Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study

M Vračko, V Bandelj, P Barbieri… - SAR and QSAR in …, 2006 - Taylor & Francis
M Vračko, V Bandelj, P Barbieri, E Benfenati, Q Chaudhry, M Cronin, J Devillers, A Gallegos…
SAR and QSAR in Environmental Research, 2006Taylor & Francis
The OECD has proposed five principles for validation of QSAR models used for regulatory
purposes. Here we present a case study investigating how these principles can be applied
to models based on Kohonen and counter propagation neural networks. The study is based
on a counter propagation network model that has been built using toxicity data in fish
fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the
OECD criteria may be met when modeling using this neural network approach.
The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.
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