Unfortunately, the set of pK a values collected in this work largely overlapped with the training set of pKa-ANI. As the evaluation of an ML approach on its training set should not be used to judge the accuracy of the method, we present this evaluation only for the sake of completeness (Figure ). As expected, pKa-ANI performance on the full dataset was strong, exceeding the other methods with respect to AUE (0.44 ± 0.07 pK) and Pearson correlation coefficient (0.87±0.05).