Overall, the reported ML methods predicting pKa are generally based on fingerprints or graphs encoding the structural and/or electronic information of the molecules. However, while these models provide satisfactory accuracies (mean absolute error -MAE below 1 pKa unit), seldom are inductive or resonance effects mentioned. Instead, descriptors such as stereochemistry (no direct correlation with pKa) are sometimes used or a method to automatically select the features may be opted for. In addition, these models are trained on either one form (acid or base) or on acids and bases separately. However, resonance and inductive stabilization may be observed in only one form. Finally, most published models in the literature do not discuss the similarities between their training and testing sets. This can lead to models memorizing the various chemical families they have seen, hence reducing the overall accuracy on compounds outside the training chemical space.