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Abstract
A fundamental concern in the Quantitative Structure-Activity Relationship approach to toxicity evaluation is the generalization of the model over a wide range of compounds. The data driven modelling of toxicity, due to the complex and ill-defined nature of eco-toxicological systems, is an uncertain process. The development of a toxicity predicting model without considering uncertainties may produce a model with a low generalization performance. This study presents a novel approach to toxicity modelling that handles the involved uncertainties using a fuzzy filter, and thus improves the generalization capability of the model. The method is illustrated by considering a data set dealing with the fathead minnow (Pimephales promelas) toxicity of 568 organic compounds.
Acknowledgements
We acknowledge Deutsche Bundesstiftung Umwelt for the financial support of Shefali Kumar. We thank Dr. Emilio Benfenati (Instituto Mario Negri, Milan, Italy) for providing us the toxicity and other data. This dataset was used in the European Community project IMAGETOX (Intelligent Modelling Algorithms for General Evaluation of TOXicities).