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Article
Modeling the toxicity of chemicals to Tetrahymena pyriformis using molecular fragment descriptors and probabilistic neural networks
Archives of environmental contamination and toxicology (2000)
  • S P Niculescu
  • K L Kaiser
  • Terry W Schultz, University of Tennessee - Knoxville
Abstract

The results of an investigation into the use of a probabilistic neural network (PNN)-based methodology to model the 48-h ICG50 (inhibitory concentration for population growth) sublethal toxicity of 825 chemicals to the ciliate Tetrahymena pyriformis are presented. The information fed into the neural networks is solely based on simple molecular descriptors as can be derived from the chemical structure. In contrast to most other toxicological models, the octanol/water partition coefficient is not used as an input parameter, and no rules of thumb or other substance selection criteria are employed. The cross-validation and external validation experiments confirmed excellent recognitive and predictive capabilities of the resulting models and recommend their future use in evaluating the potential of most organic molecules to be toxic to Tetrahymena.

Publication Date
October, 2000
Citation Information
S P Niculescu, K L Kaiser and Terry W Schultz. "Modeling the toxicity of chemicals to Tetrahymena pyriformis using molecular fragment descriptors and probabilistic neural networks" Archives of environmental contamination and toxicology Vol. 39 Iss. 3 (2000)
Available at: http://works.bepress.com/terry_schultz/71/