Prediction of enantioselectivity using chirality codes and Classification and Regression Trees

In this paper a new application of Classification and Regression Trees, concerning the prediction of enantioselectivity, is presented. The data consists on the elution order of enantiomers separated by High-Performance Liquid Chromatography with two different chiral stationary phases. The enantiomers of both datasets were classified in two groups, named First and Last, depending on their elution order, prior to the construction of the models. Classification and Regression Trees methodology was then applied to build classification trees that allowed the prediction of the elution order of the compounds by using chirality codes as explanatory variables. The chirality codes are a set of molecular descriptors that combine different parameters and are able to distinguish between enantiomers. This new approach determined quite simple models and achieved good predictions for both datasets considered. Finally the models obtained with Classification and Regression Trees were compared with Kohonen Neural Network results. This methodology was also applied to predict the quality of the separation between two enantiomers in a certain chiral stationary phase. Previously to the construction of the model, the molecules of one of the datasets were classified in three classes (Bad, Good and Very Good), according to their degree of separation (α), and the model was built using the absolute values of the chirality codes. The results obtained for the final classification tree were quite promising.