Abstract
The application of Probabilistic Neural Networks (PNNs) as modeling tools for the relative binding affinities of steroids to the progesterone receptor is investigated using a benchmark steroids data set. The results point towards the basic PNN with Gaussian kernel as the best model reported until present. The use of nonparametric statistics to confirm a model's potential to be used as a design/screening tool is also discussed.
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