Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics

Yash Gupta1, Oleksandr V Savytskyi2, Matt Coban3

  • 1Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA.

Insights

The COVID-19 pandemic accelerated drug discovery, utilizing computational biology and artificial intelligence for target-based therapeutic development. This research highlights potential agents for COVID-19 and future pandemics.

Area of Science:

  • Computational biology and drug discovery
  • Infectious disease research
  • Pharmaceutical development

Background:

  • COVID-19 pandemic caused over 5 million deaths and strained global health systems.
  • The pandemic spurred rapid technological innovation in drug development and public health.
  • Existing pharmaceutical models were challenged by the urgent need for effective treatments.

Purpose of the Study:

  • To discuss advances in target-based drug discovery for COVID-19.
  • To explore the therapeutic implications of known target-specific agents.
  • To identify potential therapeutic targets and agents for current and future pandemics.

Main Methods:

  • Leveraging computational biology and artificial intelligence for drug development.
  • Analyzing structural characterization of SARS-CoV-2 proteins and host pathways.
  • Investigating the druggability of potential therapeutic targets.
  • Reviewing existing technology for virtual computer-assisted drug development.

Main Results:

  • Every protein of SARS-CoV-2 has been structurally characterized.
  • Host pathways related to SARS-CoV-2 have been extensively mapped.
  • The research community has identified numerous druggable targets.
  • Advances in AI and virtual drug development have accelerated lead generation.

Conclusions:

  • Target-based drug discovery, aided by AI, is crucial for combating COVID-19.
  • Personalized medicine and patient stratification are needed for effective therapeutic management.
  • Rational drug design can lead to novel therapeutics for current and future pandemics.

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