Through this work, we are proposing a knowledge-based design and evaluation of ML models for molecular property prediction. Rather than indiscriminately using any available descriptors, we believe that selecting descriptors (either atom or bond properties) based on well-established chemical principles to guide the training would yield more accurate and generalizable ML models. More specifically, we propose to use chemical principles taught in class as starting points for designing the model rather than letting the machine figure out the necessary features with little guidance, an approach closer to selfteaching than to teaching. As a proof-of-principle, we applied this strategy to the prediction of pKa values.