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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2022
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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. |
format | Online Article Text |
id | pubmed-10392680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
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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