AI-Driven prediction of binding trends of SARS-CoV-2 variants from atomistic simulations

Detalles Bibliográficos
Autores principales: Capponi, Sara, Wang, Shangying, Navarro, Erik, Bianco, Simone
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biophysical Society. Published by Elsevier Inc. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8833047/
http://dx.doi.org/10.1016/j.bpj.2021.11.1211
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author Capponi, Sara
Wang, Shangying
Navarro, Erik
Bianco, Simone
author_facet Capponi, Sara
Wang, Shangying
Navarro, Erik
Bianco, Simone
author_sort Capponi, Sara
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spelling pubmed-88330472022-02-14 AI-Driven prediction of binding trends of SARS-CoV-2 variants from atomistic simulations Capponi, Sara Wang, Shangying Navarro, Erik Bianco, Simone Biophys J Article Biophysical Society. Published by Elsevier Inc. 2022-02-11 2022-02-11 /pmc/articles/PMC8833047/ http://dx.doi.org/10.1016/j.bpj.2021.11.1211 Text en Copyright © 2021 Biophysical Society. Published by Elsevier Inc. 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 Article
Capponi, Sara
Wang, Shangying
Navarro, Erik
Bianco, Simone
AI-Driven prediction of binding trends of SARS-CoV-2 variants from atomistic simulations
title AI-Driven prediction of binding trends of SARS-CoV-2 variants from atomistic simulations
title_full AI-Driven prediction of binding trends of SARS-CoV-2 variants from atomistic simulations
title_fullStr AI-Driven prediction of binding trends of SARS-CoV-2 variants from atomistic simulations
title_full_unstemmed AI-Driven prediction of binding trends of SARS-CoV-2 variants from atomistic simulations
title_short AI-Driven prediction of binding trends of SARS-CoV-2 variants from atomistic simulations
title_sort ai-driven prediction of binding trends of sars-cov-2 variants from atomistic simulations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8833047/
http://dx.doi.org/10.1016/j.bpj.2021.11.1211
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AT navarroerik aidrivenpredictionofbindingtrendsofsarscov2variantsfromatomisticsimulations
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