Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 31 appraisals
Journal of chemical information and modeling
Reinforcement Learning-Driven Multiproperty Optimization in Molecular Design Using Multicontext Transcriptome Data
Drug discovery inherently involves multiparameter optimization in the molecular design because drug candidate molecules must meet diverse properties such as bioactivity, synthesizability, and pharmacokinetic properties. This optimization has traditionally relied on iterative manual design and experimental testing, which are labor-intensive and time-consuming. There is therefore a strong incentive to develop computational methods that efficiently design drug-like molecules with multiple favorable properties using chemical and biological data on therapeutic targets. This study proposes a novel computational method for multiproperty optimization in the molecular structure design of bioactive molecules using multicontext (i.e., chemically and genetically perturbed) transcriptome data on human cells. We integrate a molecular generative model conditioned on a transcriptome profile observed with the target gene knockdown or overexpression into a reinforcement learning framework, enabling simultaneous optimization of a quantitative estimate of drug-likeness, synthetic accessibility score, and water/octanol partition coefficient, while accounting for system-level biological effects on a therapeutic target. Using comprehensive benchmarking against established baselines and rigorous validation across multiple metrics, we demonstrate that the proposed method consistently yields molecules with more favorable drug-like characteristics than existing methods. This proposed method can help achieve more efficient identification of novel drug candidates.
29 July 2026
Read appraisal →Journal of chemical information and modeling
PegaPlus─Interactive Machine Learning by Human Observation for Efficient Clustering and Analysis of Structure-Activity Data
In the early stages of drug discovery, the identification of molecular series with lead-like properties is essential for structure-activity relationship studies. A common first step is to cluster data derived from high-throughput screening (HTS). This data then needs to be exhaustively refined by an expert to identify bioactive compound classes. Here, we present the novel, interactive, and visual approach named PegaPlus for the analysis of HTS data, facilitating the incorporation of expert knowledge with the strategy of learning by observation. PegaPlus uses a tailored variant of a stochastic proximity embedding algorithm to visualize the clustered data in a two-dimensional plane via a web interface. The user can iteratively modify the proposed compound clustering by dragging data points in the drawing plane. At each step, an online support vector machine learns and improves its clustering predictions by incorporating user modifications and the molecular features of the compounds. The visualization is also updated based on the user's input and molecular similarity. PegaPlus was developed with the exploratory process in mind and allows the user to freely organize molecules on the drawing plane, or filter them by various molecular properties. Our evaluation shows that PegaPlus reduces the number of refinement steps needed by half in comparison to the purely manual approach on multiple data sets. These results demonstrate how an interactive machine learning approach can reliably support medicinal chemists and automate time-consuming, manual refinement tasks. The PegaPlus web server is openly available at pegaplus.zbh.uni-hamburg.de.
14 July 2026
Read appraisal →Cell host & microbe
Mining the code of life for new antibiotics.
Antimicrobial resistance (AMR) is outpacing antibiotic development, creating an urgent need for discovery strategies that are faster, broader, and more systematic. Here, we review the transition from classical "dirt mining" and phenotypic screening toward digital discovery approaches that treat chemical structures and biological sequences as searchable, engineerable substrates for antibiotic innovation. Modern extensions of conventional screening, including in situ cultivation, co-culture, and microfluidics, have broadened access to previously uncultured microbes. Computer-aided approaches spanning virtual screening, molecular networking, and deep learning have enabled identification of unconventional antibacterial scaffolds from ultra-large chemical libraries. Mining genomes, proteomes, and metagenomes has uncovered antimicrobial peptides, encrypted peptides, and biosynthetic gene clusters encoding novel small-molecule antibiotics. Generative AI now enables design of peptides and small molecules under multiobjective constraints, including potency, toxicity, stability, and resistance risk. Together, these advances point toward discovery platforms that improve novelty, hit rates, and long-term durability in the face of AMR.
10 July 2026
Read appraisal →Reviews in the neurosciences
Probiotics and seizure susceptibility in preclinical epilepsy models: mechanistic insights from a systematic review and meta-analysis
The gut microbiota can affect neuronal excitability, inflammation, and oxidative balance via the gut-brain axis, shaping seizure susceptibility. To translate these findings into clinical approaches, a synthesis of preclinical microbiome-based evidence is needed. This systematic review and meta-analysis examined the putative anticonvulsant, anti-inflammatory, antioxidant, and neuroprotective effects of probiotics in rodent models. An extensive systematic search up to July 2025 identified eligible animal studies in which probiotics were administered in seizure models. Reported outcomes included seizure latency, duration, severity, and frequency, as well as inflammation, oxidative stress, and behavioral measures. Of the 24 studies that met the inclusion criteria, 19 provided sufficient data to be included in the meta-analysis. The most frequently used strains belonged to the Lactobacillus (e.g., acidophilus, casei, fermentum) and Bifidobacterium genera (e.g., bifidum, longum), with occasional synbiotic combinations. Probiotics significantly increased seizure latency (MD = 22.09; 95 % CI: 10.52 to 33.67) and reduced seizure severity (MD = -1.08; 95 % CI: -1.39 to -0.76) and duration (MD = -23.19; 95 % CI: -35.56 to -10.82). Probiotics significantly reduced IL-1β, IL-6, and TNF-α levels while MDA showed a nonsignificant trend toward reduction (p = 0.076). Behaviorally, improvements in spatial learning (p < 0.05) and reduced anxiety-like behavior (p < 0.001) were observed. Probiotic supplementation appears to exert anticonvulsant, anti-inflammatory, antioxidant, and behavioral benefits in preclinical epilepsy models, although the evidence is heterogeneous and limited to animal studies. Mechanistic evidence indicates modulation of the gut-brain axis, enhanced GABAergic signaling, and improved mitochondrial function. These findings support further investigation of specific probiotic formulations as promising adjunct candidates in well-designed, mechanism-driven clinical trials.
