On the PNN Modeling of Estrogen Receptor Binding Data for Carboxylic Acid Esters and Organochlorine Compounds

摘要

We describe the relationship between the estrogen receptor binding and the molecular structure of chemicals using the probabilistic neural network methodology with structural fragment descriptors as input variables and a data set of 1118 compounds. Exploratory models identified two subsets of chemicals for which the predictions were well correlated with the measured values, name- ly chlorine-containing compounds and carboxylic esters, and for which individ- ual models were developed.