The analysis above suggests that QM-GNN models tend to perform better when the descriptors correlate with the targets. This observation leads us to hypothesize that carefully selecting subsets of QM descriptors with high correlations to the targets could further improve model performance. Intuitively, using too many irrelevant features may introduce noise and distract the models, particularly in the small-data regime. Furthermore, revealing which descriptors are helpful for specific tasks can potentially provide some insights into the physicochemical principles underlying the descriptor-target relationships and subsequently facilitate a better interpretability and comprehension of model decisions. However, conducting a full ablation study on all descriptors and datasets would be computationally expensive and beyond the scope of this work. Therefore, we perform a preliminary assessment to investigate whether particular types of descriptor can explain most of the improvement.