Artificial Neural Network Modeling of Phytoestrogen Binding to Estrogen Receptors

S Agatonovic-Kustrin; J V Turner

doi:10.2174/157018006778194871

Artificial Neural Network Modeling of Phytoestrogen Binding to Estrogen Receptors

S Agatonovic-Kustrin
, J V Turner

Medicine

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

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.

Original language	English
Pages (from-to)	436-442
Journal	Letters in Drug Design and Discovery
Volume	3
Issue number	7
DOIs	https://doi.org/10.2174/157018006778194871
Publication status	Published - 2006

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https://hdl.handle.net/1959.11/28208

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title = "Artificial Neural Network Modeling of Phytoestrogen Binding to Estrogen Receptors",

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.",

author = "S Agatonovic-Kustrin and Turner, \{J V\}",

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T1 - Artificial Neural Network Modeling of Phytoestrogen Binding to Estrogen Receptors

AU - Agatonovic-Kustrin, S

AU - Turner, J V

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N2 - 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.

AB - 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.

U2 - 10.2174/157018006778194871

DO - 10.2174/157018006778194871

M3 - Article

SN - 1570-1808

VL - 3

SP - 436

EP - 442

JO - Letters in Drug Design and Discovery

JF - Letters in Drug Design and Discovery

IS - 7

ER -

Artificial Neural Network Modeling of Phytoestrogen Binding to Estrogen Receptors

Abstract

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