World-class research. Ultimate impact.
More on impact ›

Mini Review ARTICLE

Front. Artif. Intell. | doi: 10.3389/frai.2020.00065

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development Provisionally accepted The final, formatted version of the article will be published soon. Notify me

  • 1Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, United States
  • 2Department of Electrical and Computer Engineering, University of Central Florida, United States
  • 3A2A Pharmaceuticals, United States
  • 4Atomwise Inc, United States
  • 5Department of Chemistry and Biochemistry, College of Science, University of Arizona, United States
  • 6Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, United States

SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are yet no novel antiviral agents or approved vaccines available to be deployed as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method to accomplish this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered both inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, they can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence, and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate the applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies y.

Keywords: COVID-19, SARS-CoV-2, drug, Vaccine, artificial intelligence, deep learning

Received: 09 May 2020; Accepted: 17 Jul 2020.

Copyright: © 2020 Keshavarzi Arshadi, Webb, Salem, Cruz, Calad-Thomas, Ghadirian, Collins, Diez-Cecilia, Kelly, Goodarzi and Yuan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Jiann Shiun Yuan, Department of Electrical and Computer Engineering, University of Central Florida, Orlando, 32816, Florida, United States, jiann-shiun.yuan@ucf.edu