QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY

摘要: G protein coupled receptors (GPCRs) are the largest receptor family in mammalian genomes and known to regulate wide variety of signals such as ions, hormones neurotransmitters. It has been estimated that GPCRs represent more than 30% current drug targets have attracted many pharmaceutical industries well academic groups for potential discovery. Cannabinoid (CB) receptors, members GPCR superfamily, also involved activation multiple intracellular signal transductions their endogenous ligands or cannabinoids pharmacological research because therapeutic effects. In particular, cannabinoid subtype-2 (CB2) is be immune system its developed drugs treat disorders without psychotic side-effects. Therefore, this work was focused on discovering novel CB2 by developing quantitative structure-activity relationship (QSAR) methods performing virtual experimental screenings. Three QSAR were predict biological activities binding affinities ligands. first method, a traditional fragment-based approach improved introducing fragment similarity concept enhanced prediction accuracy remarkably. second pharmacophoric morphological descriptors incorporated derive regression model with good accuracy. third fingerprint-based artificial neural network overcome similar scaffold requirement other 3D-QSAR methods. These provide foundation screening hit ranking chemical from large space. addition, several selective within nM discovered. proven inverse agonists validated functional assays could useful probes study signaling candidates autoimmune disesases.