25 June 2026
Read appraisal →International immunopharmacology
Antimicrobial peptides in malaria and tuberculosis management: a systematic review of emerging evidence
AMPs (Antimicrobial peptides) are small molecules that are crucial components of biological activities, viz wound healing, angiogenesis, antimicrobial activity, immune regulation, and exhibit anticancer properties. This systematic review summarizes the origin, sequence, efficacy, and structure-activity mechanisms of AMPs against tuberculosis and malaria, the world's leading killer infectious diseases, employing the AMP database (APD3) (till June 2025) and PubMed search (January 1965 to June 2025). The comprehension is well addressed by covering the inhibitory activity, SAR (structure-activity relationships), and action mechanisms of AMPs (natural and synthetic) against Mycobacterium tuberculosis and Plasmodium spp., as both pathogens exhibit multidrug resistance. The review also illustrates how these specific motifs, along with their properties such as hydrophobicity, charge, or amphipathicity, influence the AMPs activity against the pathogens. Moreover, challenges in the AMPs use and the way forward, including the recent breakthroughs employing nanocarriers, inhalable formulations for TB, and targeted erythrocyte delivery for malaria, are also highlighted. AMPs have illustrated promising anti-TB and antimalarial efficacy owing to their unique structure and novel mode of action, despite exhibiting varying extents of toxicity. Anti-malarial AMPs are grouped in defensins and cecropins, while the majority of natural anti-TB AMPs are classified as bacteriocins, defensins, and cathelicidins. Synthetic AMPs, in contrast, are designed to enhance activity and selectivity while reducing toxicity and the development of resistance. AMPs have α-helical, β-sheet, or random-coil structures and are cationic, amphipathic, and highly hydrophobic, attributes that contribute to observed anti-TB and anti-malarial potential. A comprehensive understanding of AMP's structure and function, along with the use of artificial intelligence (AI) and machine learning, can provide impetus for AMP therapy as an alternative to tackle tuberculosis and malaria.
15 June 2026
Read appraisal →Journal of ethnopharmacology
Exploring the molecular mechanism of Shenling baizhu formula in the treatment of ulcerative colitis via the cGMP-PKG signaling pathway
ETHNOPHARMACOLOGICAL RELEVANCE: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by symptoms such as persistent diarrhea. Shenlingbaizhu Formula (SLBZ), a classical Chinese herbal formula with centuries of clinical application, has long been used to treat such gastrointestinal disorders. This historical efficacy provides a strong ethnopharmacological rationale for investigating its therapeutic potential and underlying mechanisms in UC. AIM OF THE STUDY: To elucidate the molecular mechanism by which SLBZ ameliorates UC by restoring the intestinal barrier via the sGC-cGMP-PKG signaling pathway. MATERIALS AND METHODS: To systematically investigate the therapeutic effects of SLBZ on UC, we established a dextran sulfate sodium (DSS)-induced colitis model in 57BL/6 mice. Comprehensive evaluations were conducted to assess intestinal barrier function and elucidate underlying mechanisms, including histological analysis, immunofluorescence staining of tight junction proteins, 16S rDNA sequencing for gut microbiota profiling, transcriptomic analysis for gene expression patterns, and Western blot validation of key proteins. Furthermore, a multi-tiered experimental strategy was employed, integrating molecular modeling, machine learning algorithms, graph neural network (GNN) approaches and in vitro experiments using Caco-2 cell monolayers, to systematically validate the sGC-cGMP-PKG signaling pathway and identify bioactive compounds responsible for SLBZ's therapeutic effects. RESULTS: SLBZ treatment markedly alleviated UC symptoms in mice and restored intestinal barrier function through dual mechanisms: upregulation of tight junction proteins (ZO-1/Occludin) and modulation of gut microbiota composition. Transcriptomic analysis pinpointed the cGMP-PKG signaling pathway as a central therapeutic target. Through an integrated approach combining molecular docking, machine learning (random forest algorithm), and deep neural network analysis, we identified soluble guanylate cyclase (sGC) as the primary molecular target, with Choerospondin emerging as a key bioactive component. Mechanistic investigations demonstrated that SLBZ robustly activated the sGC-cGMP-PKG axis both in vivo (colon tissues) and in vitro (Caco-2 cells), culminating in significant suppression of intestinal epithelial apoptosis. CONCLUSION: SLBZ alleviates UC by activating the sGC-cGMP-PKG pathway to restore intestinal barrier function through tight junction upregulation and microbiota modulation. Our study both validates SLBZ's traditional use and identifies this pathway as a novel therapeutic target for UC.
12 June 2026
Read appraisal →PLoS computational biology
A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing
Many genetic loci were identified as associated with neuropsychiatric disorders and neurodegenerative disorders by Genome-wide association studies (GWAS). How these loci impact these diseases is unclear. Advances in deep-learning approaches and multi-omics data have the potential to link GWAS findings with disease mechanisms. Here, we proposed the Multi-omics Graph Transformer Network (MOGT), a semi-supervised graph neural network that leverages graph representation learning to model biological networks derived from multi-omics data to predict disease-associated genes. MOGT outperforms the current approaches in disease gene prediction for two psychiatric disorders and three neurodegenerative/neurological diseases. High-risk genes (HRGs) for Parkinson's disease (PD) predicted by MOGT were used to drug discovery by integrating with the CMAP database. Finally, 10 drugs were identified as potential candidates. Among them, the effect of drug UK-356618 was experimentally verified in a primary neuron model, showing that UK-356618 reversed the abnormal expression of PD-associated genes and improved the cell-level phenotypes of PD. Together, these results indicate that MOGT can be used to identify HRGs for brain disorders, and these predicted HRGs provide high-level insights into the mechanisms and treatments of brain disorders.
1 June 2026
Read appraisal →Medicine
Environmental toxicant glyphosate induces cardiotoxicity: New insights from network toxicology, integrated machine learning, molecular modeling and multidimensional bioinformatics analysis
Glyphosate is one of the world's most widely used herbicide, yet the causal relationship between glyphosate and cardiotoxicity remains inadequate. To elucidate the cardiotoxic mechanisms of glyphosate, an integrative strategy was developed in this work, which combines computational toxicometry, predictive machine learning, and atomic-level molecular modeling. Initially, glyphosate's systematic toxicological profile was evaluated using ProTox-3.0 and ADMETlab 3.0. Candidate genes associated with glyphosate were obtained from 3 separate databases. Target genes associated with cardiotoxicity were integrated from 2 databases. Intersection analysis identified 113 core candidate genes associated with glyphosate and cardiotoxicity. Functional enrichment-related analyses were employed and revealed that the target genes were significantly involved in key biological processes such as ERBB3 signaling pathways. In order to refine the candidate genes to a more precise gene pool, net topology analyses were employed and identified 16 key feature genes. We subsequently integrated machine learning algorithms and validated the results using the GEO database, ultimately identifying 4 hub genes, AKT1, IL1B, BRCA1, and PTGS2. Finally, we applied molecular docking analysis and revealed strong binding affinities, which suggested direct interaction mechanisms in glyphosate-induced cardiotoxicity. In summary, our study establishes a comprehensive mechanistic framework for glyphosate-induced cardiotoxicity.
