Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Area of Science:

• Virology
• Pharmacology
• Computational Chemistry

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, resulted in millions of deaths and a global crisis.
• The urgent need for effective treatments highlighted the importance of rapid drug discovery methods.
• Computer simulations have become crucial for accelerating the identification and design of novel therapeutic agents.

Purpose of the Study:

• To provide an overview of COVID-19 management strategies.
• To analyze the role of computer-aided drug discovery (CADD) in combating pandemics.
• To showcase the application of structure-based drug design (SBDD) in developing COVID-19 therapeutics.

Main Methods:

• Review of drug repurposing efforts and the development of oral antivirals like Paxlovid.
• Analysis of structure-based drug design (SBDD) techniques.
• Application of computational methods including molecular docking and molecular dynamics simulations.

Main Results:

• Demonstration of successful drug discovery campaigns utilizing CADD techniques.
• Identification of effective therapeutic entities against SARS-CoV-2 through rational design.
• Validation of computational approaches in accelerating the development of antiviral drugs.

Conclusions:

• Computer-aided drug discovery, particularly SBDD, is vital for addressing current and future pandemics.
• Computational simulations significantly expedite the identification and characterization of novel drug candidates.
• The integration of CADD accelerates the development of urgently needed pharmacological interventions.