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Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery
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. A non-systematic review was done. All articles published on Pub-Med, Medline, Google, and Google Scholar...
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Published in: | Journal of infection and public health 2022-02, Vol.15 (2), p.289-296 |
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creator | Abubaker Bagabir, Sali Ibrahim, Nahla Khamis Abubaker Bagabir, Hala Hashem Ateeq, Raghdah |
description | 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.
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.
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.
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. |
doi_str_mv | 10.1016/j.jiph.2022.01.011 |
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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.
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.
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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.
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.
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.</description><subject>Artificial Intelligence</subject><subject>Challenges</subject><subject>COVID-19</subject><subject>COVID-19 Vaccines</subject><subject>Drug Development</subject><subject>Drugs</subject><subject>Genome sequencing</subject><subject>Humans</subject><subject>Pandemics</subject><subject>Review</subject><subject>SARS-CoV-2</subject><subject>Vaccines</subject><subject>Viral Vaccines</subject><issn>1876-0341</issn><issn>1876-035X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9kU1r3DAQhk1padK0f6CH4mMP9VajD8sqJRCWJl0I9NJCb0IrjR0Zr7WVvIb8-8rZZGkuhQGJ-Xiked-ieA9kBQTqz_2q9_u7FSWUrgjkgBfFOTSyrggTv1-e7hzOijcp9YTUTHD1ujhjgshGCnFe6HWYvatAlWZ05VWcfOutN0O5GSccBt_haPFLeYNj2GGZ8M8hJ_zYfSpdPHSlwxmHsN_hOD0AZmNzFUvnkw0zxvu3xavWDAnfPZ4Xxa_rbz_X36vbHzeb9dVtZQWFqXJSoOLO5V1MIxq-pQaYbc1WUcW5lSiBtnXdEGaYY8bVQlq-VYIpKhkwyi6KzZHrgun1Pvqdifc6GK8fEiF22uTl7IDaEksQW-pawjk0QqmGo1KgjIO6rVlmXR5Z-8N2h87m5aIZnkGfV0Z_p7sw6yy3VLAAPj4CYsiCpUnvsh5ZTjNiOCRNa0qVkJKT3EqPrTaGlCK2p2eA6MVl3evFZb24rAnkgDz04d8PnkaebM0NX48NmCWfPUadrF-cdD6inbIm_n_8v_iguQ0</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Abubaker Bagabir, Sali</creator><creator>Ibrahim, Nahla Khamis</creator><creator>Abubaker Bagabir, Hala</creator><creator>Hashem Ateeq, Raghdah</creator><general>Elsevier Ltd</general><general>The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20220201</creationdate><title>Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery</title><author>Abubaker Bagabir, Sali ; Ibrahim, Nahla Khamis ; Abubaker Bagabir, Hala ; Hashem Ateeq, Raghdah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c521t-d75e94dd202a8584b2a13cfab92944c7e712f66803a3d3ad657c4b95392731323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial Intelligence</topic><topic>Challenges</topic><topic>COVID-19</topic><topic>COVID-19 Vaccines</topic><topic>Drug Development</topic><topic>Drugs</topic><topic>Genome sequencing</topic><topic>Humans</topic><topic>Pandemics</topic><topic>Review</topic><topic>SARS-CoV-2</topic><topic>Vaccines</topic><topic>Viral Vaccines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abubaker Bagabir, Sali</creatorcontrib><creatorcontrib>Ibrahim, Nahla Khamis</creatorcontrib><creatorcontrib>Abubaker Bagabir, Hala</creatorcontrib><creatorcontrib>Hashem Ateeq, Raghdah</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of infection and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abubaker Bagabir, Sali</au><au>Ibrahim, Nahla Khamis</au><au>Abubaker Bagabir, Hala</au><au>Hashem Ateeq, Raghdah</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery</atitle><jtitle>Journal of infection and public health</jtitle><addtitle>J Infect Public Health</addtitle><date>2022-02-01</date><risdate>2022</risdate><volume>15</volume><issue>2</issue><spage>289</spage><epage>296</epage><pages>289-296</pages><issn>1876-0341</issn><eissn>1876-035X</eissn><abstract>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.
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.
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.
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.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>35078755</pmid><doi>10.1016/j.jiph.2022.01.011</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Artificial Intelligence Challenges COVID-19 COVID-19 Vaccines Drug Development Drugs Genome sequencing Humans Pandemics Review SARS-CoV-2 Vaccines Viral Vaccines |
title | Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery |
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