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Using informative features in machine learning based method for COVID-19 drug repurposing.

Rosa Aghdam1, Mahnaz Habibi2, Golnaz Taheri3,4

  • 1School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. rosa.aghdam@ipm.ir.

Journal of Cheminformatics
|September 21, 2021
PubMed
Summary
This summary is machine-generated.

Computational drug repurposing identifies potential COVID-19 treatments by analyzing biological networks and drug-target interactions. This approach accelerates the discovery of therapies for Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2).

Keywords:
Clustering methodCoronavirus disease 2019Protein−protein interactionSARS-CoV-2

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Area of Science:

  • Computational biology
  • Drug discovery
  • Virology

Background:

  • Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), poses a significant global health threat with no specific treatments.
  • Drug repurposing offers a faster, cost-effective alternative to de novo drug discovery for identifying potential COVID-19 therapies.
  • Computational methods can streamline the drug repurposing process by analyzing complex biological data.

Purpose of the Study:

  • To computationally identify potential drug candidates for treating COVID-19 through drug repurposing.
  • To construct and analyze a COVID-19-related biological network to identify essential proteins.
  • To leverage drug-target and protein-protein interaction data to find effective drug clusters.

Main Methods:

  • A biological network was constructed representing COVID-19 targets and associated biological processes.
  • Essential proteins crucial for network disruption were identified, leading to the selection of 93 proteins linked to COVID-19 pathology.
  • Drug-target and protein-protein interaction data were used to derive features for clustering drugs into potential treatment groups.

Main Results:

  • Five distinct clusters of drugs were identified as potential candidates for COVID-19 treatment.
  • The study found 93 essential proteins associated with COVID-19 pathology.
  • Statistical and clinical evidence supported the identified candidate drugs, with 80% having prior study or clinical trial involvement.

Conclusions:

  • Computational drug repurposing using biological networks is a viable strategy for discovering COVID-19 treatments.
  • The identified drug clusters and candidate drugs warrant further investigation for clinical application against SARS-CoV-2.
  • This approach significantly reduces the time, cost, and risk associated with traditional drug development.