By incorporating QM features, the models can potentially better capture the underlying physical principles governing molecular properties, enabling them to make more accurate predictions on molecules with distinct structural scaffolds that are not present in the training set. This improved extrapolative performance underscores the value of QM descriptors in enhancing the generalizability and transferability of D-MPNN models across diverse chemical spaces. Overall, the judicious use of QM descriptors in D-MPNN models can lead to significant improvements in molecular property prediction, especially for small datasets and when extrapolating to novel chemical structures. However, careful consideration of computational cost, uncertainty associated with obtaining these descriptors, and the strategic selection of relevant descriptors is necessary to ensure optimal model performance, efficiency, and interpretability.