We utilized the OPERA pKa dataset as it was the largest easily available and labeled dataset for small molecule pKa. Before model training we first standardized the SMILES strings using RDkit and removed any duplicates. Then we normalized the pKa's to 0 mean and unit variance. Note that this means that for experiments 1-10 we are reporting the RMSE and R of normalized units! In order to create CCV folds, we utilized the MACCS keys representation and created clusters using a Tanimoto similarity threshold of 0.8.