AI in Developing Diagnostics, Antiviral Therapies, Antimicrobial Resistance, and Vaccines for Viral Diseases in Humans, Animals, and Birds

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 754

Special Issue Editor


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Guest Editor
College of Veterinary Medicine, Long Island University, Brooklyn, NY, USA
Interests: molecular biology of coronaviruses; coronavirus/host interaction; One Health; roles of small RNA molecules in viral pathogenesis; development of novel vaccines against coronaviruses of livestock and avian species
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Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into virology is transforming how we diagnose, treat, and prevent viral diseases across humans, animals, and birds. AI-driven approaches, including machine learning, deep learning, and computational modeling, enable rapid detection of pathogens, prediction of viral evolution, and the development of novel antiviral therapies and vaccines. The COVID-19 pandemic has demonstrated the urgent need for such technologies, as AI played a crucial role in drug repurposing, vaccine development, and outbreak prediction during this period.

We are pleased to invite researchers, clinicians, and industry experts to contribute to this Special Issue, which aims to highlight cutting-edge AI applications in virology. This collection will serve as a platform for discussing innovative strategies and fostering collaborations in AI-driven diagnostics, antiviral drug discovery, and vaccine development.

You are invited to submit original research and review articles related to the following topics:

  • AI-driven molecular diagnostics and biosensors for viral detection
  • Machine learning applications in antiviral drug discovery
  • AI-assisted vaccine design and immunogenicity prediction
  • Predictive modeling of viral evolution and spillover risks
  • AI-enhanced genomic surveillance and outbreak forecasting
  • Computational approaches to host–virus interactions and immune response modulation
  • AI-based precision medicine strategies for viral infections
  • Ethical considerations and challenges in AI applications in virology

We look forward to receiving your contributions.

Prof. Dr. Maged Gomaa Hemida
Guest Editor

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Keywords

  • artificial intelligence
  • viral diagnostics
  • antiviral drug discovery
  • vaccine development
  • genomic surveillance
  • machine learning
  • computational virology
  • predictive modeling
  • immune response prediction
  • One Health

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Published Papers (1 paper)

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Review

24 pages, 4523 KB  
Review
Artificial Intelligence Driven Framework for the Design and Development of Next-Generation Avian Viral Vaccines
by Muddapuram Deeksha Goud, Elisa Ramos, Abid Ullah Shah and Maged Gomaa Hemida
Microorganisms 2025, 13(10), 2361; https://doi.org/10.3390/microorganisms13102361 - 14 Oct 2025
Viewed by 535
Abstract
The rapid emergence and evolution of avian viral pathogens present a major challenge to global poultry health and food security. Traditional vaccine development is often slow, costly, and limited by antigenic diversity. In this study, we present a comprehensive artificial intelligence (AI)-driven pipeline [...] Read more.
The rapid emergence and evolution of avian viral pathogens present a major challenge to global poultry health and food security. Traditional vaccine development is often slow, costly, and limited by antigenic diversity. In this study, we present a comprehensive artificial intelligence (AI)-driven pipeline for the rational design, modeling, and optimization of multi-epitope vaccines targeting economically important RNA and DNA viruses affecting poultry, including H5N1, NDV, IBV, IBDV, CAV, and FPV. We utilized advanced machine learning and deep learning tools for epitope prediction, antigenicity assessment, and structural modeling (via AlphaFold2), and codon optimization. B-cell and T-cell epitopes were selected based on binding affinity, conservation, and immunogenicity, while adjuvants and linker sequences enhanced construct stability and immune response. In silico immune simulations forecasted robust humoral and cellular responses, including cytokine production and memory cell activation. The study also highlights challenges such as data quality, model interpretability, and ethical considerations. Our work demonstrates the transformative potential of AI in veterinary vaccinology and offers a scalable model for rapid, data-driven vaccine development against avian diseases. Full article
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