Notably, for the scaffold-split IP dataset, QM descriptors can offer advantage at a larger dataset size compared to random splits. Moreover, the ml-QM-GNN models tend to exhibit narrower error bars compared to their conventional GNN counterparts in scaffold-split QM8, QM9, and IP datasets, suggesting that the inclusion of additional descriptors can potentially improve the reliability of the predictions. However, for most other datasets, the performance trends closely mirrored those observed in random splits. This similarity may be attributed to the inherent uncertainty in predicted descriptors, which could limit the model's ability to improve on more challenging scaffold splits compared to random splits. We explore the impact of QM descriptor quality on model performance in greater detail in the following sections.