Abstract
Differential pathophysiological roles of estrogen receptors alpha (ERα) and beta (ERβ) are of particular interest for phytochemical screening. A QSAR incorporating theoretical descriptors was developed in the present study utilizing sequential multiple-output artificial neural networks. Significant steric, constitutional, topological and electronic descriptors were identified enabling ER affinity differentiation.
Keywords: QSAR, ANN, GRNN, Theoretical descriptors, ER-alpha, ER-beta
Letters in Drug Design & Discovery
Title: Artificial Neural Network Modeling of Phytoestrogen Binding to Estrogen Receptors
Volume: 3 Issue: 7
Author(s): S. Agatonovic-Kustrin and J. V. Turner
Affiliation:
Keywords: QSAR, ANN, GRNN, Theoretical descriptors, ER-alpha, ER-beta
Abstract: Differential pathophysiological roles of estrogen receptors alpha (ERα) and beta (ERβ) are of particular interest for phytochemical screening. A QSAR incorporating theoretical descriptors was developed in the present study utilizing sequential multiple-output artificial neural networks. Significant steric, constitutional, topological and electronic descriptors were identified enabling ER affinity differentiation.
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Cite this article as:
Agatonovic-Kustrin S. and Turner V. J., Artificial Neural Network Modeling of Phytoestrogen Binding to Estrogen Receptors, Letters in Drug Design & Discovery 2006; 3 (7) . https://dx.doi.org/10.2174/157018006778194871
DOI https://dx.doi.org/10.2174/157018006778194871 |
Print ISSN 1570-1808 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-628X |
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