pKalculator: A pK_a predictor for C–H bonds

Determining the pK_a values of various C–H sites in organic molecules offers valuable insights for synthetic chemists in predicting reaction sites. As molecular complexity increases, this task becomes more challenging. This paper introduces pKalculator, a quantum chemistry (QM)-based workflow for automatic computations of C–H pK_a values, which is used to generate a training dataset for a machine learning (ML) model. The QM workflow is benchmarked against 695 experimentally determined C–H pK_a values in DMSO. The ML model is trained on a diverse dataset of 775 molecules with 3910 C–H sites. Our ML model predicts C–H pK_a values with a mean absolute error (MAE) and a root mean squared error (RMSE) of 1.24 and 2.15 pK_a units, respectively. Furthermore, we employ our model on 1043 pK_a-dependent reactions (aldol, Claisen, and Michael) and successfully indicate the reaction sites with a Matthew’s correlation coefficient (MCC) of 0.82.