pKalculator: A pK_a predictor for C–H bonds

• Rasmus M. Borup,
• Nicolai Ree and
• Jan H. Jensen

Beilstein J. Org. Chem. 2024, 20, 1614–1622, doi:10.3762/bjoc.20.144

• . 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 pKa 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 pKa 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 pKa values with a mean absolute error (MAE) and a root mean squared error (RMSE) of 1.24 and 2.15 pKa units, respectively

• . Furthermore, we employ our model on 1043 pKa-dependent reactions (aldol, Claisen, and Michael) and successfully indicate the reaction sites with a Matthew’s correlation coefficient (MCC) of 0.82. Keywords: C–H pKa values; pKa predictor; Introduction Over the years, the ability to selectively break a C–H