Computational methods directed towards drug repurposing for COVID-19: advantages and limitations

Area of Science:

• Computational drug discovery and development
• Infectious disease research and treatment

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, continues to spread globally, with emerging variants posing ongoing challenges.
• Existing healthcare systems are strained by the pandemic, necessitating rapid therapeutic solutions.
• Drug repurposing offers a promising strategy to identify new treatments from existing, approved medications.

Purpose of the Study:

• To review and summarize diverse computational approaches for drug repurposing against COVID-19.
• To discuss the advantages and limitations of these computational strategies in accelerating drug development.
• To highlight methods for creating smart and safe drugs to combat SARS-CoV-2.

Main Methods:

• Utilized computational techniques including molecular docking and molecular dynamic simulations.
• Employed network medicine approaches such as disease-disease association, drug-drug interaction, and integrated biological networks.
• Incorporated artificial intelligence and machine learning for drug discovery and development.

Main Results:

• Identified various computational strategies that accelerate the identification of potential drug candidates for COVID-19.
• Demonstrated the utility of diverse computational methods in repurposing existing drugs for novel therapeutic applications.
• Provided insights into the effectiveness and challenges associated with computational drug repurposing for emerging infectious diseases.

Conclusions:

• Computational drug repurposing is a vital strategy for rapidly developing effective treatments against COVID-19 and future pandemics.
• The reviewed computational methods offer a powerful toolkit for accelerating the creation of safe and smart drugs.
• Continued application of these techniques is crucial for addressing evolving viral threats like SARS-CoV-2 variants.