Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas): a study based on 865 compounds.

We investigate the use of probabilistic neural networks (PNN) to model the acute toxicity (96-hr LC50) to the fathead minnow (Pimephales promelas) based on a 865 chemicals data set. In contrast to most other toxicological models, the octanol/water partition coefficient is not used as input parameter. The information fed into the neural network is solely based on simple molecular descriptors as can be derived from the chemicals' structures and indicates the potential of this approach as general methodology for the estimation of toxicological effects of chemicals.