Abstract
Volume learning algorithm (VLA) artificial neural network and partial least squares (PLS) methods were compared using the leave-one-out cross-validation procedure for prediction of relative potency of xenoestrogenic compounds to the estrogen receptor. Using Wilcoxon signed rank test we showed that VLA outperformed PLS by producing models with statistically superior results for a structurally diverse set of compounds comprising eight chemical families. Thus, CoMFA/VLA models are successful in prediction of the endocrine disrupting potential of environmental pollutants and can be effectively applied for testing of prospective chemicals prior their exposure to the environment.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Algorithms*
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Endocrine Disruptors / chemistry
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Endocrine Disruptors / metabolism
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Environmental Pollutants / chemistry
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Environmental Pollutants / metabolism
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Estrogens / chemistry*
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Estrogens / metabolism
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Estrogens, Non-Steroidal / chemistry
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Estrogens, Non-Steroidal / metabolism
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Least-Squares Analysis*
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Models, Molecular
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Molecular Structure
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Neural Networks, Computer
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Protein Binding
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Quantitative Structure-Activity Relationship
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Receptors, Estrogen / chemistry
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Receptors, Estrogen / metabolism
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Xenobiotics / chemistry
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Xenobiotics / metabolism
Substances
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Endocrine Disruptors
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Environmental Pollutants
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Estrogens
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Estrogens, Non-Steroidal
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Receptors, Estrogen
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Xenobiotics