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Prediction of toxicity using a novel RBF neural network training methodology

Journal of Molecular Modeling volume 12, pages 297–305 (2006)Cite this article

Abstract

A neural network methodology based on the radial basis function (RBF) architecture is introduced in order to establish quantitative structure-toxicity relationship models for the prediction of toxicity. The dataset used consists of 221 phenols and their corresponding toxicity values to Tetrahymena pyriformis. Physicochemical parameters and molecular descriptors are used to provide input information to the models. The performance and predictive abilities of the RBF models are compared to standard multiple linear regression (MLR) models. The leave-one-out cross validation procedure and validation through an external test set produce statistically significant R 2 and RMS values for the RBF models, which prove considerably more accurate than the MLR models.

Experimental vs predicted toxicity using the RBF methodology for the test set (41 compounds)

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Acknowledgements

G.M. wishes to thank the Greek State Scholarship Foundation for a doctoral assistantship.

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

    Georgia Melagraki, Antreas Afantitis, Kalliopi Makridima, Haralambos Sarimveis & Olga Igglessi-Markopoulou

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  1. Georgia Melagraki
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  2. Antreas Afantitis
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  3. Kalliopi Makridima
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  4. Haralambos Sarimveis
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  5. Olga Igglessi-Markopoulou
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Correspondence to Haralambos Sarimveis.

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Melagraki, G., Afantitis, A., Makridima, K. et al. Prediction of toxicity using a novel RBF neural network training methodology. J Mol Model 12, 297–305 (2006). https://doi.org/10.1007/s00894-005-0032-8

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  • DOI: https://doi.org/10.1007/s00894-005-0032-8

Keywords

  • RBF architecture
  • Neural network
  • QSTR
  • Toxicity
  • Tetrahymena pyriformis