Computational Approaches to Understanding Antimicrobial Resistance (AMR)

Antimicrobial resistance (AMR) poses a critical threat to veterinary medicine, livestock production, and companion animal health. Conventional culture-based susceptibility testing remains the diagnostic gold standard, but its throughput and mechanistic resolution are limited. Computational approaches now complement and extend traditional methods by enabling large-scale genomic surveillance, functional prediction of resistance determinants, and rational design of novel antimicrobials. This article provides an exhaustive, mechanism-focused review of computational strategies for understanding AMR in bacterial pathogens, with emphasis on veterinary species and zoonotic interfaces.

Genomic and Pangenomic Analyses

Whole-genome sequencing (WGS) of bacterial isolates has become the cornerstone of computational AMR research. Pangenome analyses define the core genome shared by all strains of a species and the accessory genome that often harbors mobile resistance elements. For example, pan-resistome and genomic plasticity analysis of Aeromonas species identified conserved vaccine targets and mobilized resistance genes in A. caviae, A. veronii, and A. hydrophila through reverse vaccinology [1]. Similarly, comparative and subtractive genomics of cystic fibrosis-associated multidrug-resistant Pandoraea sputorum revealed novel therapeutic targets by subtracting host homologous sequences from the pathogen proteome [2].

Genome-wide association studies (GWAS) for bacteria (microGWAS) have matured into robust pipelines that link genetic variants to phenotypic resistance. A dedicated computational pipeline for large-scale bacterial GWAS enables identification of single nucleotide polymorphisms (SNPs) and gene presence-absence patterns associated with elevated minimum inhibitory concentrations (MICs) [3]. In Acinetobacter baumannii, KEGG orthology-based machine learning revealed that functional annotations of metabolic and stress-response pathways are strong determinants of AMR phenotype classification [4]. The integration of protein functional domain analysis further enhances genotype-phenotype associations in comparative genomic studies of Pseudomonas aeruginosa [5].

Machine Learning for Resistance Prediction

Machine learning (ML) has been extensively applied to predict AMR from genomic data. Graph neural networks (GNNs) have emerged as powerful tools for capturing the relational structure of genomic features. The AMR-GNN framework uses a multi-representation graph neural network to enable genomic AMR prediction by encoding both sequence composition and functional annotations as graph nodes and edges [6]. Graph attention networks combined with protein-based language models have been specifically applied to predict efflux protein sequences in Porphyromonas gingivalis [7].

A systematic review of artificial intelligence applications for detecting and predicting AMR in Klebsiella pneumoniae highlighted the utility of deep learning and ensemble methods for classifying resistance using whole-genome and clinical metadata [8]. Multimodal interpretable data-driven models incorporating multivariate time series have been developed for early prediction of multidrug resistance, integrating patient (or host) clinical data with microbial genomic features [9]. Bayesian network models have been used to assess AMR patterns of Streptococcus suis isolated from swine production systems, demonstrating how probabilistic graphical models can capture conditional dependencies between resistance phenotypes and farm-level variables [10].

Protein language models (PLMs) represent a frontier in AMR prediction. These models, trained on large corpora of protein sequences, can predict antibiotic resistance genes and bacterial phenotypes directly from sequence data without requiring explicit alignment. Wang et al. demonstrated that PLM-derived embeddings outperform traditional k-mer methods for predicting resistance in diverse bacterial species [11]. Yagimoto et al. further showed that a PLM can predict the underlying resistance mechanism (e.g., enzymatic inactivation versus target modification) from sequence alone [12]. The ASAP-ML framework provides antibiotic susceptibility and antibiogram prediction using ML methods trained on phenotypic data [13].

Functional and Structural Bioinformatics

Beyond sequence-based prediction, computational approaches elucidate the molecular mechanisms of resistance and identify novel drug targets. Genome-scale metabolic modeling (GSMM) integrates genomic annotations with biochemical network reconstructions to simulate metabolic fluxes under antibiotic pressure. GSMM has been applied to identify niche-specific metabolic phenotypes that can serve as antimicrobial targets, as demonstrated in a study linking metabolic dependencies of pathogens to potential drug vulnerabilities [14]. In Mycobacterium smegmatis, combined in vitro and in silico approaches evaluated the antimycobacterial and biofilm inhibition activity of asiatic acid against dual targets, using molecular docking and molecular dynamics simulations [15].

