More strikingly, removing conjugation led to a significant improvement (entry 1, median MAE = 0.68 vs. entry 14, median MAE = 0.64), while we believed this feature would be a key feature to model conjugation. A close look at the assignment of conjugated bonds in RDKit, the python library used to compute conjugation, revealed that a carboxylic acid and a carboxylate are equally conjugated. Yet, the conjugation is more pronounced in carboxylates. To address this issue, we implemented our own conjugation feature and included it in our model (entry 20) as described in Figure . While the addition of the original conjugation feature decreased the accuracy (from entry 14 to entry 1), introducing this new conjugation definition does not (from entry 14 to entry 20). This new definition further increases the difference between our chemical principle-based model (entry 20, MAE= 0.65) and more categorical variable-based models (entry 4, MAE = 0.73) and traditional models (XGB, MAE = 0.92, Figure ) and further demonstrates the need for properly defined features rather than pre-established features. As a note, this RDKit function is used in many previously reported models, while we show here that it is not appropriate for pKa prediction. Similarly, removing bond order and bond polarization led to the largest losses (entry 1 vs. entries 13 and 15, median MAE = 0.72 and 0.73), indicating a more pronounced impact of bond features over atom features.