The test set MCC of RegioML is virtually identical to the Weisfeiler-Lehman neural network (WLN) architecture specifically trained to predict the regioselectivity of EAS reactions by Struble et al. . While the precision is 4% higher for WLN, the recall (the fraction of positives that are predicted correctly) is 5% lower leading to an nearly identical overall performance. WLN performs better on the out-of-sample set, with an MCC value that is nearly identical to that off the test set. However, it should be noted that many of the molecules in these two sets are likely included in the set used to train the WLN method, which is likely to inflate the WLN MCC values. For example, we are able to achieve MCC values of 0.98 on both the test and out-of-sample sets by training the LightGBM model on the entire collection of data using 10-fold cross-validation (the MCC value is for the best performing model).