Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Mind the Gap – Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

Theresa Clare Noonan
* ORCID logo ,
Katrin Denzinger
,
Valerij Talagayev
,
Yu Chen
ORCID logo ,
Kristina Puls
ORCID logo ,
Clemens Alexander Wolf
,
Sijie Liu
ORCID logo ,
Trung Ngoc Nguyen
,
Gerhard Wolber
ORCID logo
Version 1 : Received: 29 August 2022 / Approved: 14 September 2022 / Online: 14 September 2022 (09:10:58 CEST)

A peer-reviewed article of this Preprint also exists.

Noonan, T.; Denzinger, K.; Talagayev, V.; Chen, Y.; Puls, K.; Wolf, C.A.; Liu, S.; Nguyen, T.N.; Wolber, G. Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence. Pharmaceuticals 2022, 15, 1304. Noonan, T.; Denzinger, K.; Talagayev, V.; Chen, Y.; Puls, K.; Wolf, C.A.; Liu, S.; Nguyen, T.N.; Wolber, G. Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence. Pharmaceuticals 2022, 15, 1304.

Abstract

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. 3D pharmacophore models are powerful computational tools 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, discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning will be 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.

Keywords

ligand-based pharmacophores; structure-based pharmacophores; virtual screening; drug design; machine learning; molecular dynamics; de novo design

Subject

Chemistry and Materials Science, Medicinal Chemistry

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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