Predicting experimental yields as an index to rank synthesis routes: application for Diels–Alder reactions

It is possible to create novel synthetic routes for compounds using synthesis route design systems (SRDS). We have been investigating an in silico screening protocol, which makes it possible to reduce the number of SRDS experiments in developing new synthesis routes. However, there still remains the problem of how to rank synthesis routes for experiments. The experimental yield is considered to be one of the most important factors in determining which synthesis route is better. The present study describes an attempt toward predicting the trends of experimental yields for organic synthesis by fusing computational chemistry and chemoinformatics. We examined whether the prediction of experimental yields for Diels–Alder reactions is feasible using activation energies obtained from Density Functional Theory (DFT) calculations together with the experimental conditions. A partial least squares analysis using these values gave correlation equations for the experimental yields. If it is possible to construct similar correlation equations for other reactions, then SRDS synthetic routes could be ranked on the basis of their predicted yields, and an order can be determined before beginning the experiments.