Grzybowski and coworkers recently addressed this gap by predicting pK a values for C-H bonds in dimethylsulfoxide (DMSO) using a Graph Convolutional Neural Network (GCNN). Using a mix of experimental and computationally computed pK a data, they achieve a mean absolute error (MAE) of 2.1 pK a units. Lee and coworkers also address this problem by creating a general machine learning (ML) model using either a neural network or XGBoost. They train on experimental pK a values in 39 solvents from the Internet Bond-energy Databank (iBonD). Hereafter, they can predict the lowest pK a value for a wide range of molecules that contain bonds such as N-H, O-H, C-H, S-H, P-H. However, during their analysis, they report a scarcity of non-aqueous pK a values and achieve an MAE of 1.5 pK a units for the solvent DMSO using XGBoost. Unfortunately, neither the Grzybowski group nor the Lee group has made their models generally available to other users.