Prior to developing a model, we thought of establishing a baseline model. RF and XGB algorithms, which are broadly used to predict pKa were trained with our own dataset and a variety of molecular descriptors or fingerprints (2D molecular descriptors, Estate, EstateSum, MACCS, 2D and MACCS combined and Morgan fingerprints) was conducted (Figure ). The optimal models (2D and 2D MACCS) achieved an MAE on our testing set of 0.96 (RF) and 0.92 (XGB). Moreover, these models were trained only to predict the global macro-pKa of a molecule based on its encoding through various descriptors.