Given the limitations of using Hammet parameters alone, we next used these summed parameters as descriptors to train a machine learning model, specifically eXtreme Gradient Boosting (XGB). We performed leave-one-out cross-validation, where the classifier was trained on all but one molecule and then used to predict the active sites in the remaining molecule. The classifier was trained to identify whether each atomic site was active, and across all sites in each molecule, the one with the highest predicted probability was defined as the most active site. This machine learning approach significantly improved prediction accuracy, yielding a cross-validation accuracy of 93 % compared to the 53% obtained by summing the Hammett parameters (Supplementary Section 6.7). However, the model's dependence on experimental Hammett parameters for different functional groups still poses limitations, particularly for more complex molecules.