[Drug screening for COVID-19 using supercomputer "Fugaku"].

Area of Science:

• Computational chemistry
• Drug discovery
• Supercomputing applications

Background:

• The global COVID-19 pandemic necessitates the urgent development of effective treatments.
• The SARS-CoV-2 virus continues to pose unpredictable challenges, particularly in regions like Japan.
• Existing drug discovery pipelines require acceleration to meet public health demands.

Purpose of the Study:

• To leverage the capabilities of the Fugaku supercomputer for accelerated drug screening against SARS-CoV-2.
• To identify existing drugs with high affinity to the SARS-CoV-2 main protease, a key viral replication target.
• To demonstrate the impact of large-scale molecular dynamics simulations in identifying therapeutic drug candidates.

Main Methods:

• Utilized the Fugaku supercomputer, the world's No. 1 ranked system, for computational drug screening.
• Performed large-scale molecular dynamics calculations to assess drug-target interactions.
• Screened approximately 2,000 existing drugs for potential therapeutic efficacy against SARS-CoV-2.
• Focused analysis on the main protease (Mpro) of SARS-CoV-2, crucial for viral multiplication.

Main Results:

• Successfully identified several existing drug candidates exhibiting high affinity to the SARS-CoV-2 main protease.
• This represents the first global attempt at drug screening on this scale using molecular dynamics simulations.
• The findings provide a foundation for further investigation into these candidates as potential COVID-19 therapeutics.

Conclusions:

• The Fugaku supercomputer significantly enhances the efficiency and scale of drug discovery for emerging infectious diseases.
• Accelerated screening using advanced computational methods can expedite the identification of viable therapeutic agents.
• This research exemplifies the critical role of supercomputing in addressing global health crises like the COVID-19 pandemic.