Computational modeling of selective pharmacophores at the alpha1-adrenergic receptors

1. Introduction2. Ligand-based and receptor-based pharmacophore modeling and QSAR analysis3. The general 1-AR pharmacophore3.1. Ligand-based pharmacophore and virtual screening 3.1.1. Prazosin analogues (2,4-diamino-6,7-dimethoxyquinazoline derivatives)3.1.2. 1,4-benzodioxan (WB-4101) related compounds3.1.3. Arylpiperazine derivatives4. Modeling the 1-AR subtype selectivities of different antagonist classes4.1.1. Supermolecule based subtype pharmacophore and QSAR models4.1.2. Ligand based subtype pharmacophores4.1.3. Receptor-based subtype pharmacophore and ligand-target/antitarget interaction-based QSAR5. Antitarget modeling of biogenic amine-binding GPCRs 6. Inverse agonism: an alternative way to interpret the 1a/1b-selectivity issue6.1 In vitro functional screening of 1a and 1b ligands.6.2 Ligand- and receptor-based structural interpretation of inverse agonism67. Concluding remarks 67.1. From molecules to pharmacophores to descriptors to models 78. Perspectives8.1. Multiscale computational modeling of GPCRs8.2. Molecular systems biology and pharmacology: “network drugs”