Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Subviral Agents01:29

Subviral Agents

933
Subviral agents are infectious entities that resemble viruses but lack one or more viral components, such as a capsid or essential replication machinery. These agents include viroids, prions, and satellites, each possessing distinct structural and functional characteristics that influence their mode of infection and replication.Viroids are the simplest subviral agents, consisting of circular, single-stranded RNA molecules without a protein coat. They exclusively infect plants, relying entirely...
933

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Systematic Review of Drug-Related Interactionsî—¸Utilizing Deep Learning and LLMs for Prediction and Mitigation.

ACS omega·2026
Same author

A graph attention-based deep learning network for predicting biotech-small-molecule drug interactions.

Bioinformatics advances·2025
Same author

Combination therapy synergism prediction for virus treatment using machine learning models.

PloS one·2024
Same author

A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method.

Molecular diversity·2024
Same author

Longest common substring in Longest Common Subsequence's solution service: A novel hyper-heuristic.

Computational biology and chemistry·2023
Same author

DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.

BMC bioinformatics·2023

Related Experiment Video

Updated: May 4, 2026

Assays for the Identification of Novel Antivirals against Bluetongue Virus
12:02

Assays for the Identification of Novel Antivirals against Bluetongue Virus

Published on: October 11, 2013

14.0K

Antivirals for monkeypox virus: Proposing an effective machine/deep learning framework.

Morteza Hashemi1, Arash Zabihian2, Masih Hajsaeedi1

  • 1Department of Computer Science, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.

Plos One
|September 12, 2024
PubMed
Summary
This summary is machine-generated.

No approved drugs exist for monkeypox virus (MPXV). This study used computational methods and machine learning to identify potential MPXV antivirals, suggesting Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil for treatment.

More Related Videos

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

36.2K
Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses
09:07

Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses

Published on: January 20, 2017

9.9K

Related Experiment Videos

Last Updated: May 4, 2026

Assays for the Identification of Novel Antivirals against Bluetongue Virus
12:02

Assays for the Identification of Novel Antivirals against Bluetongue Virus

Published on: October 11, 2013

14.0K
Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
10:11

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

36.2K
Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses
09:07

Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses

Published on: January 20, 2017

9.9K

Area of Science:

  • Virology
  • Computational Biology
  • Drug Discovery

Background:

  • Monkeypox virus (MPXV) poses a significant public health threat with no specific approved antiviral treatments.
  • Drug repurposing, aided by computational methods, offers a cost-effective strategy for identifying treatments for emerging viral diseases.

Purpose of the Study:

  • To develop and apply a computational framework for predicting effective antiviral drugs against MPXV.
  • To leverage machine learning and deep learning for identifying potential MPXV therapeutics through drug repurposing.

Main Methods:

  • Generation of a novel virus-antiviral dataset for MPXV.
  • Application of machine learning and deep learning models for antiviral prediction.
  • In silico drug screening using molecular docking studies on homology-modeled and validated MPXV target proteins.

Main Results:

  • The computational framework successfully predicted several potential antiviral drugs for MPXV.
  • Docking studies validated the efficacy of the predicted drugs against MPXV targets.
  • Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil were identified as promising candidates for MPXV treatment.

Conclusions:

  • This study presents the first application of deep learning methods for MPXV antiviral prediction.
  • The identified drugs represent viable options for further investigation and potential clinical use against MPXV infections.