31 May 2026
Read appraisal →Journal of medical Internet research
Large Language Model-Generated Patient Instructions for Prescriptions in Primary Health Care: Preclinical Algorithm Validation
BACKGROUND: The application of generative artificial intelligence to simplify medication use instructions has the potential to enhance people's health by improving treatment adherence. OBJECTIVE: We evaluated the performance of large language models (LLMs) in generating medication usage instructions to complement prescriptions in primary health care. METHODS: This randomized, blinded experimental preclinical study used prescription-inducing scenarios, assigned to 62 health care professionals, to validate instructions generated by LLMs during electronic prescriptions. The instructions were generated by ChatGPT-4.0 (OpenAI), Llama3.1-8B (Meta), and Llama3.1-8B-RAG (Meta) using retrieval-augmented generation based on patient information leaflets. Performance metrics assessed adequacy, completeness, clarity, language simplification, usefulness, and errors in the generated instructions, with scores to analyze overall and individual metrics. RESULTS: The 3 models yielded high overall scores for producing qualified instructions (ChatGPT-4.0: median 88.4, IQR 22.8; Llama3.1-8B: median 66.5, IQR 50.9; Llama3.1-8B-RAG: median 79.9, IQR 34.4; Kruskal-Wallis test P=.003). Llama3.1-8B-RAG received evaluations with similar overall scores to ChatGPT-4.0 (post hoc test, P=.05) and similar to Llama3.1-8B (post hoc test, P=.44). ChatGPT-4.0 outperformed Llama3.1-8B (Bonferroni test, P<.001). Regarding specific domains, Llama3.1-8B-RAG received scores equivalent to those of ChatGPT-4.0 for adequacy (mean 6.24, SD 2.3 vs mean 6.82, SD 2.1; post hoc test, P=.54); completeness (mean 5.94, SD 2.2 vs 6.55, SD 1.9; post hoc test P=.38), clarity (mean 5.77, SD 2.4 vs mean 6.68, SD 1.9; post hoc test P=.09), and usefulness (mean 5.42, SD 2.4 vs mean 5.96, SD 2.2; post hoc test P=.63). ChatGPT-4.0 received higher scores in the language simplification criterion than Llama3.1-8B-RAG (mean 7.05, SD 1.5 vs mean 5.44, SD 2.6; post hoc test P<.001). Interrater variability in assigning scores ranged from 4.2% (n=3) to 85.8% (n=6) among primary health care professionals. Instructions leading to incorrect use of the medication had similar frequency among the models(ChatGPT-4.0: n=15, 22.7%; Llama3.1-8B: n=19, 22.8%; Llama3.1-8B-RAG: n=19, 22.8%; chi-square test P=.71). The frequencies of hallucination were similar (ChatGPT-4.0: n=7, 10.6%; Llama3.1-8B: n=9, 13.6%; Llama3.1-8B-RAG: n=6, 9.1%; chi-square test P=.67). CONCLUSIONS: The open-source LLM enhanced with external information presented similar performance to the closed-source model, except for ChatGPT4.0, which was superior in language simplification of messages. LLM generation demonstrated potential for instructing patients on medication use. Nonetheless, the introduction of this innovation into the electronic prescribing workflow demands prescriber validation for human oversight of the technology and requires a strategy for LLM performance governance. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.12688/verixiv.1359.1.
27 May 2026
Read appraisal →Journal of chemical theory and computation
Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework
Computational prediction of allosteric binding sites in protein structures remains a persistent challenge, as these regulatory pockets evade detection by both sequence-based and structure-based algorithms. Both computational and physical origins of this predictive asymmetry remain insufficiently understood. In this study, we systematically examine the determinants of binding site predictability using a dual framework that integrates a fine-tuned protein language model and the structure-based method P2Rank as complementary tools probing a diverse data set of 453 human kinases, together with a physics-based interpretability layer derived from energy landscape frustration analysis. Both predictors exhibit a sharp and reproducible dichotomy on protein kinases, in which orthosteric ATP-binding sites can be identified with high precision, whereas allosteric sites are detected with substantially lower confidence across kinase structures and distinct conformational states. To decode this divergence, we deploy energy landscape-based explainable AI approach that integrates local frustration analysis as an independent physical interpretability layer, mapping predictive behavior to the underlying energetic organization of protein structures. This analysis reveals that predictive success is governed by the local energetic embedding of binding sites within the protein energy landscape. Orthosteric pockets are located in minimally frustrated basins that generate strong evolutionary and structural signatures, whereas allosteric pockets occupy predominantly neutrally frustrated zones associated with conformational plasticity and reduced evolutionary constraint. By integrating the prediction results with energy landscape analysis, our framework converts predictive performance into physically interpretable descriptors of binding site organization in protein kinases. These results establish energy landscape frustration as a potentially important determinant of algorithmic visibility and an interpretability layer providing a feasible strategy for diagnosing the limits of current prediction methods.
27 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Predictions from deep learning propose substantial protein-carbohydrate interplay
Noncovalent interaction between proteins and carbohydrates (sugars, glycans) is the basis for biological functions from metabolic regulation to intercellular recognition. It is a grand challenge to identify the protein-carbohydrate interactomes in organisms. Direct experiments would require extensive libraries of glycans to distinguish binding from nonbinding proteins. Computational screening of proteins for carbohydrate binding potential provides an attractive alternative. Current estimates propose that <5% of proteins bind carbohydrates, a number that is not well established. We therefore developed a neural network, "Protein interaction of Carbohydrates Predictor" (PiCAP), to predict whether a protein noncovalently binds to a carbohydrate. We trained PiCAP on a manually curated dataset of known carbohydrate binders and proteins that we identified as likely not to bind carbohydrates (transcription factors, cytoskeletal components, and small-molecule-binding proteins). PiCAP achieves 90% balanced accuracy on protein-level predictions of carbohydrate binding/nonbinding. Using the same datasets, we developed Carbohydrate Protein Site Identifier 2 (CAPSIF2) to predict protein residues that interact noncovalently with carbohydrates. CAPSIF2 achieves a Dice coefficient of 0.57 on residue-level predictions on our independent test dataset, outperforming previous models. To demonstrate the models' biological applicability, we investigated human cell surface proteins and further predicted the likelihood of carbohydrate binding in six proteomes (Escherichia coli, Mus musculus, Homo sapiens, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster). PiCAP predicts that ~35 to 40% of proteins in these proteomes bind carbohydrates, with 75% of extracellular and cell surface proteins predicted to bind. The PiCAP predicted binders are enriched for functions including growth factor receptor binding, inflammation, and cell-cell adhesion.
