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Microorganisms
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Artificial Intelligence as a Tool for Combating Antimicrobial Resistance
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Artificial Intelligence as a Tool for Combating Antimicrobial Resistance

A special issue of Microorganisms (ISSN 2076-2607). This special issue belongs to the section "Microbial Biotechnology".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4056

Special Issue Editors

Dr. Sergi Maicas

E-Mail Website
Guest Editor
Departament de Microbiologia i Ecologia, Facultat de Biologia, Universitat de València, Burjassot, Spain
Interests: genomics; biotechnology; microbiology; microbial biotechnology; fermentation; microbial culture; fungi; yeasts; Saccharomyces cerevisiae; wine; yeast fermentation; winemaking; wine chemistry; enology; microbial biochemistry; wine microbiology; microbiological procedures
Special Issues, Collections and Topics in MDPI journals
Dr. Jaume Segura-Garcia

E-Mail Website
Guest Editor
Department of Computer Science, ETSE, Universitat de València, Burjassot, 46100 Valencia, Spain
Interests: IoT; AI/DL/ML; UAVs; computer vision; signal processing; acoustics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Antimicrobial resistance (AMR) is a growing global threat that undermines the effectiveness of existing treatments, leading to increased mortality and economic burdens. Artificial intelligence (AI) presents a transformative opportunity to combat AMR through enhanced surveillance, accelerated drug discovery, and optimized antimicrobial usage. By strategically leveraging AI technologies, we can develop proactive solutions to mitigate the impact of AMR and strengthen global healthcare resilience. AI can enhance AMR surveillance by analyzing vast datasets from clinical records, laboratory results, and genomic databases. Machine learning algorithms can identify patterns, predict resistance trends, and provide real-time alerts for emerging threats. This capability enables early intervention and improved decision-making in healthcare settings. AI-driven models can expedite drug discovery by identifying potential antimicrobial candidates more efficiently than traditional methods. By utilizing molecular dynamics simulations and AI-powered screening techniques, researchers can discover novel drugs, optimize existing compounds, and reduce the time and cost required to bring new antimicrobials to market. AI can optimize antimicrobial prescribing practices by providing clinicians with decision support systems that recommend appropriate treatments based on patient-specific data. This reduces the misuse of antibiotics, slows resistance development, and improves patient outcomes. AI-driven predictive analytics can also assist in identifying cases where alternative therapies may be more effective, further promoting responsible antimicrobial use. To effectively harness AI for AMR mitigation, a structured and collaborative approach is necessary: engaging governments, healthcare providers, researchers, and industry partners to align efforts and facilitate data sharing, establishing a consortium to develop standardized data collection and sharing protocols, ensuring interoperability and consistency across platforms, and providing hands-on training in AI tools, including molecular dynamics, Python, and R, to equip researchers and healthcare professionals with the necessary skills. By integrating AI into AMR strategies, we expect to achieve enhanced surveillance with improved accuracy and timeliness in tracking AMR trends, enabling proactive interventions, accelerated drug discovery with the faster identification and development of novel antimicrobials, reducing costs and improving market readiness, optimized antimicrobial use through a reduction in inappropriate antibiotic prescriptions, slowing the rate of resistance development, and strengthened global collaboration facilitated by AI-driven insights and innovations. AI has the potential to revolutionize the fight against AMR by enhancing data analysis, expediting drug development, and promoting responsible antimicrobial use. Through interdisciplinary collaboration and the adoption of AI-driven solutions, we can make significant strides in addressing this global health challenge.

This Special Issue will tackle, but is not limited to, the following issues:

  • AI/ML applications for AMR;
  • AI applications considering MD or docking (in datasets);
  • LLM applications for AMR;
  • AI-driven models for drug discovery;
  • AI-driven models for compound property detection/discovery;
  • Novel augmentation techniques applied in AMR.

As Guest Editors of this Special Issue, we look forward to reviewing your submissions.

