When we evaluate our ML models on the whole out-of-sample set, we again find the regression model (MCC of 0.82) outperforms the classification model (MCC of 0.70) when used as a binary classifier as seen in Table . While a direct comparison cannot be made between Roszak et al. 's results and ours, we find our result to outperform theirs with an accuracy of 0.96. In general, it is surprising that the LightGBM regressor outperforms our LightGBM classifier as Ree et al. has shown the opposite to be true for electrophilic aromatic substitutions. However, our regression model serves a dual function: it accurately predicts pK a values and identifies the reaction site.