Computational anti-COVID-19 drug design: progress and challenges

Area of Science:

• Pharmaceutical Science
• Computational Biology
• Infectious Diseases

Background:

• Vaccines have significantly curbed the COVID-19 pandemic, but emerging variants like Delta pose ongoing threats.
• The need for effective therapeutic strategies alongside vaccination is critical for pandemic management.

Purpose of the Study:

• To review and compare computational drug design strategies for developing anti-COVID-19 therapeutics.
• To analyze the advantages and disadvantages of structure-based and AI-based drug design approaches.

Main Methods:

• Structure-based drug design: Investigates molecular fragments and functional groups to create antiviral drugs.
• AI-based drug design: Utilizes end-to-end learning to explore biochemical space for novel drug discovery.

Main Results:

• Both structure-based and AI-based methods offer distinct advantages in antiviral drug design for COVID-19.
• Structure-based approaches focus on known interactions, while AI broadens the search space.

Conclusions:

• Computational drug design, encompassing both structure-based and AI-driven methods, is vital for developing next-generation anti-COVID-19 treatments.
• Future research should focus on optimizing these strategies to overcome challenges posed by viral evolution.