4.6 Review

Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery

Journal

JOURNAL OF INFECTION AND PUBLIC HEALTH
Volume 15, Issue 2, Pages 289-296

Publisher

ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.jiph.2022.01.011

Keywords

COVID-19; Artificial Intelligence; Genome sequencing; Drugs; Vaccines; Challenges

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This article reviews the work done using artificial intelligence in identifying COVID-19 genomic sequences, drug development, and vaccine research. The advantages of AI include prompt identification of variants of concern, cost-saving, and agility. However, challenges such as data collection, validation, ethical considerations, and the interpretability of deep learning models exist.
Objectives: To clarify the work done by using AI for identifying the genomic sequences, development of drugs and vaccines for COVID-19 and to recognize the advantages and challenges of using such technology.Methods: A non-systematic review was done. All articles published on Pub-Med, Medline, Google, and Google Scholar on AI or digital health regarding genomic sequencing, drug development, and vaccines of COVID-19 were scrutinized and summarized.Results: The sequence of SARS-CoV-2 was identified with the help of AI. It can help also in the prompt identification of variants of concern (VOC) as delta strains and Omicron. Furthermore, there are many drugs applied with the help of AI. These drugs included Atazanavir, Remdesivir, Efavirenz, Ritonavir, and Dolutegravir, PARP1 inhibitors (Olaparib and CVL218 which is Mefuparib hydrochloride), Abacavir, Roflumilast, Almitrine, and Mesylate. Many vaccines were developed utilizing the new technology of bioinformatics, databases, immune-informatics, machine learning, and reverse vaccinology to the whole SARS-CoV-2 proteomes or the structural proteins. Examples of these vaccines are the messenger RNA and viral vector vaccines. AI provides cost-saving and agility. However, the challenges of its usage are the difficulty of collecting data, the internal and external validation, ethical consideration, therapeutic effect, and the time needed for clinical trials after drug approval. Moreover, there is a common problem in the deep learning (DL) model which is the shortage of interpretability.Conclusion: The growth of AI techniques in health care opened a broad gate for discovering the genomic sequences of the COVID-19 virus and the VOC. AI helps also in the development of vaccines and drugs (including drug repurposing) to obtain potential preventive and therapeutic agents for controlling the COVID-19 pandemic.(c) 2022 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. CC_BY_NC_ND_4.0

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