Specifically, we train models using subsets of descriptors based on whether they are atom, bond, or molecular features, and compare their performance (Figures and). The results suggest that models trained with the full set of descriptors generally perform comparably to models trained on the optimal subset of descriptors. This indicates that D-MPNNs are capable of identifying relevant features from all input descriptors, provided that the optimal subset is included within the full set. In some cases, a carefully chosen subset of descriptors can slightly outperform the full set, likely due to the reduction of extraneous noise from irrelevant descriptors.