As can be seen in Table (see Tables , S4 and S5 for complete data), our models trained on our training set with the molecules with carbon ionization centers removed demonstrated an average error below 0.6 (entry 2, entry 1 includes carbon ionization centers) outperforming MolGpKa (median MAE = 0.69). The testing on the Novartis set led to models with MAE below 0.4 whether the training set was designed to be different from this set (entry 3), excluding all (entry 4) or including some (entry 5) molecules from the Novartis set. We even reached 0.30 when the ionization center is given to the model. These levels of accuracy were significantly better than those reported with other programs (Figure ), further demonstrating the power of our teaching-based approach. Similarly, when random training/testing sets were used, we, once more, observed an apparent reduction of error (entry 5), likely related to partial memorization and confirming the need for properly designed training and testing sets.