Recent computational drug repositioning strategies against SARS-CoV-2

Area of Science:

• Virology
• Pharmacology
• Computational Biology

Background:

• COVID-19 causes global suffering, and emerging variants like Omicron may resist current treatments.
• Traditional drug development is slow and expensive, necessitating alternative approaches.

Purpose of the Study:

• To review drug repositioning and combination strategies for effective COVID-19 treatment.
• To explore computational methods for identifying potential drug candidates and combinations.

Main Methods:

• Investigated drug repositioning based on sequence, expression, structure, and interaction data.
• Reviewed computational strategies for drug combination discovery.
• Analyzed the application of graph theory and neural networks in drug repositioning.

Main Results:

• Identified various drug repositioning methods tailored to different data types.
• Highlighted the benefits of drug combinations for increased efficacy and reduced toxicity.
• Found graph theory and neural networks to be highly promising computational strategies.

Conclusions:

• Drug repositioning, especially with combinations, offers a viable path for novel COVID-19 therapies.
• Integrating diverse data levels can enhance the success rate of drug repositioning efforts.
• Computational approaches are key to accelerating the discovery of effective treatments for COVID-19.