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Review

Significance of Artificial Intelligence in the Study of Virus–Host Cell Interactions

by
James Elste
1,†,
Akash Saini
2,†,
Rafael Mejia-Alvarez
3,
Armando Mejía
4,
Cesar Millán-Pacheco
5,
Michelle Swanson-Mungerson
1 and
Vaibhav Tiwari
1,*
1
Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA
2
Hinsdale Central High School, 5500 S Grant St, Hinsdale, IL 60521, USA
3
Department of Physiology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA
4
Departamento de Biotechnology, Universidad Autónoma Metropolitana-Iztapalapa, Ciudad de Mexico 09340, Mexico
5
Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Av. Universidad No. 1001, Col Chamilpa, Cuernavaca 62209, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Biomolecules 2024, 14(8), 911; https://doi.org/10.3390/biom14080911
Submission received: 13 June 2024 / Revised: 11 July 2024 / Accepted: 23 July 2024 / Published: 26 July 2024
(This article belongs to the Special Issue Molecular Mechanisms and Biomedical Applications of Virus Entry)

Abstract

A highly critical event in a virus’s life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus–host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus–host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus–host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein–protein and protein–sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms—a subfield of ML—can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus–host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.
Keywords: virus entry; virus–host cell interactions; entry receptors; artificial intelligence virus entry; virus–host cell interactions; entry receptors; artificial intelligence

Share and Cite

MDPI and ACS Style

Elste, J.; Saini, A.; Mejia-Alvarez, R.; Mejía, A.; Millán-Pacheco, C.; Swanson-Mungerson, M.; Tiwari, V. Significance of Artificial Intelligence in the Study of Virus–Host Cell Interactions. Biomolecules 2024, 14, 911. https://doi.org/10.3390/biom14080911

AMA Style

Elste J, Saini A, Mejia-Alvarez R, Mejía A, Millán-Pacheco C, Swanson-Mungerson M, Tiwari V. Significance of Artificial Intelligence in the Study of Virus–Host Cell Interactions. Biomolecules. 2024; 14(8):911. https://doi.org/10.3390/biom14080911

Chicago/Turabian Style

Elste, James, Akash Saini, Rafael Mejia-Alvarez, Armando Mejía, Cesar Millán-Pacheco, Michelle Swanson-Mungerson, and Vaibhav Tiwari. 2024. "Significance of Artificial Intelligence in the Study of Virus–Host Cell Interactions" Biomolecules 14, no. 8: 911. https://doi.org/10.3390/biom14080911

APA Style

Elste, J., Saini, A., Mejia-Alvarez, R., Mejía, A., Millán-Pacheco, C., Swanson-Mungerson, M., & Tiwari, V. (2024). Significance of Artificial Intelligence in the Study of Virus–Host Cell Interactions. Biomolecules, 14(8), 911. https://doi.org/10.3390/biom14080911

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