PXR ligand classification model with SFED-weighted WHIM and CoMMA descriptors
- Authors
- Ma, S. L.; Joung, J. Y.; Lee, S.; Cho, K. H.; No, K. T.
- Issue Date
- 2012
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- pregnane X receptor; solvation free energy density model; WHIM; CoMMA; machine learning
- Citation
- SAR AND QSAR IN ENVIRONMENTAL RESEARCH, v.23, no.5-6, pp.485 - 504
- Journal Title
- SAR AND QSAR IN ENVIRONMENTAL RESEARCH
- Volume
- 23
- Number
- 5-6
- Start Page
- 485
- End Page
- 504
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/13492
- DOI
- 10.1080/1062936X.2012.665385
- ISSN
- 1062-936X
- Abstract
- Understanding which type of endogenous and exogenous compounds serve as agonists for the nuclear pregnane X receptor (PXR) would be valuable for drug discovery and development, because PXR regulates a large number of genes related to xenobiotic metabolism. Although several models have been proposed to classify human PXR activators and non-activators, models with better predictability are necessary for practical purposes in drug discovery. Grid-weighted holistic invariant molecular (G-WHIM) and comparative molecular moment analysis (G-CoMMA) type 3D descriptors that contain information about the solvation free energy of target molecules were developed. With these descriptors, prediction models built using decision tree (DT)-, support vector machine (SVM)-, and Kohonen neural network (KNN)-based models exhibited better predictability than previously proposed models. Solvation free energy density-weighted G-WHIM and G-CoMMA descriptors reveal new insights into PXR ligand classification, and incorporation with machine learning methods (DT, SVM, KNN) exhibits promising results, especially SVM and KNN. SVM- and KNN-based models exhibit accuracy around 0.90, and DT-based models exhibit accuracy around 0.8 for both the training and test sets.
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Collections - College of Natural Sciences > School of Systems and Biomedical Science > 1. Journal Articles
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