Dr. Sergi Maicas
Dr. Jaume Segura-Garcia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microorganisms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI
  • AMR
  • drug discovery
  • datasets
  • antibiotics

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Published Papers (4 papers)

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Research

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12 pages, 215 KB  
Article
Handling Missing Race and Ethnicity in an EHR-Based Study Through Integration of Individual Measures and Neighborhood Sociodemographic and Socioeconomic Measures
by Chaohua Li, Abdolreza Mosaddegh, Lilly Immergluck, Samuel Owusu, Sadia Firoza Chowdhury and Peter Baltrus
Microorganisms 2026, 14(3), 662; https://doi.org/10.3390/microorganisms14030662 - 14 Mar 2026
Viewed by 335
Abstract
Race and ethnicity are frequently missing in electronic health records (EHRs), where excluding these records can bias pediatric research and disparity estimates. Imputing missing values may reduce this bias but can perform unevenly across methods and subgroups, especially for smaller or heterogeneous categories. [...] Read more.
Race and ethnicity are frequently missing in electronic health records (EHRs), where excluding these records can bias pediatric research and disparity estimates. Imputing missing values may reduce this bias but can perform unevenly across methods and subgroups, especially for smaller or heterogeneous categories. We compared four approaches—logistic regression, random forest, k-nearest neighbors (KNN), and multiple imputation by chained equations (MICE)—to impute missing race (Black/White/Other) and ethnicity (Hispanic/Non-Hispanic) using individual- and census-tract-level sociodemographic measures. We analyzed 5309 children (<19 years) treated for Staphylococcus aureus infections at two pediatric hospitals in metropolitan Atlanta (2002–2015). The performance was evaluated on a held-out test set (n = 554) using accuracy and weighted F1. For race, logistic regression and KNN performed best (accuracy/weighted F1: 0.838/0.822 and 0.839/0.823), followed by random forest (0.798/0.787), with MICE being the lowest (0.736/0.743). For ethnicity, KNN achieved the highest accuracy (0.912) and random forest the highest weighted F1 (0.895) (logistic regression 0.901/0.876; random forest 0.904/0.895; KNN 0.912/0.887; MICE 0.866/0.864). Performance was the lowest for Hispanic ethnicity and the “Other” race category, consistent with the class imbalance. Imputation performance depends on the demographic attribute and modeling approach; subgroup-specific evaluation is essential when imputing race and ethnicity in pediatric EHR research. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Tool for Combating Antimicrobial Resistance)
21 pages, 1234 KB  
Article
ReShuffle-MS: Region-Guided Data Augmentation Improves Artificial Intelligence-Based Resistance Prediction in Escherichia coli from MALDI-TOF Mass Spectrometry
by Dongbo Dai, Chenyang Huang, Junjie Li, Xiao Wei, Shengzhou Li, Qiong Wu and Huiran Zhang
Microorganisms 2026, 14(1), 177; https://doi.org/10.3390/microorganisms14010177 - 13 Jan 2026
Viewed by 547
Abstract
Rapid antimicrobial resistance (AMR) prediction from MALDI-TOF mass spectrometry (MS) remains challenging, particularly when training artificial intelligence (AI) models under small-sample constraints. Performance is often hampered by the high dimensionality of spectral data and the subtle nature of resistance-related signals: full-spectrum approaches risk [...] Read more.
Rapid antimicrobial resistance (AMR) prediction from MALDI-TOF mass spectrometry (MS) remains challenging, particularly when training artificial intelligence (AI) models under small-sample constraints. Performance is often hampered by the high dimensionality of spectral data and the subtle nature of resistance-related signals: full-spectrum approaches risk overfitting to high-dimensional noise, whereas peak-selection strategies risk discarding structurally informative, low-intensity signals. Here, we propose ReShuffle-MS, a region-guided data augmentation framework for MS data. Each spectrum is partitioned into a Main Discriminative Region (MDR) and a Peripheral Peak Region (PPR). By recombining signals within the PPR across samples of the same class while keeping the MDR intact, ReShuffle-MS generates structure-preserving augmented samples. On a clinical dataset for Escherichia coli (E. coli) levofloxacin resistance prediction, ReShuffle-MS delivered significant and consistent performance gains. It improved the average accuracy of classical machine learning models by 3.7% and enabled a one-dimensional convolutional neural network (CNN) to achieve 83.25% accuracy and 97.28% recall. Visualization using Grad-CAM revealed a shift from sparse, peak-dependent attention toward broader and more meaningful spectral patterns. Validation on the external DRIAMS-C dataset for ceftriaxone resistance further demonstrated that the method generalizes to a distinct laboratory setting and a different antibiotic target. These findings suggest that ReShuffle-MS can enhance the robustness and clinical utility of AI-based AMR prediction from routinely acquired MALDI-TOF spectra. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Tool for Combating Antimicrobial Resistance)
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Review

