Our Gaussian process model showed promising results in the SAMPL6 challenge, but was limited by the scope of our training set. The chemical space represented in our training set was limited to mono-and diprotic molecules (Section 2.5). Despite this limitation, we still saw fairly good agreement between our predicted macroscopic pK a s and the experimentally measured values and performed competitively compared to other participants. We rank in the top ten by RMSE (1.7 ± 0.3) after removing a single obvious outlier (Figure ). This outlier, with an acidic amide group, highlighted a potential hole in our limited definition of ionizable groups. Improving our model will require adding groups which are often ionized outside the aqueous pK a range, but which can be perturbed to ionize within that range. Our performance in this blind challenge is evidence that a single model trained on physically and chemically relevant features can be competitive with established methods which rely on specialized models for individual functional groups.