Finally, the MAE measurement on the relative testing set of these various models is not sufficient to fully compare and analyze their performance. Analyzing the predicted pKa versus label pKa graph allows us to better compare the accuracies of different models. At this stage, identifying these outliers is essential as they can reveal systematic errors and biases in the models. As shown in Figure and S6-8, some molecules in the test set have large prediction errors (up to 10 pKa units). These errors might not be only due to the prediction of the models but can also come from choices made in our encoding of the different chemical functional groups recognized by our models. For instance, we did not encode the recognition of simple alcohols as a potential ionization centers as most of these groups would present a pKa value over 14. Considering the total number of datapoints, the number of outliers identified from the plots is relatively small. We want to emphasize that the accuracies as presented in figure ) have to be taken with care as the training sets used to trained these different models (1) varies and, (2) may (e.g., Epik 7) or may not (QupKake 28 ) include molecules from the Novartis test set