Accelerating COVID-19 Drug Discovery with High-Performance Computing.

Area of Science:

• Computational chemistry and pharmacology
• Drug discovery and development
• Bioinformatics and computational biology

Background:

• The COVID-19 pandemic highlighted the slow, costly nature of traditional drug discovery.
• There is an urgent need for accelerated therapeutic development, especially for emerging infectious diseases.
• Future pandemics necessitate faster methods for identifying and developing treatments.

Approach:

• Leveraging high-performance computing (HPC) for rapid computational drug discovery.
• Integrating artificial intelligence (AI) and machine learning (ML) to revolutionize drug design.
• Employing molecular dynamics (MD) simulations coupled with ML and HPC for studying protein inhibitors.

Key Points:

• The review details a strategic workflow for accelerated drug discovery.
• Focuses on the integration of MD simulations with ML and HPC.
• Demonstrates a powerful tool for studying inhibitors against COVID-19 target proteins.

Conclusions:

• Computational methods, particularly AI/ML with MD on HPC, offer a pathway to pandemic-speed drug discovery.
• This integrated approach can significantly reduce the time and cost of developing new therapeutics.
• The described strategy provides a framework for rapid response to future health crises.