variable, and extrapolating from animals to humans remains an open challenge.

The alternative the authors champion is to stop running the wet chemistry altogether. Near-infrared, mid-infrared, and Raman spectroscopy can capture a vibrational fingerprint of an intact sample in seconds, and a multivariate calibration model, most often partial least-squares regression, maps the spectral absorption bands directly to reference antioxidant values. Once validated, the model predicts antioxidant capacity from the optical spectrum alone, consuming zero reagents and leaving the sample untouched. The evidence base is substantial: near-infrared models with variable-selection algorithms predicted phenolic content and antioxidant capacity of peanut seeds with calibration coefficients of determination up to 0.95, similar architectures worked for black goji berries without any extraction step, and mid-infrared models predicted FRAP values of propolis extracts at comparable accuracy. The authors are careful to note that these headline values are calibration statistics, which tend to be inflated relative to cross-validated or external-test figures, and that a model built on one matrix, such as peanut seed, generally cannot be transferred to a structurally different matrix without recalibration. Cross-matrix generalizability remains an emerging frontier.

Machine learning is extending this pipeline in two directions. Quantitative structure-activity relationship models map computed molecular descriptors to measured antioxidant activity, and the review highlights several rigorous demonstrations. Jung and colleagues trained five algorithms on more than 1,900 compounds using extended-connectivity fingerprints, with Random Forest and Support Vector Machines achieving classification accuracy above 0.90 and generalizing to the external BATMAN natural-product database. Ghironi and colleagues compared eleven regression models on 1,911 small molecules from the AODB database, finding that ensemble tree methods, particularly Extra Trees and a consensus model combining Extra Trees, Gradient Boosting, and XGBoost, substantially outperformed linear approaches, and that the consensus model correctly predicted the antioxidant potency of urolithin A, a compound absent from training, in close agreement with experiment. Mateus and Abreu showed the approach can be democratized, building a four-descriptor model with entirely open-source tools, though the authors caution that with only 70 compounds and over 12,000 candidate descriptors, overfitting risk cannot be fully excluded.

The second direction is image-based prediction, which replaces the spectrophotometer with a smartphone camera. In one platform, a drop of sample reacts with DPPH on a moving-drop device, and the magenta-to-yellow color ratio is converted into IC50 and Trolox-equivalent values statistically indistinguishable from the reference method. Paper-based tests now allow complex food emulsions to be applied directly to DPPH-spotted strips with no extraction step at all. Machine learning classifiers operating across RGB, HSV, and CIELAB color spaces adapt to varying lighting, cameras, and users, overcoming the fragility of purely optical approaches. The most striking demonstration is a smartphone-integrated system for point-of-care antioxidant testing in human saliva: a convolutional neural network reached about 78 percent classification accuracy, a stacking ensemble of four CNNs with a Support Vector Machine meta-classifier pushed it to 92 percent, and adding a YOLOv4-tiny object detection step to localize the reaction vial raised accuracy to nearly 98 percent, all running in real time on an Android device without cloud processing, using a single image captured two minutes into the reaction.

The review is refreshingly honest about what these tools cannot do. Every chemometric and QSAR model is trained against the same wet-chemical reference values whose non-physiological limitations were catalogued at the outset, so AI currently accelerates and de-reagents the execution of these assays without, by itself, closing the biorelevance gap. Models trained on narrow compound sets fail to generalize without recalibration, and high-performing black-box models rarely reveal which features drove a prediction, a barrier to regulatory acceptance. The authors point to explainable AI frameworks such as SHAP, which assign each spectral band or molecular descriptor a quantitative contribution to a prediction, as a way to verify that models rely on mechanistically plausible features like phenolic hydroxyl count and conjugation rather than spurious correlations. They also sketch a future of multimodal sensor fusion combining orthogonal spectra and image data, federated learning networks that let laboratories train shared models without exchanging proprietary data, standardized open repositories of curated antioxidant datasets, and even blockchain-enabled provenance architectures that would give regulators tamper-evident assurance that a reported calibration curve corresponds to an untampered, timestamped dataset.

The paradigm shift the authors describe is ultimately about throughput and trust. A destructive, multi-hour wet-chemistry assay becomes a sub-minute, reagent-free spectral measurement; a benchtop spectrophotometer becomes a phone in a field worker’s hand; a black-box score becomes an auditable, interpretable prediction. None of this erases the need for cell-based and in vivo confirmation of biological efficacy, and the authors are explicit that triangulating across assay categories remains essential. But for the pharmaceutical, nutraceutical, and food industries screening thousands of candidate compounds and complex extracts, the message is clear: the next generation of antioxidant analysis will be learned from data rather than measured in a cuvette, and the laboratories that build validated, interpretable, transferable models first will define how antioxidant science is done for years to come.

Subject of Research: Limitations of traditional antioxidant assays and the integration of AI and non-destructive spectroscopic techniques for antioxidant activity prediction

Article Title: Overcoming the limitations of traditional antioxidant assays: the role of AI and non-destructive techniques

Article References: Mcheik, A., Jaber, A., Ibrahim, G., Cheble, E., & Yassin, A. (2026). Overcoming the limitations of traditional antioxidant assays: the role of AI and non-destructive techniques. Results in Chemistry, 31, Article 103899. https://doi.org/10.1016/j.rechem.2026.103899

Image Credits: AI Generated

DOI: 10.1016/j.rechem.2026.103899

Keywords: antioxidants, DPPH, ABTS, FRAP, ORAC, machine learning, QSAR, near-infrared spectroscopy, chemometrics, smartphone colorimetry, deep learning, oxidative stress

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