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Updated: May 4, 2026

Assays for the Identification of Novel Antivirals against Bluetongue Virus

Assays for the Identification of Novel Antivirals against Bluetongue Virus

Published on: October 11, 2013

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.

|September 12, 2024

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.

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Last Updated: May 4, 2026

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Published on: January 20, 2017

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.