Structural bioinformatics plays a key role in understanding how mutations confer resistance. Computational structure-guided approaches simulated the binding of delamanid and pretomanid to mycobacterial F420 redox cycling proteins, identifying key residues whose mutation abrogates drug activation [16]. In Chlamydia pneumoniae, in silico analysis assessed the functional and structural impacts of rpoB mutations on rifampicin sensitivity [17]. Integrated virtual screening and compound generation targeting the H275Y mutation in neuraminidase (a viral example, but the methodology applies to bacterial targets) showcased how computational libraries can be screened for activity against resistance variants [18].

Reverse vaccinology and subtractive genomics are also applied to discover vaccine candidates that could reduce antimicrobial use. For Aeromonas species, the pan-resistome analysis included reverse vaccinology to identify surface-exposed proteins conserved across resistant strains [1]. For Gardnerella vaginalis, a comparative in silico investigation identified drug targets by prioritizing essential genes absent in the host [19]. Drug repurposing screens using computational docking have identified FDA-approved molecules with activity against P. aeruginosa, addressing resistance through known safety profiles [20].

Metagenomics and Resistome Surveillance

Metagenomic sequencing directly from environmental, fecal, or tissue samples circumvents the need for culture and captures the entire resistome. The TRACE framework reconstructs fragmented microbial landscapes for high-resolution AMR surveillance by using metagenomic assembly and binning to contextualize resistance genes within their genomic neighborhoods [21]. Metagenomic analysis of antimicrobial resistance genes in domestic canines revealed a diverse array of resistance determinants in the canine gut, highlighting the potential for pet-to-human transmission of resistance elements [22]. In swine wastewater treatment systems, metagenomic approaches have been systematically reviewed for quantification of antibiotic resistance genes, showing that treatment processes can reduce but not eliminate the resistome [23].

The CZ ID platform (an open-source, cloud-based metagenomics pipeline) enables simultaneous detection of pathogens and antimicrobial resistance genes from shotgun sequencing data, making resistome analysis accessible to veterinary diagnostic laboratories [24]. Targeted sequencing using CRISPR-Cas9 enrichment coupled with long-read sequencing has been used to selectively capture Enterobacterales bacteria from complex samples, improving the detection of plasmid-borne resistance genes [25].

Plasmid Dynamics and Horizontal Gene Transfer

Plasmids are major vectors for AMR dissemination. Computational phylodynamic inference quantifies plasmid movement within bacterial populations. For Shigella species, phylodynamic models estimated the rate of plasmid transfer between lineages, revealing that certain plasmid-background combinations are more stable and thus more likely to spread resistance [26]. The concept of "plasmid-bacteria associations in the clinical context" has been reviewed extensively, emphasizing that computational models must account for the fitness cost of plasmid carriage and the genetic background of the host [27].

A targeted Hi-C approach has been developed to detect rare plasmid hosts by capturing physical contacts between plasmids and bacterial chromosomes, allowing computational reconstruction of which bacteria carry which plasmids in mixed communities [28]. Metagenomic assembly graphs can also be used to link plasmid contigs to chromosomal bins, as facilitated by the TRACE framework [21].

Ecological and Evolutionary Modeling

AMR is an ecological and evolutionary phenomenon. Evolutionary accumulation modeling using machine learning can infer and predict the dynamics of multi-drug resistance acquisition in bacterial populations [29]. Linking spatial drug heterogeneity to microbial growth dynamics, computational models combined with microfluidic experiments demonstrate how gradients of antibiotic concentration can select for different resistance mechanisms depending on spatial structure [30].

Population ecology models combined with quantitative microbial risk assessment (QMRA) have been developed for antibiotic-resistant E. coli in recreational waters, predicting human and animal exposure risks based on environmental concentrations [31]. For Porphyromonas gingivalis, neural networks have been used to classify resistance sequences, linking evolutionary signals to functional efflux pump activity [32].

Network-Based and Systems Biology Approaches

Network-based methods integrate multi-omic data to study AMR. Bayesian networks provide probabilistic graphical models that capture causal relationships between genes, pathways, and resistance phenotypes. These have been applied to Streptococcus suis AMR patterns in swine [10] and more broadly in systems biology for veterinary inference. Microbe-drug association prediction models based on adaptive network fusion of structural-topological information with integration strategy can predict novel drug-microbe interactions, including those relevant to resistance [33].