26 May 2026
Read appraisal →Pharmaceutical biology
Ginger-based formulations for allergic rhinitis disease: a systematic review and meta-analysis of experimental studies in animals and humans
BACKGROUND: Allergic rhinitis is a chronic non-communicable disease that adversely affects respiratory health and quality of life. Ginger-based formulations, owing to their anti-inflammatory and immunomodulatory properties, may offer a promising alternative therapeutic option. OBJECTIVE: To systematically review and meta-analyze evidence from animal studies and human randomized controlled trials assessing the efficacy of ginger-based formulations in allergic rhinitis. METHODS: A systematic search of the Cochrane Library, Embase, PubMed, and Scopus (April 2025) identified studies comparing ginger-based formulations with control interventions. Outcomes included clinical symptoms and relevant biomarkers. Risk of bias was assessed using the SYRCLE tool for animal studies and RoB2 for human studies. Meta-analyses were conducted using random-effects models, with results expressed as standardized mean difference (SMD) and 95% confidence intervals (CIs). RESULTS: Twenty-one studies (6 animal and 15 human studies) were included. In rodents, ginger-based formulations significantly reduced nasal rubbing (SMD = -1.99; 95%CI, -3.15 to -0.83), sneezing (SMD = -1.99; 95%CI, -2.78 to -1.20), and biomarkers (IL-4, IL-5, and OVA-specific IgE). In humans, ginger-based formulations significantly improved itching (SMD = -0.51; 95%CI, -0.79 to -0.24), runny nose (SMD = -0.59; 95%CI, -0.87 to -0.30), sneezing (SMD = -0.56; 95%CI, -0.75 to -0.37), total nasal symptom scores (SMD = -0.63; 95%CI, -1.00 to -0.26), and quality of life (SMD = -0.56; 95%CI, -0.87 to -0.24), and eosinophil levels. CONCLUSION: Ginger-based formulations improve symptoms and biomarkers of allergic rhinitis in both animal and human studies. As many interventions are multi-component, the observed effects are more appropriately attributed to the overall formulations rather than ginger alone. Further research is needed to explore mechanisms and clarify clinical applications.
26 May 2026
Read appraisal →Biochemistry
Antimycobacterial Peptides: From Natural Product Discovery to AI Guided Design
Mycobacterial pathogens remain major global health threats, exacerbated by both rapid acquisition of antibiotic resistance and the formidable drug diffusion barrier presented by the rigid mycomembrane. These challenges have renewed interest in antimycobacterial peptides (AMyPs), a diverse class of short amphiphilic sequences capable of rapidly killing both drug-sensitive and drug-resistant mycobacteria. Beyond their intrinsic potency, AMyPs can synergize with existing antibiotics and exhibit markedly slower resistance development relative to conventional small molecules. In this review, we synthesize recent advances spanning natural bioprospecting, mechanism-guided rational design, and chemical optimization strategies that have yielded increasingly potent and selective AMyP candidates. We further highlight the rapid emergence of artificial intelligence-driven discovery platforms, which leverage machine-learning models trained on curated, mycobacteria-specific data sets to predict and refine novel AMyPs with growing accuracy. Together, these technologic, biologic, and computational advances outline a rapidly expanding landscape for AMyP-based therapeutic development and establish a foundation for next-generation antimycobacterial drug design.
21 May 2026
Read appraisal →Analytical chemistry
Adaptive Deep Learning for High-Fidelity Quantitative Single-Molecule Imaging
Accurate quantification of molecular dynamics in live cells is critical for elucidating receptor signaling and guiding therapeutic strategies. Yet current deep learning methods for fluorescence imaging often distort intensity and lack robustness under diverse imaging settings, limiting quantitative utility. We present an adaptive deep learning framework that constructs in situ training data sets from ongoing sequences and integrates a self-feedback algorithm to preserve absolute molecular intensities. This approach achieves ≥ 3.9-fold gains in signal-to-noise and 1.6-fold improvements in localization precision across organic dyes, quantum dots, and fluorescent proteins. By maintaining both spatial and intensity fidelity, it enhances super-resolution reconstruction, stepwise photobleaching analysis, and live-cell single-molecule tracking. Applied to programmed death-ligand 1 (PD-L1), it uncovers density-dependent clustering, with small-molecule inhibitors inducing dimerization and reduced mobility, while antibodies increase diffusivity. Combined treatments exert complementary effects on PD-L1 membrane organization. This adaptive and intensity-preserving framework provides a broadly applicable platform for high-precision quantitative bioimaging across modalities and experimental conditions.
20 May 2026
Read appraisal →Journal of molecular modeling
Carbonic anhydrase under the computational lens: a review of advances, challenges, and future directions
CONTEXT: Carbonic anhydrases (CAs) are zinc metalloenzymes responsible for the catalysis of the reversible hydration of carbon dioxide and play pivotal roles in many physiological and pathological processes. The complexity of CA isoforms with distinct expression patterns and catalytic characteristics has aroused fierce interest in the development of isoform-selective inhibitors and activators for therapeutic purposes and diagnostics. In this review, a systematic overview of computational and theoretical methods utilized to determine the structure, mechanism, inhibition, and activation of CAs was given. It reviews the evolution and utilization of computational methods in deciphering catalytic and proton transfer mechanisms as well as active-site interactions into atomic resolution and also features recent developments in drug design and screening strategies engineered for CA isoforms, particularly those involved in cancers and Alzheimer's disease. This study can be a reference for multidisciplinary researchers and inspire the future rational design of CA modulators. METHODS: This review summarizes the literature that employed QM and QM/MM by using like B3LYP, BE0-D3BJ, and MD simulations, GROMACS and Desmond packages, and various force fields: OPLS-AA, AMBER14, and CHARMM36. Molecular docking was performed using such as Glide, AutoDock Vina, and GOLD. Machine learning and AI techniques including SVM, LightGBM, SHAP, and neural networks were also reviewed. This review addresses the limitations in these models as well as prospects with newly emerging instruments such as polarizable force fields and real-time DFT as well as explainable AI frameworks, too. Incorporating data-driven insights with physics-based simulations, the next era of work is primed to provide predictive, scalable platforms for rational design of next-generation CA modulators.
