We then compare our ML model with previously reported ML models for predicting pK a values; the GCNN C-H pK a predictor by Roszak et al. , and the XGBoost pK a predictor by Yang et al. . Roszak et al. use a mix of experimental data (414 compounds), 7 manually curated DFT data (212 compounds), and previous reported DFT data (194 C-H sites) and yields an MAE of 2.18 pK a units for their test set. Yang et al. use filtered entries from the iBonD dataset, comprising 15338 compounds and 19397 pK a values across 39 solvents. As they not only predict C-H pK a values, we cannot compare our result with their best ML model. However, they also report a holistic 6-solvent (HM-6S) XGBoost model in DMSO (9.3 % of the data) that most likely contains the majority of C-H pK a values. For this XGBoost model, they achieve an MAE and RMSE of 1.53 and 2.35 pK a units, respectively. The comparison between our ML model, Roszak et al. . Now we train a LightGBM classifier and a LightGMB regressor for the entire dataset (775 compounds; 3910 pKa values) of QM computed pK a values to assess the generalization capability of our ML models. We use an out-of-sample dataset of 1043 pK a -dependent reactions from Reaxys, containing 584 Aldol, 408 Claisen, and 51 Michael reactions. These reactions are chosen because they all involve a deprotonation step, and the C-H site with the lowest pK a value is most likely the site of the reaction. We also use these reactions to compare with Roszak et al. as they evaluate their GCNN model against 12873 pK acontrolled reactions, including Aldol, Claisen and Michael reactions where they correctly predict the reacting site with an accuracy of 90.5 %. Our out-of-sample set is also used to see how well our ML models predict the site of reaction using the lowest ML predicted pK a value.