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 modelling 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 modelling 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. The predictions of Kovats indices with neural networks are better than predictions from regression equations by a factor of about 1.25 when the correlation coefficient between the structural descriptors and retention index is low. However, when the correlation coefficient is high, the regression predictions are better than the neural network predictions by factors between 1.5 and 2.0.