15 May 2026
Read appraisal →Neurology. Clinical practice
Optimizing Antiseizure Medication Taper in the Epilepsy Monitoring Unit
BACKGROUND AND OBJECTIVES: Inpatient video-EEG epilepsy monitoring aims to provide a safe environment for antiseizure medication (ASM) tapering to precipitate seizures, which is often required for diagnosis and treatment. However, there is no standardized taper protocol across institutions, which may contribute to increased morbidity through longer hospitalizations if the taper is too mild and bilateral tonic-clonic seizures (BTCSs) if too aggressive. This study investigates how ASM taper influences length of stay (LOS) and patient outcomes in the epilepsy monitoring unit (EMU), aiming to develop guidelines for medication taper strategies to maximize diagnostic yield while minimizing patient risk. METHODS: We used natural language processing to extract data from electronic medical records of 639 patients admitted to the University of Pennsylvania EMU between 2017 and 2023. We estimated ASM load, the total normalized sum of all ASM serum concentrations based on dose-dependent pharmacokinetic models, throughout each hospitalization. We applied LASSO (Least Absolute Shrinkage and Selection Operator) regression and mixed-effects models to identify predictors of LOS and BTCS risk as a function of taper speed and percentage of baseline ASM load in patients with (hBTCS) and without (nBTCS) a history of BTCS. RESULTS: Taper speed did not correlate with BTCS risk, although absolute ASM load did. Median ASM loads at seizure occurrence were 51% of baseline in the hBTCS group, 41% in the nBTCS group, and 39% in the unclear group. BTCS occurred at higher ASM loads in patients with hBTCS vs nBTCS. Median ASM loads at first EMU seizure were 33% for both hBTCS and nBTCS groups, while the unclear group had first seizures at lower loads (25% of baseline). Time to first seizure and number of baseline ASMs were positively correlated with longer hospital stays, suggesting that sequential medication taper delayed seizures and prolonged LOS without altering BTCS risk. DISCUSSION: Patient history and ASM load are critical factors in predicting BTCS. Rapid taper to approximately 50% baseline ASM load may more efficiently produce seizures without increased BTCS risk in patients without BTCS history. Individualized taper protocols remain advised, particularly for severe epilepsies. This study provides a foundation for quantitative, evidence-based guidelines to optimize ASM reduction strategies for safely precipitating seizures in EMUs.
15 May 2026
Read appraisal →Translational psychiatry
Acute placebo responsiveness predicts longitudinal expectation effects in antidepressant treatment
Positive treatment expectations are known to enhance antidepressant efficacy. Experimental placebo studies aim to provide mechanistic insights into this association, yet their findings have not been clearly linked to clinical expectation effects within individual patients. In a randomized crossover trial, 61 participants with major depressive disorder (31 women, 30 men) completed an emotion classification task and mood assessments after administration of a saline nasal spray labeled as either oxytocin (sham oxytocin treatment, deceptive placebo) or saline (control). Forty-five participants subsequently underwent antidepressant treatment and were monitored weekly for treatment expectations and depressive symptoms, with follow-ups extending to three months. Using linear mixed-effect models, we examined trajectories of clinical expectation effects and their prediction by experimental placebo-induced changes in emotional processing. Sham oxytocin treatment acutely induced positive expectations, improved mood, and shifted emotional processing toward positivity. Longitudinal modeling revealed that weekly reductions in depressive symptoms were predicted by higher treatment expectations reported the preceding week. These clinical expectation effects were most pronounced in individuals exhibiting strong acute placebo responses in emotional processing. Overall, our findings indicate that positive treatment expectations facilitate critical aspects of emotional processing in depression and that individual responsiveness generalizes across different treatments and time windows. Experimental placebo assessments offer an ecologically valid model for elucidating mechanisms and predictors of clinical expectation effects and highlight expectation sensitivity as a promising target for treatment optimization.
15 May 2026
Read appraisal →International journal of oral science
Machine learning-driven discovery of therapeutic nucleoside hydrogels for periodontitis
Supramolecular hydrogels hold significant potential in drug delivery and tissue engineering, with standing out for their unique properties. Despite their promise, predicting nucleoside bioactivity remains challenging. This study aims to predict the biological activity of nucleosides to guide the rational synthesis of hydrogels. Specifically, nine predictive models and databases for various biological activities were built with feature-selected machine learning methods including decision trees, logistic regression, random forest, and extreme gradient boosting. Then, the Molecular Bioactivity Specificity Index (MBSI) was introduced to gauge the primary bioactivity of nucleoside derivatives, and the Composite Molecular Attribute Score (CMAS) was devised to measure the overall performance of nucleoside derivatives. Subsequently, screening strategies for bioactive nucleoside hydrogels were established, and two candidate hydrogels (GMP and dGMP) with high hydrogel-forming ability, biocompatibility, and antibacterial activity were identified. Finally, two hydrogels were validated for antibacterial treatment of periodontitis. This study highlights the feasibility of ML-based strategies and MBSI/CMAS in rationally designing bioactive nucleoside hydrogels for biomedical applications. The discovery of GMP and dGMP hydrogels and their successful validation in periodontitis models highlight the potential of this strategy for developing targeted therapies for oral diseases.
14 May 2026
Read appraisal →Angewandte Chemie (International ed. in English)
A Novel Acetylcholine Nanosensor for Single Vesicle Storage and Sub-Quantal Exocytosis in Living Neurons and Organoids
Acetylcholine (ACh) is a critical neurotransmitter that regulates diverse physiological functions, such as cognition and muscle contraction, through synaptic transmission. However, in situ quantitative chemical analysis of single-vesicle storage and release dynamics at single-cell level remains a major technical challenge, hindering mechanistic understanding of cholinergic synaptic plasticity across physiological and pathological states. To address this, we developed an Ti3C2Tx MXene/enzyme-functionalized (M@E@CF) nanosensor, enabling real-time monitoring of vesicular ACh storage and exocytotic release in primary cholinergic neurons and human spinal cord organoids. Our findings reveal a sub-quantal release mode in both mouse and human-derived neurons. To further elucidate the regulatory principles of exocytosis kinetics, we classified single-vesicle exocytosis patterns based on signal peak shapes using a 1D convolutional neural network (1D-CNN) deep learning model, uncovering significant differences in the number of released molecules and kinetic parameters across modes. Critically, in Down syndrome models, we observed significantly reduced single vesicle ACh storage and release alongside an elevated release fraction, concurrent with shortened fusion pore durations during exocytosis. Thus, the M@E@CF nanosensor platform establishes a versatile tool for spatiotemporal investigation of neurotransmitter storage and release dynamics, providing a critical technical foundation for exploring physiological functions and pathological mechanisms of the cholinergic system.