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36 pages, 2033 KB  
Review
Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi
by Calina Wu-Mo, Ariana Flores-González, Jezrael Meléndez-Delgado, Valerie Ortiz-Gómez, Héctor Meléndez-González and Rafael Maldonado-Hernández
Microorganisms 2026, 14(3), 591; https://doi.org/10.3390/microorganisms14030591 - 6 Mar 2026
Cited by 1 | Viewed by 1148
Abstract
Antimicrobial resistance (AMR) poses an escalating global health crisis driven by multidrug-resistant ESKAPE pathogens and emerging fungal threats such as Candida auris (C. auris). In response to this urgent need for new therapeutic strategies, antimicrobial peptides (AMPs) represent a mechanistically distinct [...] Read more.
Antimicrobial resistance (AMR) poses an escalating global health crisis driven by multidrug-resistant ESKAPE pathogens and emerging fungal threats such as Candida auris (C. auris). In response to this urgent need for new therapeutic strategies, antimicrobial peptides (AMPs) represent a mechanistically distinct alternative to conventional antibiotics due to their membrane-targeting mechanisms and a reduced propensity for resistance development; however, clinical translation has been hindered by toxicity, instability and manufacturing constraints. Recent advances in artificial intelligence (AI) are reshaping AMP discovery and optimization. Machine learning (ML), deep learning (DL) and transformer-based protein language models now enable improved prediction of antimicrobial activity, selectivity, protease stability and host toxicity. Generative approaches, including variational autoencoders, diffusion models and reinforcement learning, facilitate de novo multi-objective peptide design and pathogen-directed optimization against resistant bacteria and multidrug-resistant fungal pathogens. Integrated design–test–learn pipelines are accelerating iterative peptide engineering by tightly coupling computational prediction with experimental validation. Clinically used peptide-derived antibiotics such as polymyxins and daptomycin demonstrate the therapeutic feasibility of peptide-based antimicrobials, while investigational peptides, including pexiganan, illustrate ongoing translational progress. Although no fully AI-designed AMP has yet achieved regulatory approval, the accelerating convergence of computational modeling and experimental validation suggests a rapidly evolving translational landscape. Advancing scalable, surveillance-informed AI frameworks that integrate resistance data, predictive safety modeling and delivery optimization will be essential to accelerate the clinical translation of next-generation, multi-objective AMPs against high-risk resistant pathogens. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Tool for Combating Antimicrobial Resistance)
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30 pages, 4048 KB  
Review
Artificial Intelligence as a Catalyst for Antimicrobial Discovery: From Predictive Models to De Novo Design
by Romaisaa Boudza, Salim Bounou, Jaume Segura-Garcia, Ismail Moukadiri and Sergi Maicas
Microorganisms 2026, 14(2), 394; https://doi.org/10.3390/microorganisms14020394 - 6 Feb 2026
Cited by 1 | Viewed by 1356
Abstract
Antimicrobial resistance represents one of the most critical global health challenges of the 21st century, urgently demanding innovative strategies for antimicrobial discovery. Traditional antibiotic development pipelines are slow, costly, and increasingly ineffective against multidrug-resistant pathogens. In this context, recent advances in artificial intelligence [...] Read more.
Antimicrobial resistance represents one of the most critical global health challenges of the 21st century, urgently demanding innovative strategies for antimicrobial discovery. Traditional antibiotic development pipelines are slow, costly, and increasingly ineffective against multidrug-resistant pathogens. In this context, recent advances in artificial intelligence have emerged as transformative tools capable of accelerating antimicrobial discovery and expanding accessible chemical and biological space. This comprehensive review critically synthesizes recent progress in AI-driven approaches applied to the discovery and design of both small-molecule antibiotics and antimicrobial peptides. We examine how machine learning, deep learning, and generative models are being leveraged for virtual screening, activity prediction, mechanism-informed prioritization, and de novo antimicrobial design. Particular emphasis is placed on graph-based neural networks, attention-based and transformer architectures, and generative frameworks such as variational autoencoders and large language model-based generators. Across these approaches, AI has enabled the identification of structurally novel compounds, facilitated narrow-spectrum antimicrobial strategies, and improved interpretability in peptide prediction. However, significant challenges remain, including data scarcity and imbalance, limited experimental validation, and barriers to clinical translation. By integrating methodological advances with a critical analysis of the current limitations, this review highlights emerging trends and outlines future directions aimed at bridging the gap between in silico discovery and real-world therapeutic development. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Tool for Combating Antimicrobial Resistance)
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