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Article

Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery

Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
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Author to whom correspondence should be addressed.
Academic Editor: Irina Moreira
Int. J. Mol. Sci. 2021, 22(8), 4060; https://doi.org/10.3390/ijms22084060
Received: 24 February 2021 / Revised: 31 March 2021 / Accepted: 7 April 2021 / Published: 14 April 2021
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Drug Development)
The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a receptor subtype was compared with explicit information on ligand-receptor interactions from induced-fit docking. Both methods have proved their usefulness in drug response predictions. Yet, their successful combination still requires allosteric/orthosteric assignment of ligands from datasets. Our ligand datasets included activities of two members of the secretin receptor family: GCGR and GLP-1R. Simultaneous activation of two or three receptors of this family by dual or triple agonists is not a typical kind of information included in compound databases. A precise allosteric/orthosteric ligand assignment requires a continuous update based on new structural and biological data. This data incompleteness remains the main obstacle for current ML methods applied to class B GPCR drug discovery. Even so, for these two class B receptors, our ligand-based ML model demonstrated high accuracy (5-fold cross-validation Q2 > 0.63 and Q2 > 0.67 for GLP-1R and GCGR, respectively). In addition, we performed a ligand annotation using recent cryogenic-electron microscopy (cryo-EM) and X-ray crystallographic data on small-molecule complexes of GCGR and GLP-1R. As a result, we assigned GLP-1R and GCGR actives deposited in ChEMBL to four small-molecule binding sites occupied by positive and negative allosteric modulators and a full agonist. Annotated compounds were added to our recently released repository of GPCR data. View Full-Text
Keywords: G protein-coupled receptors; machine learning; gradient boosting; induced-fit docking; virtual screening; molecular docking; scoring functions; drug discovery; glucagon receptor family; GCGR; GLP-1R; secretin receptor family; class B GPCRs G protein-coupled receptors; machine learning; gradient boosting; induced-fit docking; virtual screening; molecular docking; scoring functions; drug discovery; glucagon receptor family; GCGR; GLP-1R; secretin receptor family; class B GPCRs
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MDPI and ACS Style

Mizera, M.; Latek, D. Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery. Int. J. Mol. Sci. 2021, 22, 4060. https://doi.org/10.3390/ijms22084060

AMA Style

Mizera M, Latek D. Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery. International Journal of Molecular Sciences. 2021; 22(8):4060. https://doi.org/10.3390/ijms22084060

Chicago/Turabian Style

Mizera, Mikołaj, and Dorota Latek. 2021. "Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery" International Journal of Molecular Sciences 22, no. 8: 4060. https://doi.org/10.3390/ijms22084060

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