Performances and Critical Limitations of Docking and Protein–Ligand Interactions for Efficacy-Driven Virtual Screening Targeting Class A GPCRs
Abstract
Structure-based virtual screening of chemical libraries is an established and widely used strategy for identifying novel ligands for G-protein-coupled receptors. An enhancement based on integrating protein–ligand interaction with docking has previously been proposed, but its actual impact on improving screening outcomes has remained unclear. Here, we present a comprehensive assessment based on systematic benchmarking using a diverse set of class A G-protein-coupled receptors and different approaches to represent protein–ligand, including dynamic patterns extracted from molecular dynamics simulations. Our results demonstrate that the combined approach overall improves the efficacy bias of selected ligands as compared to docking alone (ranking by scoring function). All tested variations prove broadly functional; however, the most sophisticated one─incorporating simulation and a learning model─emerges as the most robust alternative for a prospective setting. The analysis of two prospective cases, the design of both agonists and antagonists of CNR1 and the more challenging search for CXCR4 nonpeptidic agonist, reveals both great potential and inherent structural limitations, highlighting the need for an accurate and suitable three-dimensional structure.
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BibTeX
@article{chiesa2026performances,
title = {{Performances and Critical Limitations of Docking and Protein–Ligand Interactions for Efficacy-Driven Virtual Screening Targeting Class A GPCRs}},
author = {Luca Chiesa and Guillaume Bret and Séverine Schneider and Martine Schmitt and Frédéric Bihel and Omolade Otun and Mathias Lescuyer and Thierry Durroux and Esther Kellenberger},
journal = {Journal of Chemical Information and Modeling},
year = {2026},
month = oct,
doi = {10.1021/acs.jcim.6c02252},
url = {https://hal.science/hal-05779097v1},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Performances and Critical Limitations of Docking and Protein–Ligand Interactions for Efficacy-Driven Virtual Screening Targeting Class A GPCRs
AU - Luca Chiesa
AU - Guillaume Bret
AU - Séverine Schneider
AU - Martine Schmitt
AU - Frédéric Bihel
AU - Omolade Otun
AU - Mathias Lescuyer
AU - Thierry Durroux
AU - Esther Kellenberger
PY - 2026
DA - 2026/10/19
JO - Journal of Chemical Information and Modeling
DO - 10.1021/acs.jcim.6c02252
UR - https://hal.science/hal-05779097v1
AB - Structure-based virtual screening of chemical libraries is an established and widely used strategy for identifying novel ligands for G-protein-coupled receptors. An enhancement based on integrating protein–ligand interaction with docking has previously been proposed, but its actual impact on improving screening outcomes has remained unclear. Here, we present a comprehensive assessment based on systematic benchmarking using a diverse set of class A G-protein-coupled receptors and different approaches to represent protein–ligand, including dynamic patterns extracted from molecular dynamics simulations. Our results demonstrate that the combined approach overall improves the efficacy bias of selected ligands as compared to docking alone (ranking by scoring function). All tested variations prove broadly functional; however, the most sophisticated one─incorporating simulation and a learning model─emerges as the most robust alternative for a prospective setting. The analysis of two prospective cases, the design of both agonists and antagonists of CNR1 and the more challenging search for CXCR4 nonpeptidic agonist, reveals both great potential and inherent structural limitations, highlighting the need for an accurate and suitable three-dimensional structure.
SN - 1549-9596
SN - 1549-960X
ER -