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Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence
G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computat...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695541/ https://www.ncbi.nlm.nih.gov/pubmed/36355476 http://dx.doi.org/10.3390/ph15111304 |
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author | Noonan, Theresa Denzinger, Katrin Talagayev, Valerij Chen, Yu Puls, Kristina Wolf, Clemens Alexander Liu, Sijie Nguyen, Trung Ngoc Wolber, Gerhard |
author_facet | Noonan, Theresa Denzinger, Katrin Talagayev, Valerij Chen, Yu Puls, Kristina Wolf, Clemens Alexander Liu, Sijie Nguyen, Trung Ngoc Wolber, Gerhard |
author_sort | Noonan, Theresa |
collection | PubMed |
description | G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs. |
format | Online Article Text |
id | pubmed-9695541 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96955412022-11-26 Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence Noonan, Theresa Denzinger, Katrin Talagayev, Valerij Chen, Yu Puls, Kristina Wolf, Clemens Alexander Liu, Sijie Nguyen, Trung Ngoc Wolber, Gerhard Pharmaceuticals (Basel) Review G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs. MDPI 2022-10-22 /pmc/articles/PMC9695541/ /pubmed/36355476 http://dx.doi.org/10.3390/ph15111304 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Noonan, Theresa Denzinger, Katrin Talagayev, Valerij Chen, Yu Puls, Kristina Wolf, Clemens Alexander Liu, Sijie Nguyen, Trung Ngoc Wolber, Gerhard Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence |
title | Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence |
title_full | Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence |
title_fullStr | Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence |
title_full_unstemmed | Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence |
title_short | Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence |
title_sort | mind the gap—deciphering gpcr pharmacology using 3d pharmacophores and artificial intelligence |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695541/ https://www.ncbi.nlm.nih.gov/pubmed/36355476 http://dx.doi.org/10.3390/ph15111304 |
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