Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.

Area of Science:

• Computational biology
• Virology
• Artificial intelligence in drug discovery

Background:

• The COVID-19 pandemic necessitates rapid development of novel antiviral agents and vaccines.
• Understanding SARS-COV-2 pathobiology is crucial for identifying effective therapeutic targets.
• Current drug discovery methods require acceleration through advanced computational approaches.

Purpose of the Study:

• To review advances in COVID-19 drug and vaccine development using artificial intelligence (AI).
• To explore the potential of machine learning (ML) for discovering novel antiviral therapies.
• To present a dataset (CoronaDB-AI) for training AI models for COVID-19 treatment discovery.

Main Methods:

• Leveraging machine learning models trained on biomolecular data for in silico drug candidate prediction.
• Identifying and analyzing key molecular targets of SARS-COV-2.
• Utilizing the CoronaDB-AI dataset comprising compounds, peptides, and epitopes for model training.

Main Results:

• Machine learning models offer rapid and cost-effective methods for identifying potential viral therapies.
• Highlighting multiple molecular targets for COVID-19, inhibition of which could improve patient outcomes.
• The CoronaDB-AI dataset provides valuable data for training deep learning models.

Conclusions:

• AI and deep learning show significant promise in accelerating the discovery of effective COVID-19 therapeutics.
• Intelligent training on curated datasets like CoronaDB-AI can expedite the development of drugs and vaccines.
• Computational methods are essential tools for combating viral pandemics like COVID-19.