Graph neural networks, as mentioned, are a specific class of deep learning models that operate on graph-structured data. AMR-GNN [6] and multi-scale feature landscapes that predict AMR across ESKAPE pathogens (a set of nosocomial pathogens, but the methodology applies broadly) [34] represent a shift toward using relational inductive biases in AMR prediction.

Computational Antimicrobial Peptide Discovery

The discovery of novel antimicrobial peptides (AMPs) has been accelerated by computational methods. Recent advances in big data, modeling, and AI have ushered a new era of AMP development, where machine learning models trained on known AMP databases can generate and optimize sequences with desired activity profiles [35]. Machine learning-driven discovery of highly selective antifungal peptides containing non-canonical beta-amino acids demonstrates the power of computational design [36]. Functional antimicrobial peptide-loaded scaffolds for infected bone defect treatment have been developed using AI combined with multidimensional printing [37].

Drug Target Identification and Mode of Action

Understanding the mode of action of antibiotics and how resistance arises is essential for rational drug design. Merging multi-omics with proteome integral solubility alteration (PISA) can reveal antibiotic mode of action by identifying proteins that change solubility upon drug treatment [38]. Targeting bacterial RNA polymerase using simulations and machine learning has been applied to design inhibitors for drug-resistant pathogens [39]. In silico and in vitro comparative analysis of Acinetobacter baumannii clinical isolates combined computational predictions with phenotypic testing to validate resistance mechanisms [40].

For fungal and parasitic pathogens, computational approaches are equally relevant. Predicting antifolate resistance in the unculturable fungal pathogen Pneumocystis jirovecii relied on structural modeling of dihydrofolate reductase mutations [41]. For Trypanosoma brucei and other protozoan parasites, molecular docking and dynamics have been used to assess resistance to existing drugs [42].

Data Integration and Visualization

The complexity of AMR data demands integrated platforms. The WORKFLOW below illustrates a typical computational AMR analysis pipeline from sample to actionable insight.

flowchart TD
    A[Clinical / Environmental Sample], > B[Culture or Direct DNA Extraction]
    B, > C{Sequencing Approach}
    C, > D[Whole-Genome Sequencing (WGS)]
    C, > E[Metagenomic Shotgun Sequencing]
    C, > F[Targeted Amplicon Sequencing]
    D, > G[Assembly and Annotation]
    E, > H[Metagenomic Assembly / Binning]
    F, > I[Amplicon Clustering]
    G, > J[Detect AMR Genes / Mutations]
    H, > J
    I, > J
    J, > K[Database Search: CARD, ResFinder, NCBI AMRFinder]
    K, > L[Phenotype Prediction: ML Models]
    L, > M[Statistical Analysis / GWAS]
    M, > N[Interpretation and Reporting]
    L, > O[Phylogenetic / Phylodynamic Analysis]
    O, > N

The following table summarizes key computational approaches and their veterinary applications.

| Approach | Input Data | Key Algorithms / Tools | Example Veterinary Application | |, - |, - |, - |, - | | Genome-wide association (GWAS) | WGS variants, phenotypes | microGWAS, PLINK | Identifying SNPs linked to resistance in S. suis [10, 3] | | Machine learning (ML) | Genomic features, metadata | Random forest, GNN, PLM | Predicting AMR in K. pneumoniae [8, 6] | | Protein language model (PLM) | Protein sequences | Transformer-based embeddings | Predicting resistance mechanism [11, 12] | | Metabolic modeling (GSMM) | Genome annotation | COBRA toolbox | Identifying target vulnerabilities in P. aeruginosa [43, 14] | | Molecular docking | Protein structure, ligands | AutoDock, GOLD | Repurposing drugs against P. aeruginosa [20] | | Metagenomics | Shotgun reads | TRACE, CZ ID | Resistome profiling in canine feces [22, 21] | | Phylodynamics | WGS, temporal data | BEAST, phydelity | Quantifying plasmid spread in Shigella [26] | | Bayesian networks | Phenotypic + farm data | bnlearn | Mapping AMR patterns in swine production [10] |

Limitations and Future Directions

Computational approaches face several limitations. Predictive models are highly dependent on the quality and representativeness of training data. Many resistance determinants remain uncharacterized, and silent or cryptic resistance genes may be missed by homology-based methods. Machine learning models, particularly deep learning, can suffer from overfitting and lack of interpretability. However, interpretable ML frameworks are being developed. Evolutionary modeling must account for the dynamic nature of horizontal gene transfer and the impact of compensatory mutations.

