As acidity/basicity of molecules is influenced by resonance, inductive and other effects, we selected descriptors directly related to these properties. The selection and curation of these descriptors were thoroughly tested, resulting in the simplest yet complete set of chemical descriptors needed for our training. Gratifyingly, this approach led to a model that is more precise than a fingerprint-based model with minimal physical meaning, a GAT-based model but only using categorical variables and other reported models. While this work was primarily aimed at testing this concept of teaching machines, we were happy to see that this approach produced one of the most predictive pKa prediction models on a regular benchmark set.