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.

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.

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The intricate involvement of the serotonin 5-HT2A receptor (5-HT2AR) both in schizophrenia and in the activity of antipsychotic drugs is widely acknowledged. The currently marketed antipsychotic drugs, although effective in managing the symptoms of schizophrenia to a certain extent, are not without their repertoire of serious side effects. There is a need for better therapeutics to treat schizophrenia for which understanding the mechanism of action of the current antipsychotic drugs is imperative. With bioluminescence resonance energy transfer (BRET) assays, we trace the signaling signature of six antipsychotic drugs belonging to three generations at the 5-HT2AR for the entire spectrum of signaling pathways activated by serotonin (5-HT). The antipsychotic drugs display previously unidentified pathway preference at the level of the individual Gα subunits and β-arrestins. In particular, risperidone, clozapine, olanzapine and haloperidol showed G protein-selective inverse agonist activity. In addition, G protein-selective partial agonism was found for aripiprazole and cariprazine. Pathway-specific apparent dissociation constants determined from functional analyses revealed distinct coupling-modulating capacities of the tested antipsychotics at the different 5-HT-activated pathways. Computational analyses of the pharmacological and structural fingerprints supports a mechanistically based clustering that recapitulate the clinical classification (typical/first generation, atypical/second generation, third generation) of the antipsychotic drugs. The study provides a new framework to functionally classify antipsychotics that should represent a useful tool for the identification of better and safer neuropsychiatric drugs and allows formulating hypotheses on the links between specific signaling cascades and in the clinical outcomes of the existing drugs.