Rings can have a significant effect on solvation energies, making this a demanding extrapolation task. The results demonstrate that incorporating QM descriptors leads to improved performance for both random and scaffold splits (Figure ). Notably, the addition of QM descriptors results in a substantially greater performance gain in the more challenging scaffold split experiment, suggesting that QM descriptors can significantly enhance the extrapolative capability and generalizability of D-MPNN models for molecular property predictions. In summary, our findings demonstrate that incorporating QM descriptors into D-MPNN models can substantially improve their performance for molecular property prediction, particularly when dealing with small datasets. However, it is crucial to consider the trade-off between the potential benefits and the computational cost and uncertainty associated with obtaining these descriptors. For small datasets with easily computable QM descriptors, we recommend directly calculating the descriptors using quantum mechanics for each inquiry molecule during inference. This approach minimizes the likelihood of model underperformance due to errors in descriptor values introduced by on-the-fly prediction. In contrast, when dealing with datasets for which QM descriptors are difficult to compute or estimate accurately, caution should be exercised when applying them within GNN models. In such cases, thorough pilot benchmark studies are advised to better understand the correlation and uncertainty of selected QM descriptors on target prediction model performance before embarking on costly QM calculations for a large number of molecules or reactions. Moreover, descriptor selection can have a decisive impact on model performance, with the optimal subset often varying depending on the specific task, underscoring the importance of a wellplanned selection strategy. Judiciously choosing the most informative descriptors not only improves model accuracy but also enhances interpretability by potentially revealing underlying physicochemical principles governing descriptor-target relationships while reducing computational costs by avoiding unnecessary calculations. Adopting this targeted approach can facilitate the development of more accurate, robust, and interpretable models for molecular property prediction. Our analysis of model generalizability using scaffold splitting highlights the potential of QM descriptors to enhance the extrapolative capability of D-MPNN models.