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Computational drug discovery under RNA times

RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutical...

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Detalles Bibliográficos
Autores principales: Bernetti, Mattia, Aguti, Riccardo, Bosio, Stefano, Recanatini, Maurizio, Masetti, Matteo, Cavalli, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392680/
https://www.ncbi.nlm.nih.gov/pubmed/37529286
http://dx.doi.org/10.1017/qrd.2022.20
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author Bernetti, Mattia
Aguti, Riccardo
Bosio, Stefano
Recanatini, Maurizio
Masetti, Matteo
Cavalli, Andrea
author_facet Bernetti, Mattia
Aguti, Riccardo
Bosio, Stefano
Recanatini, Maurizio
Masetti, Matteo
Cavalli, Andrea
author_sort Bernetti, Mattia
collection PubMed
description RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands.
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spelling pubmed-103926802023-08-01 Computational drug discovery under RNA times Bernetti, Mattia Aguti, Riccardo Bosio, Stefano Recanatini, Maurizio Masetti, Matteo Cavalli, Andrea QRB Discov Perspective RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands. Cambridge University Press 2022-11-14 /pmc/articles/PMC10392680/ /pubmed/37529286 http://dx.doi.org/10.1017/qrd.2022.20 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
spellingShingle Perspective
Bernetti, Mattia
Aguti, Riccardo
Bosio, Stefano
Recanatini, Maurizio
Masetti, Matteo
Cavalli, Andrea
Computational drug discovery under RNA times
title Computational drug discovery under RNA times
title_full Computational drug discovery under RNA times
title_fullStr Computational drug discovery under RNA times
title_full_unstemmed Computational drug discovery under RNA times
title_short Computational drug discovery under RNA times
title_sort computational drug discovery under rna times
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392680/
https://www.ncbi.nlm.nih.gov/pubmed/37529286
http://dx.doi.org/10.1017/qrd.2022.20
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