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Artificial Neural Network Modeling of Phytoestrogen Binding to Estrogen Receptors

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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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Keywords: ANN; ER-alpha; ER-beta; GRNN; QSAR; Theoretical descriptors

Document Type: Research Article

Affiliations: School of Medicine - Rural Clinical Division, The University of Queensland, Locked Bag 9009, Toowoomba QLD 4350, Australia.

Publication date: September 1, 2006

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  • Letters in Drug Design & Discovery publishes original letters on all areas of rational drug design and discovery including medicinal chemistry, in-silico drug design, combinatorial chemistry, high-throughput screening, drug targets, and structure-activity relationships. The emphasis will be on publishing quality papers very rapidly. Letters will be processed rapidly by taking full advantage of Internet technology for both the submission and review of manuscripts. The journal is essential reading to all pharmaceutical scientists involved in research in drug design and discovery.
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