Our approach to computational chemistry methods is based on the encoding of well-established chemistry principles. For example, the Hoffman postulate was implemented into ACE to predict transition state structures, while the impact of hyperconjugation on torsional energy was quantified into HTEQ. Similarly, we wondered if we could act as teachers to improve ML models and apply this methodology to pKa prediction as a proof-of-concept.