G-Protein-Coupled Receptor Affinity Prediction Based on the Use of a Profiling Dataset:  QSAR Design, Synthesis, and Experimental Validation

• Published in: Journal of medicinal chemistry Vol. 48; no. 21; pp. 6563 - 6574
• Main Authors: Rolland, Catherine, Gozalbes, Rafael, Nicolaï, Eric, Paugam, Marie-France, Coussy, Laurent, Barbosa, Frédérique, Horvath, Dragos, Revah, Frédéric
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 20-10-2005
• Subjects: Biological and medical sciences; Combinatorial Chemistry Techniques; Drug Design; Ligands; Medical sciences; Miscellaneous; Models, Molecular; Pharmacology. Drug treatments; Quantitative Structure-Activity Relationship; Radioligand Assay; Receptors, Chemokine - chemistry; Receptors, Chemokine - metabolism; Receptors, G-Protein-Coupled - chemistry; Receptors, G-Protein-Coupled - metabolism; Receptors, Peptide - chemistry; Receptors, Peptide - metabolism; Transmembrane protein; Signal transduction; Biological fixation; Structure activity relation; Ligand; Chemical compound library; Molecular model; Prediction; Affinity; Modeling; G protein; Biological receptor
• ISSN: 0022-2623; 1520-4804
• DOI: 10.1021/jm0500673

A QSAR model accounting for “average” G-protein-coupled receptor (GPCR) binding was built from a large set of experimental standardized binding data (1939 compounds systematically tested over 40 different GPCRs) and applied to the design of a library of “GPCR-predicted” compounds. Three hundred and sixty of these compounds were randomly selected and tested in 21 GPCR binding assays. Positives were defined by their ability to inhibit by more than 70% the binding of reference compounds at 10 μM. A 5.5-fold enrichment in positives was observed when comparing the “GPCR-predicted” compounds with 600 randomly selected compounds predicted as “non-GPCR” from a general collection. The model was efficient in predicting strongest binders, since enrichment was greater for higher cutoffs. Significant enrichment was also observed for peptidic GPCRs and receptors not included to develop the QSAR model, suggesting the usefulness of the model to design ligands binding with newly identified GPCRs, including orphan ones.