13 May 2026
Read appraisal →Journal of ethnopharmacology
Advances in the pharmacology and mechanism of anti-skin aging in Tonifying Traditional Chinese Medicines
ETHNOPHARMACOLOGICAL RELEVANCE: According to the Traditional Chinese Medicine (TCM) tenet that "internal imbalances manifest externally," skin aging reflects deficiencies in qi, blood, and organ systems-particularly the liver and kidneys. Tonifying Traditional Chinese Medicines (TTCM) address these root causes, nourishing the skin and delaying aging through internal regulation, as documented in classical texts including the Shennong Ben Cao Jing and the Compendium of Materia Medica. AIM OF THE STUDY: This article systematically reviews the anti-skin-aging mechanisms of TTCM by integrating traditional applications with modern pharmacological evidence. It explores their multi-component, multi-target, and multi-pathway actions, evaluates current experimental models, and investigates the emerging role of artificial intelligence (AI) in advancing TTCM research. MATERIALS AND METHODS: We systematically searched PubMed, China National Knowledge Infrastructure (CNKI), Web of Science and Scopus for articles published from January 2015 to August 2025 with keywords such as "skin aging", "anti-aging" and the Latin names of TTCM (e.g. Panax ginseng, Astragalus membranaceus). Cellular, animal and clinical studies were analyzed for active constituents, molecular targets, ageing biomarkers and related signalling pathways. RESULTS: TTCM demonstrates anti-skin-aging effects through systemic regulation of organ function and qi-blood-yin-yang balance. At the molecular level, TTCM attenuates senescence through antioxidative, anti-inflammatory, extracellular matrix (ECM) stabilizing mechanisms, mediated by modulation of multiple pathways, such as Mitogen-Activated Protein Kinase (MAPK), Tumor Protein p53 (p53) and Nuclear factor erythroid 2-related factor 2 (Nrf2), exemplifying a "holistic regulation-targeted intervention" therapeutic paradigm. CONCLUSIONS: TTCM exerts anti-skin-aging effects through multi-component, multi-target and multi-pathway synergism. We strongly recommend the further integration of TTCM with modern medicine and in-depth mechanistic studies to promote translational applications.
12 May 2026
Read appraisal →Journal of computer-aided molecular design
NeuroPred-GMC: a dual-branch deep learning architecture for neuropeptide prediction based on gated dilated convolutional network and multi-scale convolutional network
Neuropeptides are multifunctional signaling molecules in the nervous system. By modulating synaptic transmission and integrating physiological systems, they influence a broad range of functions from pain perception to emotional regulation. Predicting neuropeptides can rapidly expand the library of potential therapeutic targets, thereby providing novel candidate molecules for drug development in areas such as analgesics, anti-anxiety medications, and weight-loss drugs. Traditional experimental methods are extremely time-consuming, labor-intensive, these promising alternative computational methods have emerged. In this study, a dual-branch deep learning architecture for neuropeptide prediction known as NeuroPred-GMC are built up based on gated dilated convolutional network with ESM-2 feature representation and multi-scale convolutional network with Prot-T5 feature representation. Dilated convolution exponentially enlarges the receptive field via increased dilation rates, gating mechanism enables dynamic, selective feature enhancement and noise suppression, and multi-scale convolution captures multi-level contextual information. On the independence test set, the accuracy of 93.24%, Sn of 93.69%, Sp of 92.79%, Pre of 92.86%, MCC of 0.8649 and the auROC of 0.9667 are obtained. The experimental results through cross-validation and independent test demonstrate that the proposed model has good robustness and generalizability, and can serve as a supplemental candidate predictor. The source datasets and codes can be freely available at https://github.com/yunyunliang88/NeuroPred-GMC .
11 May 2026
Read appraisal →Computers in biology and medicine
A decoction-inspired genetic algorithm and PPO reinforcement learning for intelligent molecular discovery: Anti-colorectal cancer candidates from the tiao-pi AnChang formula
BACKGROUND: Colorectal cancer (CRC) remains among the leading causes of cancer-related mortality worldwide. The TiaoPi AnChang Decoction (TPACD), a traditional Chinese herbal formula, has shown potential anti-CRC activity in preclinical studies (in vitro and in vivo); however, its active chemical basis and key target interactions remain unclear. The complex, multicomponent nature of TPACD poses significant challenges for systematically identifying and optimizing compounds. PURPOSE: This study aimed to establish an intelligent molecular discovery framework that integrates a decoction-inspired genetic algorithm (EGA) with proximal policy optimization (PPO)-based reinforcement learning to simulate the dynamic transformation and recombination processes underlying traditional herbal decoctions and to generate and prioritize in silico candidate molecules for further anti-CRC evaluation. STUDY DESIGN: A parallel molecular generation strategy was developed, comprising three algorithmic pathways, namely, the basic molecular generation algorithm (BMGA), enhanced molecular generation algorithm (EMGA), and intelligent molecular generation algorithm (IMGA), which represent progressive levels of structural diversity, biological relevance, and optimization capacity. METHODS: The EGA simulated the selection-transformation-recombination principles of decoction through adaptive mutation, fragment recombination, and a diversity-preserving selection procedure. PPO-based reinforcement learning further refined the molecular properties via a reward-guided exploration of the chemical space. The BMGA enabled baseline molecular generation; the EMGA incorporated affinity-based selection; and the IMGA achieved multiobjective optimization, integrating drug likeness, novelty, and toxicity filtering. The candidate molecules were validated by affinity prediction, QED scoring, and molecular docking. RESULTS: All three methods generated chemically valid and pharmacologically enhanced molecules relative to the original TPACD components. The EMGA provided improved biological affinity and exploration efficiency, whereas the IMGA achieved the highest overall performance level, with superior QED and docking stability, and predicted the CRC inhibition potential of the molecules. CONCLUSION: The proposed EGA-PPO framework effectively connects traditional decoction principles with modern AI-based drug design. The tri-algorithmic system (BMGA-EMGA-IMGA) provides a scalable and interpretable strategy for de novo molecular discovery, offering promising leads for acquiring natural product-derived CRC therapeutics.
