Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID...

Area of Science:

• Virology
• Computational Biology
• Immunology

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, has caused global health and healthcare system challenges.
• Developing vaccines and treatments for SARS-CoV-2 is a critical global health priority.
• Traditional vaccine development is time-consuming, necessitating faster methodologies.

Purpose of the Study:

• To explore the role of artificial intelligence (AI) and machine learning (ML) in accelerating vaccine development against SARS-CoV-2.
• To highlight the application of reverse vaccinology (RV) and AI tools in identifying potential vaccine antigens and drug candidates.
• To discuss the potential of AI in clinical trials and practices for vaccine development.

Main Methods:

• Utilizing reverse vaccinology (RV) principles, which leverage AI and ML algorithms.
• Employing computational approaches and AI tools for drug discovery and vaccine candidate identification.
• Analyzing datasets to identify existing drugs with potential inhibitory effects against human coronaviruses.

Main Results:

• Reverse vaccinology (RV) has reduced antigen discovery time from 5-15 years to 1-2 years.
• AI and ML models show promise in accelerating the discovery and optimization of antivirals and vaccine candidates.
• AI tools facilitate rapid research and development of vaccines and therapies for SARS-CoV-2.

Conclusions:

• AI and ML are pivotal in speeding up the identification of vaccine antigens and drug targets for SARS-CoV-2.
• Computational approaches significantly shorten the timeline for developing vaccines and treatments.
• AI holds substantial potential for enhancing vaccine clinical trials and clinical practices against COVID-19.