Literature DB >> 16650995

2D QSAR of PPARgamma agonist binding and transactivation.

Christoph Rücker1, Marco Scarsi, Markus Meringer.   

Abstract

Multilinear QSAR models are developed for the largest and most diverse set of PPARgamma agonists treated hitherto. Binding of these small molecules to the human nuclear receptor PPARgamma is described by models that are built on simple 2D molecular descriptors and nevertheless are of good quality and predictive power (e.g., 144 compounds, 10 descriptors, r2=0.79, r2(cv)=0.76). The models presented are thoroughly validated by crossvalidation, randomization experiments, bootstrapping, and training set/test set partitioning. They may therefore be helpful in the design of new antidiabetic drug candidates. For gene transactivation, the functional activity of the agonists, a corresponding model for a similarly diverse compound set is of somewhat lower statistical quality.

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Year:  2006        PMID: 16650995     DOI: 10.1016/j.bmc.2006.04.005

Source DB:  PubMed          Journal:  Bioorg Med Chem        ISSN: 0968-0896            Impact factor:   3.641


  3 in total

1.  How the energy evaluation method used in the geometry optimization step affect the quality of the subsequent QSAR/QSPR models.

Authors:  Asmund Rinnan; Niels Johan Christensen; Søren Balling Engelsen
Journal:  J Comput Aided Mol Des       Date:  2009-11-27       Impact factor: 3.686

2.  Identification of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists using molecular modeling method.

Authors:  Veronica M W Gee; Fiona S L Wong; Lalitha Ramachandran; Gautam Sethi; Alan Prem Kumar; Chun Wei Yap
Journal:  J Comput Aided Mol Des       Date:  2014-08-29       Impact factor: 3.686

3.  Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures.

Authors:  Myungwon Seo; Chong Hak Chae; Yuno Lee; Ha Ryong Kim; Jongwoon Kim
Journal:  Toxics       Date:  2021-03-16
  3 in total

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