Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Noonan, Theresa, Denzinger, Katrin, Talagayev, Valerij, Chen, Yu, Puls, Kristina, Wolf, Clemens Alexander, Liu, Sijie, Nguyen, Trung Ngoc, Wolber, Gerhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784838086769770496
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
work_keys_str_mv AT noonantheresa mindthegapdecipheringgpcrpharmacologyusing3dpharmacophoresandartificialintelligence
AT denzingerkatrin mindthegapdecipheringgpcrpharmacologyusing3dpharmacophoresandartificialintelligence
AT talagayevvalerij mindthegapdecipheringgpcrpharmacologyusing3dpharmacophoresandartificialintelligence
AT chenyu mindthegapdecipheringgpcrpharmacologyusing3dpharmacophoresandartificialintelligence
AT pulskristina mindthegapdecipheringgpcrpharmacologyusing3dpharmacophoresandartificialintelligence
AT wolfclemensalexander mindthegapdecipheringgpcrpharmacologyusing3dpharmacophoresandartificialintelligence
AT liusijie mindthegapdecipheringgpcrpharmacologyusing3dpharmacophoresandartificialintelligence
AT nguyentrungngoc mindthegapdecipheringgpcrpharmacologyusing3dpharmacophoresandartificialintelligence
AT wolbergerhard mindthegapdecipheringgpcrpharmacologyusing3dpharmacophoresandartificialintelligence