To learn about chemical properties that pose challenges for pK a predictions, we analyzed the prediction errors of the molecules (Figure ). For reference, compound classes and structures of the molecules are available in Figure . We chose to use MAE for molecular analysis because it is less affected by outliers compared to RMSE and is, therefore, more appropriate for following global trends. When we consider the calculated MAE of each molecule separated out by method category the prediction accuracy of each molecule varies based on method category (Figure ). The MAE calculated for each molecule as an average of all methods shows that SM25 was the most poorly predicted molecule. The QM+LEC method category appears to be less accurate for the majority of the molecules compared to the other method categories. Compared to the other two method cat- Some SEM values are too small to be seen under the data points. Method DFT_M05-2X_SMD was selected as the method with the median RMSE of all ranked methods analyzed in the challenge. Performance statistics of these methods is available in Table . egories, QSPR/ML methods performed better for molecules SM41-SM43, which are isoxazoles (oxygen and nitrogen containing heteroaromatics), and molecule SM44-SM46, which are 1,2,3-triazoles (nitrogen containing heteroaromatics). Physical QM methods performed poorly for molecules SM25 and SM26 (acylsulfonamide compound class). Figure shows error distribution for each challenge molecule over all the prediction methods. Molecule SM25 has the most spread in pK a prediction error.