Counter-propagation neural networks in the modeling and prediction of Kovats indexes for substituted phenols

Counter-propagation neural networks are applied to the problem of modeling and predicting the Kovats indices of a set of substituted phenols from their nonempirical structural descriptors. The results are compared to those obtained from quantitative structure-chromatographic retention relationships in the form of multivariate linear regression equations. I find that the neural networks are significantly better at modeling the data, typically giving root mean square errors in Kovats indices between 0 and 10, whereas linear regression equations typically give root mean square errors between 50 and 150