K.L.E. Kaiser¹, Stefan P. Niculescu², Kathleen M. Gough³

Neural network modeling of Vibrio fischeri and fathead minnow acute toxicity data with molecular indicator variables and physico-chemical bulk parameters.

¹National Water Research Institute, Burlington, ON, L7R 4A6, Canada, E-mail: klaus.kaiser@cciw.ca;
² TerraBase Inc., Burlington, ON, L7N 3L5, Canada, E-mail: stenic@globalserve.net
³University of Manitoba, Winnipeg, MAN, R3T 2N2, Canada, E-mail: kmgough@ms.umanitoba.ca

We have investigated the predictability of non-congeneric acute toxicity data for fathead minnow (over 400 compounds) and Vibrio fischeri bacteria (over 1200 chemicals) using single linear [1], multiple linear [1], principal component analysis [2], feed forward backpropagation [3] and probabilistic [4] neural networks, using different data pre-processing and kernel choices [4]. As independent parameters served approximately 50 simple structural indicator variables for specific functional groups and molecular moieties and the exploded molecular formula. The presence of a measured physico-chemical bulk parameter (e.g. octanol/water partition coefficient, aqueous solubility) provides improved models as determined by the mean squared errors [5].

The resulting probabilistic neural network models of non-congeneric data are far superior to traditional types of structure-activity relationships and encourage further investigations with other fragment and computed molecular parameters.

References:

1. Kaiser, K.L.E.; Niculescu, S.P.; McKinnon, M.B. 1997. On the SLR, the MLR and the Elementary PNN with Gaussian kernel's performance in modeling toxicity values to fathead minnow based on Microtox data, the octanol/water partition coefficient and various structural descriptors for a 419 compounds data set. In: Quantitative Structure-Activity Relationships in Environmental Sciences - VII. F. Chen and G. Schüürmann (eds.), SETAC Press, Pensacola, FL, p. 285-297.
2. Schüürmann, G. et al.; unpublished results.
3. Kaiser, K.L.E.; Niculescu, S.P.; Schüürmann, G. 1997. Feed forward backpropagation neural networks and their use in predicting the acute toxicity of chemicals to the fathead minnow. Water Qual. Res. J. Canada 32: 637-657.
4. Niculescu, S.P.; Kaiser, K.L.E.; Schüürmann, G. 1998. Influence of data preprocessing and kernel selection on probabilistic neural network modeling of the acute toxicity of chemicals to the fathead minnow and Vibrio fischeri bacteria. Water Qual. Res. J. Canada 33: 153-165.
5. Kaiser, K.L.E.; Niculescu, S.P. 1998. Neural network modeling of Microtox toxicity data with structural physico-chemical parameters and molecular indicator variables. Poster, QSAR 98, May 1998, Baltimore, MD.

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