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Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19

The coronavirus disease (COVID-19) pandemic has caused havoc worldwide. The tests currently used to diagnose COVID-19 are based on real time reverse transcription polymerase chain reaction (RT-PCR), computed tomography medical imaging techniques and immunoassays. It takes 2 days to obtain results fr...

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Autores principales: Mikkili, Indira, Karlapudi, Abraham Peele, Venkateswarulu, T. C., Kodali, Vidya Prabhakar, Macamdas, Deepika Sri Singh, Sreerama, Krupanidhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500072/
https://www.ncbi.nlm.nih.gov/pubmed/34707924
http://dx.doi.org/10.7717/peerj.12073
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author Mikkili, Indira
Karlapudi, Abraham Peele
Venkateswarulu, T. C.
Kodali, Vidya Prabhakar
Macamdas, Deepika Sri Singh
Sreerama, Krupanidhi
author_facet Mikkili, Indira
Karlapudi, Abraham Peele
Venkateswarulu, T. C.
Kodali, Vidya Prabhakar
Macamdas, Deepika Sri Singh
Sreerama, Krupanidhi
author_sort Mikkili, Indira
collection PubMed
description The coronavirus disease (COVID-19) pandemic has caused havoc worldwide. The tests currently used to diagnose COVID-19 are based on real time reverse transcription polymerase chain reaction (RT-PCR), computed tomography medical imaging techniques and immunoassays. It takes 2 days to obtain results from the RT-PCR test and also shortage of test kits creating a requirement for alternate and rapid methods to accurately diagnose COVID-19. Application of artificial intelligence technologies such as the Internet of Things, machine learning tools and big data analysis to COVID-19 diagnosis could yield rapid and accurate results. The neural networks and machine learning tools can also be used to develop potential drug molecules. Pharmaceutical companies face challenges linked to the costs of drug molecules, research and development efforts, reduced efficiency of drugs, safety concerns and the conduct of clinical trials. In this review, relevant features of artificial intelligence and their potential applications in COVID-19 diagnosis and drug development are highlighted.
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spelling pubmed-85000722021-10-26 Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19 Mikkili, Indira Karlapudi, Abraham Peele Venkateswarulu, T. C. Kodali, Vidya Prabhakar Macamdas, Deepika Sri Singh Sreerama, Krupanidhi PeerJ Bioinformatics The coronavirus disease (COVID-19) pandemic has caused havoc worldwide. The tests currently used to diagnose COVID-19 are based on real time reverse transcription polymerase chain reaction (RT-PCR), computed tomography medical imaging techniques and immunoassays. It takes 2 days to obtain results from the RT-PCR test and also shortage of test kits creating a requirement for alternate and rapid methods to accurately diagnose COVID-19. Application of artificial intelligence technologies such as the Internet of Things, machine learning tools and big data analysis to COVID-19 diagnosis could yield rapid and accurate results. The neural networks and machine learning tools can also be used to develop potential drug molecules. Pharmaceutical companies face challenges linked to the costs of drug molecules, research and development efforts, reduced efficiency of drugs, safety concerns and the conduct of clinical trials. In this review, relevant features of artificial intelligence and their potential applications in COVID-19 diagnosis and drug development are highlighted. PeerJ Inc. 2021-10-05 /pmc/articles/PMC8500072/ /pubmed/34707924 http://dx.doi.org/10.7717/peerj.12073 Text en © 2021 Mikkili et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Mikkili, Indira
Karlapudi, Abraham Peele
Venkateswarulu, T. C.
Kodali, Vidya Prabhakar
Macamdas, Deepika Sri Singh
Sreerama, Krupanidhi
Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19
title Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19
title_full Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19
title_fullStr Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19
title_full_unstemmed Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19
title_short Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19
title_sort potential of artificial intelligence to accelerate diagnosis and drug discovery for covid-19
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500072/
https://www.ncbi.nlm.nih.gov/pubmed/34707924
http://dx.doi.org/10.7717/peerj.12073
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