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Antibiotics
Special Issues
Novel Approaches to Prevent and Combat Antimicrobial Resistance
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Novel Approaches to Prevent and Combat Antimicrobial Resistance

A special issue of Antibiotics (ISSN 2079-6382).

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 468

Special Issue Editor

Dr. Xiaotao Jiang

E-Mail Website
Guest Editor
Microbiome Research Centre, St. George and Sutherland Clinical School, Department of Medicine, University of New South Wales, 18 High St., Kensington, Sydney, NSW 2052, Australia
Interests: microbiome; antimicrobial resistance; metagenomic next generation sequencing (mNGS); bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Antimicrobial resistance (AMR) is a critical global health challenge and hidden pandemic, killing millions every year. They threaten the effectiveness of existing treatments with drug-resistant infections. The rise in multidrug-resistant pathogens calls for urgent action to discover and implement innovative approaches to prevent their spread and treat them.

This Special Issue invites original research articles, reviews, and perspectives on novel approaches to tackling and mitigating AMR.

We welcome contributions in, but not limited to, the following areas:

  • Antimicrobial Peptides (AMPs): Discovery, mechanisms, and applications of novel AMPs as next-generation antimicrobials.
  • Novel Antibiotics Screening: Development and testing of new antibiotics, including novel compounds and natural product-based discovery, targeting resistant pathogens with new mechanisms of working.
  • Phage Therapy: The use of bacteriophages/phage cocktails and engineered phages to target drug-resistant infections.
  • Fecal Microbiota Transplantation (FMT): Exploring gut microbiome restoration as a strategy to reduce AMR burden.
  • Dietary and Functional Food Interventions: The role of diet, prebiotics, and probiotics in shaping the microbiome and reducing AMR risks and infectious risk.
  • AMR Prevention Strategies:
    • Antibiotic Stewardship: Optimizing antibiotic use in clinical, agricultural, and veterinary settings.
    • Infection Control Measures: Surveillance, hygiene, and public health strategies to prevent the spread of resistant pathogens.
    • Environmental and One Health Approaches: Understanding how water, soil, animals, and climate change contribute to AMR emergence and implementing strategies to reduce environmental reservoirs of resistance genes.

We encourage interdisciplinary studies that integrate microbiology, bioinformatics, clinical research, and public health to address AMR from a One Health perspective and focus on intervention and prevention.

Dr. Xiaotao Jiang
Guest Editor

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. Antibiotics 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 2900 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

  • antimicrobial peptides
  • novel antibiotics screening
  • phage therapy
  • fecal microbiota transplantation (FMT)
  • dietary and functional food interventions
  • AMR prevention strategies

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Published Papers (1 paper)

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Review

48 pages, 1173 KB  
Review
Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides
by Naveed Saleem, Naresh Kumar, Emad El-Omar, Mark Willcox and Xiao-Tao Jiang
Antibiotics 2025, 14(12), 1263; https://doi.org/10.3390/antibiotics14121263 (registering DOI) - 14 Dec 2025
Abstract
Antimicrobial resistance (AMR) has become a major health crisis worldwide, and it is expected to surpass cancer as one of the leading causes of death by 2050. Conventional antibiotics are struggling to keep pace with the rapidly evolving resistance trends, underscoring the urgent [...] Read more.
Antimicrobial resistance (AMR) has become a major health crisis worldwide, and it is expected to surpass cancer as one of the leading causes of death by 2050. Conventional antibiotics are struggling to keep pace with the rapidly evolving resistance trends, underscoring the urgent need for novel antimicrobial therapeutic strategies. Antimicrobial peptides (AMPs) function through diverse, often membrane-disrupting mechanisms that can address the latest challenges to resistance. However, the identification, prediction, and optimization of novel AMPs can be impeded by several issues, including extensive sequence spaces, context-dependent activity, and the higher costs associated with wet laboratory screenings. Recent developments in artificial intelligence (AI) have enabled large-scale mining of genomes, metagenomes, and quantitative species-resolved activity prediction, i.e., MIC, and de novo AMPs designed with integrated stability and toxicity filters. The current review has synthesized and highlighted progress across different discriminative models, such as classical machine learning and deep learning models and transformer embeddings, alongside graphs and geometric encoders, structure-guided and multi-modal hybrid learning approaches, closed-loop generative methods, and large language models (LLMs) predicted frameworks. This review compares models’ benchmark performances, highlighting AI-predicted novel hybrid approaches for designing AMPs, validated by in vitro and in vivo methods against clinical and resistant pathogens to increase overall experimental hit rates. Based on observations, multimodal paradigm strategies are proposed, focusing on identification, prediction, and characterization, followed by design frameworks, linking active-learning lab cycles, mechanistic interpretability, curated data resources, and uncertainty estimation. Therefore, for reproducible benchmarks and interoperable data, collaborative computational and wet lab experimental validations must be required to accelerate AI-driven novel AMP discovery to combat multidrug-resistant Gram-negative pathogens. Full article
(This article belongs to the Special Issue Novel Approaches to Prevent and Combat Antimicrobial Resistance)
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