Future developments will integrate multi-omics data (transcriptomics, proteomics, metabolomics) with genomic predictions to achieve a systems-level understanding of AMR. The incorporation of spatial and temporal metadata into computational models will enable real-time surveillance and outbreak prediction. As computational power increases and sequencing costs decline, these approaches will become standard in veterinary diagnostic laboratories, supporting evidence-based antimicrobial stewardship and One Health surveillance.

References

[1] Tristão CLAM, Felice AG, Prado LCDS, et al. Pan-resistome and genomic plasticity analysis of antibiotic resistance in Aeromonas: new vaccine targets for A. caviae, A. veronii and A. hydrophila through reverse vaccinology. Open Biol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42191148/

[2] Mia R, Mozumder A, Das A, et al. Comparative and subtractive genomics reveals novel therapeutic targets in cystic fibrosis-associated multidrug-resistant Pandoraea sputorum. Diagn Microbiol Infect Dis. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42013487/

[3] Burgaya J, Damaris BF, Fiebig J, et al. microGWAS: a computational pipeline to perform large-scale bacterial genome-wide association studies. Microb Genom. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/39932497/

[4] Zheng Z, Jiang B, Shenkutie AM, et al. KEGG orthology-based machine learning reveals functional determinants of antimicrobial resistance in Acinetobacter baumannii. Microbiol Spectr. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42095666/

[5] Bianconi I, Esposito A, Piazza S, et al. Protein functional domain analysis enhances genotype-phenotype associations in comparative genomic studies of Pseudomonas aeruginosa. Front Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40842837/

[6] Nguyen HA, Peleg AY, Wisniewski JA, et al. AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction. Nat Commun. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41792137/

[7] Yadalam PK, Natarajan PM, Shetty N, et al. Analyzing and exploring Graph Attention Networks and protein-based language models for predicting Porhyromonas gingivalis resistant efflux protein sequences. Dent Med Probl. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40372388/

[8] Aggarwal RV, Shah N, Wong JJE, et al. Artificial Intelligence for Antimicrobial Resistance Detection and Prediction in Klebsiella pneumoniae: A Systematic Review of Clinical Microbiology Applications. Infect Drug Resist. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42261281/

[9] Escudero-Arnanz Ó, Martínez-Agüero S, Martín-Palomeque P, et al. Multimodal interpretable data-driven models for early prediction of multidrug resistance using multivariate time series. Health Inf Sci Syst. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40352427/

[10] Rupasinghe R, Morgan Bustamante BL, Robbins RC, et al. Bayesian network models to assess antimicrobial resistance patterns of Streptococcus suis isolated from swine production systems in the United States between 2014-2021. PLoS Comput Biol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41886440/

[11] Wang B, Meng R, Li Z, et al. Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models. Front Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40988849/

[12] Yagimoto K, Hosoda S, Sato M, et al. Prediction of antibiotic resistance mechanisms using a protein language model. Bioinformatics. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39254573/

[13] Topcu D, Akcapinar Sezer E. ASAP-ML: Antibiotic Susceptibility and Antibiogram Prediction With Machine Learning Methods. IEEE Trans Comput Biol Bioinform. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41247898/

[14] Glass EM, Dillard LR, Kolling GL, et al. Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens. PLoS Biol. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39556591/

[15] Singh K, Upadhyay TK, Bano A, et al. In Vitro and In Silico Approaches for the Evaluation of Antimycobacterial and Biofilm Inhibition Activity of Asiatic Acid Against Dual Targets of Mycobacterium smegmatis. Curr Top Med Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41968692/

[16] Chakraborty G, Kolpe MS, Nath IVA, et al. Computational structure-guided approach to simulate delamanid and pretomanid binding to mycobacterial F420 redox cycling proteins: identification of key determinants of resistance. J Biomol Struct Dyn. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40326994/

