We also explore whether it is better to predict the proton affinities using regression and use them to identify reactive centers, rather the classification approach. Although the Light-GBM RegioSQM20 regression model is able to achieve a mean absolute error (MAE) of 2.00 kcal/mol on the test set, the accuracy is not good enough to distinguish between reactive and non-reactive sites compared to the LightGBM RegioSQM20 model, as evidence by the low recall values of 71%-73%. However, the LightGBM RegioSQM20 regression model can be used to predict low, medium, or high reactivity as we showed in the RegioSQM20 paper. In fact, by combining the classification model and the regression model one gets both regioselectivity predictions and a qualitative prediction of the reactivity of a molecule with almost no additional cost as the atomic descriptors only have to be calculated once. Examples of the output of RegioML can be seen in Fig. .