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Titolo:
On the PNN modeling of estrogen receptor binding data for carboxylic acid esters and organochlorine compounds
Autore:
Kaiser, KLE; Niculescu, SP;
Indirizzi:
Natl Water Res Inst, Burlington, ON L7R 4A6, Canada Natl Water Res Inst Burlington ON Canada L7R 4A6 gton, ON L7R 4A6, Canada TerraBase Inc, Burlington, ON L7N 3L5, Canada TerraBase Inc Burlington ONCanada L7N 3L5 Burlington, ON L7N 3L5, Canada
Titolo Testata:
WATER QUALITY RESEARCH JOURNAL OF CANADA
fascicolo: 3, volume: 36, anno: 2001,
pagine: 619 - 630
SICI:
1201-3080(2001)36:3<619:OTPMOE>2.0.ZU;2-9
Fonte:
ISI
Lingua:
ENG
Keywords:
estrogen receptor binding; probabilistic neural network; modeling; endocrine disruptors; CEPA; DSL;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Citazioni:
11
Recensione:
Indirizzi per estratti:
Indirizzo: Kaiser, KLE Natl Water Res Inst, 867 Lakeshore Rd, Burlington, ON L7R 4A6,Canada Natl Water Res Inst 867 Lakeshore Rd Burlington ON Canada L7R 4A6
Citazione:
K.L.E. Kaiser e S.P. Niculescu, "On the PNN modeling of estrogen receptor binding data for carboxylic acid esters and organochlorine compounds", WAT QUAL RE, 36(3), 2001, pp. 619-630

Abstract

We describe the relationship between the estrogen receptor binding and themolecular structure of chemicals using the probabilistic neural network methodology with structural fragment descriptors as input variables and a data set of 1118 compounds. Exploratory models identified two subsets of chemicals for which the predictions were well correlated with the measured values, namely chlorine-containing compounds and carboxylic esters, and for which individual models were developed. Both compound classes are in the classification system for chemicals on the Canadian Domestic Substances List (DSL) and the data cover five orders of magnitude in activity in each of these classes. The results show excellent performance of both models and are highly encouraging in the search for other models for this and other receptor binding data as well as other classes of DSL substances. They also confirm the flexibility, usefulness and applicability of the probabilistic neural networks as modeling methodology to a wide variety of modeling challenges in the environmental and health fields.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 19/01/20 alle ore 20:44:33