[17] Esskhayry S, Benamri I, Lamzouri A, et al. Adoption of an in-silico analysis approach to assess the functional and structural impacts of rpoB-encoded protein mutations on Chlamydia pneumoniae sensitivity to antibiotics. BMC Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40102727/

[18] Khan WH, Khan N, Tembhre MK, et al. Integrated virtual screening and compound generation targeting H275Y mutation in the neuraminidase gene of oseltamivir-resistant influenza strains. Mol Divers. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40085404/

[19] Riaz R, Khan K, Aghayeva S, et al. Combatting antibiotic resistance in Gardnerella vaginalis: A comparative in silico investigation for drug target identification. PLoS One. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40073044/

[20] Chatterjee D, Sivashanmugam K. Computational approach towards repurposing of FDA approved drug molecules: strategy to combat antibiotic resistance conferred by Pseudomonas aeruginosa. J Biomol Struct Dyn. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/39580714/

[21] Bai X, Zhang J, Jiang Z, et al. TRACE: reconstructing fragmented microbial landscapes for high-resolution antimicrobial resistance surveillance. Front Microbiol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41924476/

[22] Craddock HA, Motro Y, Winner KM, et al. Metagenomic analysis of antimicrobial resistance genes in domestic canines. One Health. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41953110/

[23] Torres MC, Breyer GM, da Silva MERJ, et al. Metagenomic approaches for the quantification of antibiotic resistance genes in swine wastewater treatment system: a systematic review. Mol Biol Rep. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40788461/

[24] Lu D, Kalantar KL, Glascock AL, et al. Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID platform. Genome Med. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40329334/

[25] Cottingham H, Judd LM, Wisniewski JA, et al. Targeted sequencing of Enterobacterales bacteria using CRISPR-Cas9 enrichment and Oxford Nanopore Technologies. mSystems. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/39772804/

[26] Müller NF, Wick RR, Judd LM, et al. Quantifying plasmid movement in drug-resistant Shigella species using phylodynamic inference. PLoS Pathog. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41325432/

[27] Toribio-Celestino L, San Millan A. Plasmid-bacteria associations in the clinical context. Trends Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40374465/

[28] Castañeda-Barba S, Ridenhour BJ, Top EM, et al. Detection of rare plasmid hosts using a targeted Hi-C approach. ISME Commun. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40161467/

[29] Renz J, Dauda KA, Aga ONL, et al. Evolutionary accumulation modeling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance. mBio. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40396716/

[30] Hu Z, Wu Y, Freire T, et al. Linking spatial drug heterogeneity to microbial growth dynamics in theory and experiment. PLoS Comput Biol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41557766/

[31] Heida A, Hamilton MT, Gambino J, et al. Population Ecology-Quantitative Microbial Risk Assessment (QMRA) Model for Antibiotic-Resistant and Susceptible E. coli in Recreational Water. Environ Sci Technol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40008406/

[32] Yadalam PK, Anegundi RV, Natarajan PM, et al. Neural Networks for Predicting and Classifying Antimicrobial Resistance Sequences in Porphyromonas gingivalis. Int Dent J. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40618714/

[33] Wang L, Zhang B, Wu H, et al. Microbe-Drug Association Prediction Model Based on Adaptive Network Fusion of Structural-Topological Information With Integration Strategy. IEEE Trans Comput Biol Bioinform. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40811312/

[34] Ghosh A, Brenner EP, Vang CK, et al. From sequence to signature: Machine learning uncovers multiscale feature landscapes that predict AMR across ESKAPE pathogens. bioRxiv. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41279520/

[35] Ibisanmi TA, Jiang X, Willcox M, et al. Recent advances in computational antimicrobial peptide discovery through big data, modeling, and artificial intelligence and their interplay in ushering the next golden era of drug development. Front Bioinform. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41923798/

[36] Chang DH, Richardson JD, Lee MR, et al. Machine learning-driven discovery of highly selective antifungal peptides containing non-canonical β-amino acids. Chem Sci. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40028619/

[37] Li M, Zhao P, Wang J, et al. Functional antimicrobial peptide-loaded 3D scaffolds for infected bone defect treatment with AI and multidimensional printing. Mater Horiz. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/39484845/

