This study highlights the value of integrating quantum mechanical (QM) descriptors into directed message passing neural networks (D-MPNNs) to enhance the accuracy of molecular property predictions, particularly when training data is limited. By incorporating physicsbased information, this approach enables data-efficient, interpretable machine learning for chemistry. Our findings suggest that QM descriptors are most beneficial when: (1) the training set has fewer than 2000 data points, (2) the selected descriptors correlate well with the target properties, and (3) the descriptors can be accurately computed at a reasonable cost.