<p>Machine learning (ML) is gaining momentum in chemistry for the prediction of various molecular properties. However, these models are often trained on relatively scarce, sometimes low-quality data, resulting in what we describe as memorization (rather than learning) and poorly generalizable models. Aiming to revisit the way ML is practiced in chemistry, our strategy involves imparting chemistry knowledge to ML algorithms. Teachers teach chemistry with different levels of complexity in high school and graduate studies. This is due to fundamental principles being a prerequisite to understanding more advanced concepts. We posit that teaching fundamental principles to machines to predict properties, analogous to the way we teach students, will provide more accurate models. Thus, we propose to start with fundamental principles (e.g., electronegativity and inductive effect, conjugation, aromaticity) taught to students to allow them to predict properties (e.g., pKa) and provide these principles to machines to guide them to predict more advanced, yet related, properties. Based on this teaching-based approach, we developed pKaLearn, a pKa predictor that outperforms other state-of-the-art predictors. The ML models presented herein leverage the chemists’ knowledge and qualitative principles to quantify and predict chemical properties with high performance.</p><p></p>