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Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases
The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570449/ https://www.ncbi.nlm.nih.gov/pubmed/34748992 http://dx.doi.org/10.1016/j.drudis.2021.10.022 |
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author | Bess, Adam Berglind, Frej Mukhopadhyay, Supratik Brylinski, Michal Griggs, Nicholas Cho, Tiffany Galliano, Chris Wasan, Kishor M. |
author_facet | Bess, Adam Berglind, Frej Mukhopadhyay, Supratik Brylinski, Michal Griggs, Nicholas Cho, Tiffany Galliano, Chris Wasan, Kishor M. |
author_sort | Bess, Adam |
collection | PubMed |
description | The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost-friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline. Several research teams have developed successful AI platforms for hit identification, lead generation, and lead optimization. In this review, we investigate the technologies at the forefront of spearheading an AI revolution in drug discovery and pharmaceutical sciences. |
format | Online Article Text |
id | pubmed-8570449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85704492021-11-08 Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases Bess, Adam Berglind, Frej Mukhopadhyay, Supratik Brylinski, Michal Griggs, Nicholas Cho, Tiffany Galliano, Chris Wasan, Kishor M. Drug Discov Today Informatics (Orange) The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost-friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline. Several research teams have developed successful AI platforms for hit identification, lead generation, and lead optimization. In this review, we investigate the technologies at the forefront of spearheading an AI revolution in drug discovery and pharmaceutical sciences. Elsevier Ltd. 2022-04 2021-11-05 /pmc/articles/PMC8570449/ /pubmed/34748992 http://dx.doi.org/10.1016/j.drudis.2021.10.022 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Informatics (Orange) Bess, Adam Berglind, Frej Mukhopadhyay, Supratik Brylinski, Michal Griggs, Nicholas Cho, Tiffany Galliano, Chris Wasan, Kishor M. Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases |
title | Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases |
title_full | Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases |
title_fullStr | Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases |
title_full_unstemmed | Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases |
title_short | Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases |
title_sort | artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases |
topic | Informatics (Orange) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570449/ https://www.ncbi.nlm.nih.gov/pubmed/34748992 http://dx.doi.org/10.1016/j.drudis.2021.10.022 |
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