title: Development of pharmacophore-based classification model for activators of constitutive androstane receptor.
abstract: Constitutive androstane receptor (CAR) is predominantly expressed in the liver and is important for regulating drug metabolism and transport. Despite its biological importance, there have been few attempts to develop in silico models to predict the activity of CAR modulated by chemical compounds. The number of in silico studies of CAR may be limited because of CAR's constitutive activity under normal conditions, which makes it difficult to elucidate the key structural features of the interaction between CAR and its ligands. In this study, to address these limitations, we introduced 3D pharmacophore-based descriptors with an integrated ligand and structure-based pharmacophore features, which represent the receptor-ligand interaction. Machine learning methods (support vector machine and artificial neural network) were applied to develop an in silico model with the descriptors containing significant information regarding the ligand binding positions. The best classification model built with a solvent accessibility volume-based filter and the support vector machine showed good predictabilities of 87%, and 85.4% for the training set and validation set, respectively. This demonstrates that our model can be used to accurately predict CAR activators and offers structural information regarding ligand/protein interactions.
authors.affiliation: Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, South Korea.
authors.forename: Kyungro
authors.initials: K
authors.lastname: Lee
authors.affiliation: Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, South Korea.
authors.forename: Hwan
authors.initials: H
authors.lastname: You
authors.affiliation: Bioinformatics & Molecular Design Research Center, Yonsei University, Seoul 03722, South Korea.
authors.forename: Jiwon
authors.initials: J
authors.lastname: Choi
authors.affiliation: Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, South Korea; Bioinformatics & Molecular Design Research Center, Yonsei University, Seoul 03722, South Korea. Electronic address: ktno@bmdrc.org.
authors.forename: Kyoung Tai
authors.initials: KT
authors.lastname: No
mesh_terms: D000090702:Constitutive Androstane Receptor; D006801:Humans; D008024:Ligands; D016571:Neural Networks, Computer; D018160:Receptors, Cytoplasmic and Nuclear / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D000090702:Constitutive Androstane Receptor; D008024:Ligands; D018160:Receptors, Cytoplasmic and Nuclear
keywords: Classification; Constitutive androstane receptor; Machine learning; Pharmacophore-based descriptor; Support vector machine