2D QSAR of PPARγ agonist binding and transactivation.

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2D QSAR of PPARγ agonist binding and transactivation. / Rücker, Christoph; Scarsi, Marco; Meringer, Markus.
in: Bioorganic & Medicinal Chemistry, Jahrgang 14, Nr. 15, 01.08.2006, S. 5178-5195.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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Rücker C, Scarsi M, Meringer M. 2D QSAR of PPARγ agonist binding and transactivation. Bioorganic & Medicinal Chemistry. 2006 Aug 1;14(15):5178-5195. doi: 10.1016/j.bmc.2006.04.005

Bibtex

@article{1bd99c909b8e47138ef63abacd0d9107,
title = "2D QSAR of PPARγ agonist binding and transactivation.",
abstract = "Multilinear QSAR models are developed for the largest and most diverse set of PPARγ agonists treated hitherto. Binding of these small molecules to the human nuclear receptor PPARγ 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, r 2 = 0.79, r cv 2 = 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.",
keywords = "Chemistry, 2D QSAR, PPARγ agonists, Type 2 diabetes",
author = "Christoph R{\"u}cker and Marco Scarsi and Markus Meringer",
year = "2006",
month = aug,
day = "1",
doi = "10.1016/j.bmc.2006.04.005",
language = "English",
volume = "14",
pages = "5178--5195",
journal = "Bioorganic & Medicinal Chemistry",
issn = "1464-3391",
publisher = "Elsevier B.V.",
number = "15",

}

RIS

TY - JOUR

T1 - 2D QSAR of PPARγ agonist binding and transactivation.

AU - Rücker, Christoph

AU - Scarsi, Marco

AU - Meringer, Markus

PY - 2006/8/1

Y1 - 2006/8/1

N2 - Multilinear QSAR models are developed for the largest and most diverse set of PPARγ agonists treated hitherto. Binding of these small molecules to the human nuclear receptor PPARγ 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, r 2 = 0.79, r cv 2 = 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.

AB - Multilinear QSAR models are developed for the largest and most diverse set of PPARγ agonists treated hitherto. Binding of these small molecules to the human nuclear receptor PPARγ 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, r 2 = 0.79, r cv 2 = 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.

KW - Chemistry

KW - 2D QSAR

KW - PPARγ agonists

KW - Type 2 diabetes

UR - http://www.scopus.com/inward/record.url?scp=33745172856&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/88f908b5-6981-30cf-83e7-89813cccce20/

U2 - 10.1016/j.bmc.2006.04.005

DO - 10.1016/j.bmc.2006.04.005

M3 - Journal articles

VL - 14

SP - 5178

EP - 5195

JO - Bioorganic & Medicinal Chemistry

JF - Bioorganic & Medicinal Chemistry

SN - 1464-3391

IS - 15

ER -

DOI