Literature DB >> 8951225

Ligand-based identification of environmental estrogens.

C L Waller1, T I Oprea, K Chae, H K Park, K S Korach, S C Laws, T E Wiese, W R Kelce, L E Gray.   

Abstract

Comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) paradigm, was used to examine the estrogen receptor (ER) binding affinities of a series of structurally diverse natural, synthetic, and environmental chemicals of interest. The CoMFA/3D-QSAR model is statistically robust and internally consistent, and successfully illustrates that the overall steric and electrostatic properties of structurally diverse ligands for the estrogen receptor are both necessary and sufficient to describe the binding affinity. The ability of the model to accurately predict the ER binding affinity of an external test set of molecules suggests that structure-based 3D-QSAR models may be used to supplement the process of endocrine disruptor identification through prioritization of novel compounds for bioassay. The general application of this 3D-QSAR model within a toxicological framework is, at present, limited only by the quantity and quality of biological data for relevant biomarkers of toxicity and hormonal responsiveness.

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Year:  1996        PMID: 8951225     DOI: 10.1021/tx960054f

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  21 in total

1.  Receptor-based 3D QSAR analysis of estrogen receptor ligands--merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods.

Authors:  W Sippl
Journal:  J Comput Aided Mol Des       Date:  2000-08       Impact factor: 3.686

2.  Free energies of ligand binding for structurally diverse compounds.

Authors:  Chris Oostenbrink; Wilfred F van Gunsteren
Journal:  Proc Natl Acad Sci U S A       Date:  2005-03-14       Impact factor: 11.205

3.  Pharmacophore search for anti-fertility and estrogenic potencies of estrogen analogs.

Authors:  Sk Mahasin Alam; Ria Pal; Shuchi Nagar; Md Ataul Islam; Achintya Saha
Journal:  J Mol Model       Date:  2008-07-29       Impact factor: 1.810

4.  Antiestrogenic activity of anthropogenic and natural chemicals.

Authors:  J M Navas; H Segner
Journal:  Environ Sci Pollut Res Int       Date:  1998       Impact factor: 4.223

5.  The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

Authors:  Jiazhong Li; Paola Gramatica
Journal:  Mol Divers       Date:  2009-11-17       Impact factor: 2.943

6.  The Transactivating Function 2 (AF-2) of Estrogen Receptor (ER) α is Indispensable for ERα-mediated Physiological Responses and AF-1 Activity.

Authors:  Yukitomo Arao; Katherine J Hamilton; Kenneth S Korach
Journal:  Open J Endocr Metab Dis       Date:  2013-08-06

7.  Transactivation Function-2 of Estrogen Receptor α Contains Transactivation Function-1-regulating Element.

Authors:  Yukitomo Arao; Laurel A Coons; William J Zuercher; Kenneth S Korach
Journal:  J Biol Chem       Date:  2015-05-31       Impact factor: 5.157

8.  Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction.

Authors:  Daniel P Russo; Kimberley M Zorn; Alex M Clark; Hao Zhu; Sean Ekins
Journal:  Mol Pharm       Date:  2018-08-28       Impact factor: 4.939

9.  Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction.

Authors:  Kimberley M Zorn; Daniel H Foil; Thomas R Lane; Daniel P Russo; Wendy Hillwalker; David J Feifarek; Frank Jones; William D Klaren; Ashley M Brinkman; Sean Ekins
Journal:  Environ Sci Technol       Date:  2020-09-15       Impact factor: 9.028

10.  In silico prediction of estrogen receptor subtype binding affinity and selectivity using statistical methods and molecular docking with 2-arylnaphthalenes and 2-arylquinolines.

Authors:  Zhizhong Wang; Yan Li; Chunzhi Ai; Yonghua Wang
Journal:  Int J Mol Sci       Date:  2010-09-20       Impact factor: 5.923

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