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A Novel RBF Neural Network Training Methodology to Predict Toxicity to Vibrio Fischeri

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Summary

This work introduces a neural network methodology for developing QSTR predictors of toxicity to Vibrio fischeri. The method adopts the Radial Basis Function (RBF) architecture and the fuzzy means training strategy, which is fast and repetitive, in contrast to most traditional training techniques. The data set that was utilized consisted of 39 organic compounds and their corresponding toxicity values to Vibrio fischeri, while lipophilicity, equalized electronegativity and one topological index were used to provide input information to the models. The performance and predictive ability of the RBF model were illustrated through external validation and various statistical tests. The proposed methodology can be used to successfully model toxicity to Vibrio fischerifor a heterogeneous set of compounds.

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Authors and Affiliations

  1. Laboratory of Organic Chemistry, School of Chemical Engineering, National Technical University of Athens, 9, Heroon Polytechniou Str., Zografou Campus, Athens, 15780, Greece

    Georgia Melagraki, Antreas Afantitis & Olga Igglessi-Markopoulou

  2. Laboratory of Process Control & Informatics, School of Chemical Engineering, National Technical University of Athens, 9, Heroon Polytechniou Str., Zografou Campus, Athens, 15780, Greece

    Haralambos Sarimveis & Alex Alexandridis

Authors
  1. Georgia Melagraki
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  2. Antreas Afantitis
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  3. Haralambos Sarimveis
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  4. Olga Igglessi-Markopoulou
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  5. Alex Alexandridis
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Correspondence to Haralambos Sarimveis.

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Melagraki, G., Afantitis, A., Sarimveis, H. et al. A Novel RBF Neural Network Training Methodology to Predict Toxicity to Vibrio Fischeri. Mol Divers 10, 213–221 (2006). https://doi.org/10.1007/s11030-005-9008-y

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  • DOI: https://doi.org/10.1007/s11030-005-9008-y

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