To establish a baseline for further comparisons, we conducted a control experiment to investigate whether including the actual target value as a descriptor improves the model's performance in predicting the target itself. Considering that the HOMO/LUMO gap serves both as a molecular descriptor and a target in the QM9 dataset, we posed the following question: Does the inclusion of the true gap value in the model decrease the error in predicting the gap itself and its associated properties? In our first experiment, we introduced the true HOMO/LUMO gap value as an additional descriptor for the model trained on the full training set of the QM9 dataset. The results showed a considerable reduction in the prediction error of the gap itself (Table ), confirming that including correlated (i.e., in this case, perfectly correlated) QM descriptors can enhance model accuracy. In the second experiment, we used the true HOMO/LUMO gap value to train a model for predicting other targets in the QM9 dataset, which include multiple energetic properties (e.g., enthalpy, Gibbs free energy, etc.) closely related to the HOMO/LUMO gap. The results confirmed that the QM-GNN approach improves accuracy in predicting related properties, further supporting the observation that augmenting D-MPNN with target-correlated QM descriptors can enhance model performance (Table ).