title: Predictive models of Cannabinoid-1 receptor antagonists derived from diverse classes.
abstract: Chemical database design is an important consideration for screening processes in drug discovery. More specifically, classification of a diverse compound set deeply influences the validation and the predictive power of prediction model for the designing of novel compounds. In this work, we investigated the effect of the reasonable classification on the prediction model. We first collected the known Cannabinoid-1 receptor antagonists. Following this, we calculate the chemical descriptors in order to classify the collected compounds. Finally, we build two predictive models via the 3D-QSAR using different molecular alignment and the alignment independent Molecular Interaction Field models.
authors.affiliation: Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology, Yuseong-gu, Daejeon, Republic of Korea. nskang@krict.re.kr
authors.forename: Nam Sook
authors.initials: NS
authors.lastname: Kang
authors.forename: Gil Nam
authors.initials: GN
authors.lastname: Lee
authors.forename: Sung-Eun
authors.initials: SE
authors.lastname: Yoo
mesh_terms: D016208:Databases, Factual; D055808:Drug Discovery; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D043884:Receptor, Cannabinoid, CB1 / Q000037:antagonists & inhibitors* / Q000378:metabolism
chemical_list: D043884:Receptor, Cannabinoid, CB1