To learn and predict C-H pK a values, we train a LightGBM regression model with our generated dataset containing QM computed pK a values (775 compounds; 3910 pK a values). Hereafter, we correlate and compare the ML predicted pK a values and the QM computed pK a values and achieve an MAE and RMSE of 1.24 and 2.15 pK a units for the held-out test set (155 compounds; 789 pK a values), as illustrated in Figure . When zooming in on the ML predicted pK a values that are not correlating well with the QM computed pK a values, we find C-H sites that are either bridgeheads or where the negative charge is stabilized by resonance. This may be due to the nature of the chosen descriptor vector, based on sorted CM5 atomic charges, as it may not account, e.g., steric strain and charge delocalisation. We discuss this further in SI -Outliers for the test set section.