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Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study |
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| Authors: | M Vračko V Bandelj P Barbieri E Benfenati Q Chaudhry M Cronin |
| Institution: | 1. European Chemical Bureau , Institute for Health and Consumer Protection , European Commission Joint Research Centre, 21020 Ispra, Italy;2. Kemijski in?titut/National Institute of Chemistry , Hajdrihova 19, 1000 Ljubljana, Slovenia marjan.vracko@ki.si;4. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale , B. go Grotta Gigante-Bri??iki 42/C, Sgonico-Zgonik (TS), Italy;5. Department of Chemical Sciences , University of Trieste , Via L. Giorgieri 1, I-34127 Trieste, Italy;6. Istituto di Ricerche Farmacologiche Mario Negri , Via Eritrea 62, 20157 Milano, Italy;7. Central Science Laboratory , Sand Hutton, York YO41 1LZ, UK;8. School of Pharmacy and Chemistry , Liverpool John Moores University , Byrom Street, Liverpool L3 3AF, UK |
| Abstract: | 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. |
| Keywords: | Validation of QSAR models Counter propagation neural network Duluth database |