9 May 2026
Read appraisal →Journal of ethnopharmacology
MAGED: Multimodal attentive graph learning with gene expression dynamics on knowledge graphs for TCM target prediction
ETHNOPHARMACOLOGICAL RELEVANCE: Plants used in traditional medicine have long provided valuable clues for drug discovery, yet systematically connecting their documented therapeutic effects with modern understanding of biological mechanisms remains a key scientific question. Bridging the gap between traditional knowledge, clinical symptom profiles, and contemporary molecular-level regulation is essential for advancing the study of herbal medicine. OBJECTIVES: This study aims to develop a multimodal graph attention learning framework, named MAGED, which integrates knowledge graphs with gene expression dynamics to improve the accuracy and interpretability of Herb-Target Interaction (HTI) prediction. METHODS: MAGED incorporates multi-source heterogeneous data, including traditional Chinese medicine (TCM) properties, hierarchical clinical symptom information, and molecular-level biological data, such as gene expression dynamics under drug intervention and causal biological networks. A multimodal fusion encoder is employed to embed herb attributes into efficacy-related feature vectors, which are then combined with functional representations (e.g., normalized enrichment scores) to form a unified contextualized representation. Additionally, a hierarchical graph attention network is designed to integrate macroscopic symptom-gene associations with microscopic regulatory pathways, thereby establishing a coherent inference chain from herbal effects to biological targets and phenotypic symptoms. RESULTS: Systematic experimental evaluations demonstrated that MAGED significantly outperforms existing baseline methods across multiple evaluation metrics, achieving a 59.8 % improvement in HR@10. The model also exhibited superior ranking and recall performance under cold-start scenarios. In a case study on Scutellaria baicalensis, 8 out of the top 10 predicted targets were supported by existing literature, with functional or direct interaction evidence, and some targets were further validated through molecular docking. CONCLUSION: MAGED provides an accurate and interpretable framework for predicting herb-target interactions, effectively integrating traditional knowledge with modern molecular evidence. This approach shows strong potential to facilitate the discovery of herbal mechanisms and the identification of novel therapeutic targets.
27 Apr 2026
Read appraisal →The British journal of ophthalmology
Ethnic and dose-dependent differences in atropine efficacy for myopia control: a systematic review and meta-analysis
BACKGROUND/AIM: Clinical uncertainty remains regarding optimal atropine concentration, treatment duration and potential differences in efficacy for myopia control between Asian and non-Asian children. This systematic review and meta-analysis evaluated the efficacy of different concentrations of atropine for myopia control, comparing outcomes among East Asian, South Asian and white European children. METHODS: Five databases were searched for randomised controlled trials (RCTs) including children ≤16 years with myopia who received atropine treatment. Thirty-four RCTs with ≥12 months of follow-up were included. Weighted mean differences (WMD) in spherical equivalent refraction (SER) progression and axial length (AL) elongation were pooled by atropine concentration and ethnicity. RESULTS: Compared with controls, atropine significantly reduced myopia progression across all concentrations: <0.1% (WMD in SER: 0.44 (95% CI 0.35 to 0.52) dioptres (D)/year; AL: -0.20 (95% CI -0.24 to -0.16) mm/year), 0.1% to <0.5% (0.81 (95% CI 0.50 to 1.13) D/year) and ≥0.5% (1.06 (95% CI 0.88 to 1.24) D/year; -0.36 (95% CI -0.40 to -0.33) mm/year). The pooled effect on SER and AL progression across all concentrations was greater in East Asians (0.63 (95% CI 0.50 to 0.76) D/year; -0.26 (95% CI -0.31 to -0.20) mm/year) than in South Asians (0.40 (95% CI 0.11 to 0.70) D/year; -0.13 (95% CI -0.21 to -0.05) mm/year) or white Europeans (0.18 (95% CI 0.11 to 0.25) D/year; -0.11 (95% CI -0.16 to -0.05) mm/year). CONCLUSION: Atropine slows myopia progression in a dose-dependent manner in studies with 1-5 years of follow-up. Efficacy appears greater in Asian children, particularly East Asians, who also exhibit greater photopic pupil dilation. These findings support the role of atropine in myopia control and highlight the importance of ethnicity-specific considerations when prescribing and tailoring treatment strategies. PROSPERO REGISTRATION NUMBER: CRD42023454104.
26 Apr 2026
Read appraisal →Journal of medicinal chemistry
Machine Learning Approach to Anticancer Activity Prediction of Transition-Metal Complexes Based on a Large-Scale Experimental Database
In this work, we developed a straightforward data-driven approach to predict the cytotoxicity of metal complexes based entirely on their (metal + ligands) composition. To this end, we have manually curated MetalCytoToxDB─a comprehensive experimental database comprising 26,500 IC50 values for 7050 metal complexes against 754 cell lines from 1921 articles. Based on these, machine learning models were created to accurately assign the cytotoxicity class within the ruthenium and iridium subsets. Moreover, external validation of the best-performing model on the unseen data was carried out. The possibility of multimetal predictions was explored, enabling assessment of cytotoxicity among the complexes of metals, for which experimental data are relatively scarce. The interpretability and limitations of the developed models are discussed. Finally, a pipeline for the effective high-throughput computational screening of ruthenium complexes is proposed. The MetalCytoToxDB is available online for AI-assisted exploration at https://biometaldb.streamlit.app/.
25 Apr 2026
Read appraisal →ACS nano
Deep Learning Enables Pixel-Level Nanoparticle Distribution Mapping in Routine Histological Sections by Integrating Cancer Associated Fibroblasts Features
The efficient accumulation and uniform distribution of nanomedicine within tumors are critical for achieving therapeutic outcomes. However, conventional medical imaging technologies struggle to efficiently and accurately detect nanoparticles (NPs) distribution, especially in resolving their cellular-level spatial heterogeneity. Existing deep-learning-based predictive models focus on tumor cell density and vascular distribution but do not address the complex spatial relationship between cancer-associated fibroblasts (CAFs) and drug distribution. This study presents NanoNet, a deep-learning framework that leverages fibroblast activation protein (FAP) immunostaining to spatially characterize CAFs and predict NPs distribution at high resolution. NanoNet achieved high predictive accuracy (ICC = 0.963, R2 = 0.9849) by transforming tumor section images into pixel-level NPs distribution maps. The FAP channel contributed substantially to predictive accuracy, indicating its important role in guiding NPs behavior. This study provides a spatially resolved predictive framework that enables pixel-level predictions of NPs distribution from conventional histological sections, with potential applications for optimizing nanomedicine design and personalized nanomedicine.
