Recent omics-based computational methods for COVID-19 drug discovery and repurposing

Area of Science:

• Computational biology
• Bioinformatics
• Artificial Intelligence in Medicine

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, presents a global health crisis requiring innovative solutions.
• Traditional containment measures have proven insufficient, necessitating rapid development of treatments and diagnostics.
• Computational methods, particularly AI, offer powerful tools to accelerate research and development efforts.

Purpose of the Study:

• To review recent studies employing Omics-based data and AI for combating COVID-19.
• To consolidate information on datasets, methodologies, and applications in drug discovery, vaccination, and diagnostics.
• To provide researchers with a timely overview for efficient pandemic response.

Main Methods:

• Review of recent scientific literature focusing on AI and computational tools applied to COVID-19.
• Analysis of studies utilizing Omics data (genomic, proteomic, metabolic) for therapeutic and diagnostic development.
• Categorization of methods based on data granularity (e.g., molecular structures, sequences, metabolic pathways).

Main Results:

• AI and computational approaches show significant promise in identifying potential drug candidates and repurposing existing drugs.
• Development of AI-driven diagnostic tools for early detection and monitoring of COVID-19.
• Advancements in AI for vaccine design and efficacy prediction.

Conclusions:

• AI and Omics-based data integration are crucial for accelerating the development of COVID-19 countermeasures.
• Continued research and collaboration leveraging these computational tools are essential for pandemic control.
• The reviewed methods offer a foundation for future innovations in infectious disease research.