Choosing an algorithm (equivalent to learning method/styles in classes) is the next step. The concept of graph neural network (GNN) coincides with the basic components of molecules made of atoms (nodes) connected by chemical bonds (edges), making GNNs popular candidates to predict properties from graphs representing molecules. GNN's have been used in recent reports to predict pKa's. For this work, we opted for a GNN with multi-head attention mechanism (GAT). With this architecture, the spatial relationship between atoms is accounted for, thus allowing considerations of inductive and resonance effects. In other words, this GAT model can be "taught" the principles of inductive and resonance effects (Section S-III).