Abstract
86 compounds from NTP carcinogenic potency data base have been used to derive neural network models. Compounds were described with topological indices. Carcinogenicity has been given as a binary quantity - a compound is carcinogenic or non carcinogenic. Several models have been tested with a recognition ability test and with the leave-one-out cross validation method. For the best model the ratio between correct and wrong classifications was 70/30. Furthermore, the model has been used to classify 17 compounds not used for setting of the models. The predicted carcinogenic classes and the neighbors in the neural network influencing the predictions have been discussed.
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