Development of pharmacophore-based classification model for activators of constitutive androstane receptor

Kyungro Lee, Hwan You, Jiwon Choi, Kyoung Tai No

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)172-178
Number of pages7
JournalDrug Metabolism and Pharmacokinetics
Volume32
Issue number3
DOIs
Publication statusPublished - 2017 Jun 1

Fingerprint

Ligands
Computer Simulation
constitutive androstane receptor
Liver
Pharmaceutical Preparations
Proteins
Support Vector Machine

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Pharmaceutical Science
  • Pharmacology (medical)

Cite this

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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.",
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Development of pharmacophore-based classification model for activators of constitutive androstane receptor. / Lee, Kyungro; You, Hwan; Choi, Jiwon; No, Kyoung Tai.

In: Drug Metabolism and Pharmacokinetics, Vol. 32, No. 3, 01.06.2017, p. 172-178.

Research output: Contribution to journalArticle

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