[38] Maity R, Zhang X, Liberati FR, et al. Merging multi-omics with proteome integral solubility alteration unveils antibiotic mode of action. Elife. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39329363/

[39] Goonetilleke EC, Huang X. Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens. Biochemistry. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40014017/

[40] Scarrone M, Turner D, Dion M, et al. In silico and in vitro comparative analysis of 79 Acinetobacter baumannii clinical isolates. Microbiol Spectr. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40377313/

[41] Rouleau FD, Dubé AK, Pageau A, et al. Predicting antifolate resistance in the unculturable fungal pathogen Pneumocystis jirovecii. PLoS Genet. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42201980/

[42] Venkatachalam S, Muralidharan N, Pandian R, et al. Integrative Computational Approaches for Understanding Drug Resistance in HIV-1 Protease Subtype C. Viruses. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40573441/

[43] Tao J, Lin YW, Zhong L, et al. Genome-scale metabolic modelling in antimicrobial pharmacology: Present and future. Adv Drug Deliv Rev. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40816653/

[44] Lenka S, Mir SA, Meher RK, et al. Biological assessment of Coccinia grandis leaf and Lupeol against β-lactam resistant Klebsiella pneumoniae through integrated in-silico and in-vitro studies. Sci Rep. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41760715/

[45] Rosignoli S, Lustrino E, Shevchuk O, et al. Bioinformatics-Driven, Plant-Based Antibiotic Research Against Quorum Sensing and Biofilm Formation in Pseudomonas aeruginosa and Escherichia coli Multiresistant Microbes. Biomolecules. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41750267/

[46] Mim TJ, Aqib AI, Noman AA, et al. Klebsiella pneumoniae-induced pneumonia: Pathogenesis, immune interactions, and antimicrobial resistance in a global context. Res Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41241222/

[47] Sinoliya P, Niraj RRK, Sharma V. Cracking the Code: How Nano-Informatics is Crafting Intelligent Nano-Weapons to Outsmart Multiple Drug Resistance (MDR). Pharm Nanotechnol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40947736/

[48] Puente A, Cobo-Díaz JF, Oliveira M, et al. Diverse Acinetobacter in retail meat: a hidden vector of novel species and antimicrobial resistance genes, including plasmid-borne bla(OXA-58), mcr-4.3 and tet(X3). Int J Food Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40513431/

[49] Li J, Chang J, Ma J, et al. Genome-based assessment of antimicrobial resistance of Escherichia coli recovered from diseased swine in eastern China for a 12-year period. mBio. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40243369/

[50] Barrios Steed D, Koundakjian D, Harris AD, et al. Leveraging strain competition to address antimicrobial resistance with microbiota therapies. Gut Microbes. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40195644/

[51] Founou LL, Lawal OU, Djiyou A, et al. Enable, empower, succeed: a bioinformatics workshop Harnessing open web-based tools for surveillance of bacterial antimicrobial resistance. BMC Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40102762/

[52] Yönden Z, Reshadi S, Hayati AF, et al. Reviewing on AI-Designed Antibiotic Targeting Drug-Resistant Superbugs by Emphasizing Mechanisms of Action. Drug Dev Res. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/39932058/

[53] Kitchens SR, Wang C, Price SB. Bridging Classical Methodologies in Salmonella Investigation with Modern Technologies: A Comprehensive Review. Microorganisms. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39597638/

[54] Sakagianni A, Koufopoulou C, Koufopoulos P, et al. The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review. Antibiotics (Basel). 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39452262/

[55] Udaondo Z, Ramos JL, Abram K. Unraveling the genomic diversity of the Pseudomonas putida group: exploring taxonomy, core pangenome, and antibiotic resistance mechanisms. FEMS Microbiol Rev. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39390673/

[56] Polemis M, Sideroglou T, Chrysostomou A, et al. First Data on WGS-Based Typing and Antimicrobial Resistance of Human Salmonella Enteritidis Isolates in Greece. Antibiotics (Basel). 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39200008/

[57] Li J, Jin X, Jiao Z, et al. Designing antibacterial materials through simulation and theory. J Mater Chem B. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39189825/

[58] Haj Hasan A, Preet G, Astakala RV, et al. Antibacterial activity of natural flavones against bovine mastitis pathogens: in vitro, SAR analysis, and computational study. In Silico Pharmacol. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39184231/