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Viruses
Special Issues
Harnessing AI and Machine Learning for Antiviral Development
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Harnessing AI and Machine Learning for Antiviral Development

A special issue of Viruses (ISSN 1999-4915). This special issue belongs to the section "General Virology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 727

Special Issue Editor

Prof. Dr. X. Frank Zhang

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
Interests: single-molecule detection techniques; biophysical characterization of viral entry and virus-host cell interactions; mechanobiology of the cardiovascular system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) are ushering in a new era in the development of drugs and therapies, promising breakthroughs that could revolutionize our approach to the combat of numerous diseases. In virology, AI and ML tools have already made a significant impact, particularly in epidemiological modeling and outbreak prediction, which are crucial for informing public health strategies and mitigating viral spread. The application of AI and ML in the development of antiviral treatments has also rapidly emerged. AI's capacity to analyze vast datasets and predict outcomes with high accuracy is transforming the way in which we identify potential antiviral agents, understand viral mechanics, and develop personalized treatments.

This Special Issue aims to connect leading researchers and practitioners in the field in order to provide a comprehensive overview of how AI and ML are shaping the future of antiviral therapies, offering insights into the challenges and opportunities ahead. We aim to collect research articles and comprehensive review articles that address innovative methodologies, such as (but not limited to) virtual screening, genomic analysis, and molecular docking, showcasing how AI-driven models identify novel drug candidates and repurpose existing ones with high efficiency. We are also interested in advancements in using AI/ML to predict drug resistance, design robust antiviral compounds, and optimize clinical trials.

Prof. Dr. X. Frank Zhang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Viruses 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 2600 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

  • artificial intelligence
  • machine learning
  • antiviral therapies
  • personalized medicine
  • drug discovery
  • drug repurposing
  • drug resistance

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Published Papers (1 paper)

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25 pages, 7711 KiB  
Article
Synergizing Attribute-Guided Latent Space Exploration (AGLSE) with Classical Molecular Simulations to Design Potent Pep-Magnet Peptide Inhibitors to Abrogate SARS-CoV-2 Host Cell Entry
by Farhan Ullah, Aobo Xiao, Shahid Ullah, Na Yang, Min Lei, Liang Chen and Sheng Wang
Viruses 2025, 17(6), 828; https://doi.org/10.3390/v17060828 - 7 Jun 2025
Viewed by 236
Abstract
The COVID-19 infection, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has evoked a worldwide pandemic. Even though vaccines have been developed on an enormous scale, but due to regular mutations in the viral gene and the emergence of new strains could [...] Read more.
The COVID-19 infection, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has evoked a worldwide pandemic. Even though vaccines have been developed on an enormous scale, but due to regular mutations in the viral gene and the emergence of new strains could pose a more significant problem for the population. Therefore, new treatments are always necessary to combat future pandemics. Utilizing an antiviral peptide as a model biomolecule, we trained a generative deep learning algorithm on a database of known antiviral peptides to design novel peptide sequences with antiviral activity. Using artificial intelligence (AI), specifically variational autoencoders (VAE) and Wasserstein autoencoders (WAE), we were able to generate a latent space plot that can be surveyed for peptides with known properties and interpolated across a predictive vector between two defined points to identify novel peptides that exhibit dose-responsive antiviral activity. Two hundred peptide sequences were generated from the trained latent space and the top peptides were subjected to a molecular docking study. The docking analysis revealed that the top four peptides (MSK-1, MSK-2, MSK-3, and MSK-4) exhibited the strongest binding affinity, with docking scores of −106.4, −126.2, −125.7, and −127.8, respectively. Molecular dynamics simulations lasting 500 ns were performed to assess their stability and binding interactions. Further analyses, including MMGBSA, RMSD, RMSF, and hydrogen bond analysis, confirmed the stability and strong binding interactions of the peptide–protein complexes, suggesting that MSK-4 is a promising therapeutic agent for further development. We believe that the peptides generated through AI and MD simulations in the current study could be potential inhibitors in natural systems that can be utilized in designing therapeutic strategies against SARS-CoV-2. Full article
(This article belongs to the Special Issue Harnessing AI and Machine Learning for Antiviral Development)
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