Based on the classification of the minimum pK a value for each site in a molecule is set to either a '1' (lowest pK a site) or '0' (not lowest pK a site. we also introduce a tolerance where a pK a value within +1 pK a units or +2 pK a units of the lowest pK a value accepted as '1' to account for slight variations; see Table . From that, a confusion matrix (CM) is constructed to compare the predictions of the machine learning (ML) model to the calculated sites. A site is classified as a true positive (TP) or true negative (TN) if the ML model's prediction aligns with the QM computed sites. Conversely, it is labeled as a false positive (FP) or false negative (FN) if the ML prediction differs from the calculated sites. From these classifications, we derive several evaluation parameters: Accuracy (ACC), Matthew's correlation coefficient (MCC), recall/sensitivity (true-positive rate -TPR), specificity (truenegative rate -TNR), precision (positive predictive value -PPV), and negative predictive value (NPV). pK a values target taget+1 taget+2 [15, 20, 25]