Prediction of enantiomeric excess in a combinatorial library of catalytic enantioselective reactions

A quantitative structure-enantioselectivity relationship was established for a combinatorial library of enantioselective reactions performed by addition of diethyl zinc to benzaldehyde. Chiral catalysts and additives were encoded by their chirality codes and presented as input to neural networks. The networks were trained to predict the enantiomeric excess. With independent test sets, predictions of enantiomeric excess could be made with an average error as low as 6% ee. Multilinear regression, perceptrons, and support vector machines were also evaluated as modeling tools. The method is of interest for the computer-aided design of combinatorial libraries involving chiral compounds or enantioselective reactions. This is the first example of a quantitative structure-property relationship based on chirality codes.