15 Apr 2026
Read appraisal →Journal of controlled release : official journal of the Controlled Release Society
Redox-active microneedles for disease management.
Reactive oxygen and nitrogen species are essential to several biological functions, from maintaining cellular homeostasis to driving disease progression and aging. As such, the precise modulation of cellular redox states has emerged as a promising medical strategy. In parallel, microneedle technology has rapidly advanced over the past two decades, offering minimally invasive, targeted, and painless delivery of therapeutics. This review presents a comprehensive analysis of the emerging field of redox-active microneedles (RAMs), a novel class of wearable medical devices with broad potential for treating infected wounds, cancer, psoriasis, diabetes, arthritis, hair loss, skin aging, and potentially many other conditions. By integrating redox biology with advanced microneedle-based drug delivery systems, RAMs are poised to transform personalized and precision medicine, an evolution supported by a growing number of patents, clinical trials, and rapid advancements in catalytic, photodynamic, and nanomedicine research.
11 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
AI-derived therapeutic development of a serotonin receptor-targeting drug for the treatment of opioid use disorder
The opioid epidemic has led to a devastating loss of life nationwide. Of those dependent on opioids, many individuals desire to quit or reduce use, but their efforts are often unsuccessful given the powerful reinforcing properties associated with opioid drugs, including fentanyl. Here, we developed a therapeutic drug based on an AI-based platform, which was rationally designed to identify markers of dysregulation from human drug user postmortem brain tissue. Two top candidate compounds, GATC-021 and GATC-1021, were synthesized and validated with in vitro screening for target specificity. Thereafter, the drug candidates were examined for their effectiveness in modulating opioid reinforcement and intake. GATC-1021 emerged as the most efficacious compound as it significantly decreased fentanyl intake in both male and female rats, without any notable side effects. GATC-1021 was further found to modulate gene expression patterns in addiction-relevant brain regions following fentanyl-induced alterations. These findings validate GATC's AI-based platform with its polypharmacy-focused drug development approach and further support the clinical potential of GATC-1021 as a promising therapeutic for those suffering from opioid use disorder.
7 Apr 2026
Read appraisal →Journal of ethnopharmacology
Trends and multidisciplinary research of torch ginger [Etlingera elatior (Jack) R.M.Sm.]: A systematic review.
ETHNOPHARMACOLOGICAL RELEVANCE: Etlingera elatior (Jack) R.M.Sm. (torch ginger) is a tropical plant traditionally used as a cut flower, culinary ingredient, and medicinal resource across Southeast Asia and other regions. However, integrated research on its multidisciplinary utilization remains limited. AIM OF THE STUDY: This study aims to provide a comprehensive systematic review of scientific publications on torch ginger, including analyses of contributing countries, institutions, plant parts, chemical constituents, and research fields. MATERIALS AND METHODS: Publications up to 2024 were retrieved from Scopus and screened using predefined exclusion criteria based on PRISMA principles. The data were analyzed to determine publication trends and bibliometric distributions. Phytochemical and pharmacological data were also extracted. RESULTS: Research on torch ginger began in 1997 and now involves fifteen contributing countries, with Indonesia, Malaysia, and Brazil as major contributors. Studies span fourteen research fields, with pharmacology and phytochemistry as dominant areas. The literature reports eighty-five major bioactive compounds. Extractives from most plant parts contain n-hexadecanoic acid, and essential oils from all parts contain dodecanal and 1-dodecanol. Most research focuses on flowers because their bioactive compounds show wide pharmacological potential, including antioxidant, anti-inflammatory, anti-aging, anti-allergic, anti-hyperuricemic, antidiabetic, anti-obesity, anticancer, antimicrobial, and anthelmintic activities. However, extracts from stems, rhizomes, and fruits demonstrate pharmacological potentials that exceed those of flowers and leaves. CONCLUSIONS: Torch ginger demonstrates remarkable pharmacological potential. Its ease of cultivation and versatile applications support its suitability for large-scale development. As a multifunctional industrial crop, it holds strong promise for zero-waste bioeconomic utilization. Further research must investigate plant parts other than the flower.
6 Apr 2026
Read appraisal →Intensive Care Med
Critical care management of the patient with pharmaceutical poisoning
Poisonings account for up to 10% of admissions to intensive care units and represent a common yet complex challenge in critical care. While most patients recover with supportive care alone, a significant proportion require targeted interventions and advanced organ support. This state-of-the-art review provides a practical, evidence-based guide to manage pharmaceutical poisoning in the intensive care unit, with emphasis on real-world applicability for intensivists. It highlights toxicant-specific considerations for early resuscitation, structured by the ABCDE approach. Essential principles of the diagnostic workup are also summarized. In addition, the review offers actionable guidance on when and how to escalate care, moving from supportive measures to specific interventions including gastrointestinal decontamination, extracorporeal toxicant elimination, antidote therapy, and rescue strategies such as intravenous lipid emulsion and extracorporeal membrane oxygenation. Key algorithms and decision pathways-such as the diagnostic evaluation, ABCDE-based stabilization, gastrointestinal decontamination, and extracorporeal elimination approaches-are presented with visually structured figures. A comprehensive table provides an overview of the most relevant antidotes used in critical care, including indications and dosing.
1 Dec 2025
Read appraisal →Cell
A generative deep learning approach to de novo antibiotic design
The antimicrobial resistance crisis necessitates structurally distinct antibiotics. While deep learning approaches can identify antibacterial compounds from existing libraries, structural novelty remains limited. Here, we developed a generative artificial intelligence framework for designing de novo antibiotics through two approaches: a fragment-based method to comprehensively screen >107 chemical fragments in silico against Neisseria gonorrhoeae or Staphylococcus aureus, subsequently expanding promising fragments, and an unconstrained de novo compound generation, each using genetic algorithms and variational autoencoders. Of 24 synthesized compounds, seven demonstrated selective antibacterial activity. Two lead compounds exhibited bactericidal efficacy against multidrug-resistant isolates with distinct mechanisms of action and reduced bacterial burden in vivo in mouse models of N. gonorrhoeae vaginal infection and methicillin-resistant S. aureus skin infection. We further validated structural analogs for both compound classes as antibacterial. Our approach enables the generative deep-learning-guided design of de novo antibiotics, providing a platform for mapping uncharted regions of chemical space.
1 Oct 2025
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