Prediction of enantioselectivity using chirality codes and Classification and Regression Trees

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.