To examine the effect of integrating QM descriptors into D-MPNNs for diverse molecular property prediction tasks, we first predict the QM descriptors (listed in Table ) for each compound in the 16 benchmark datasets (described in Table ). Subsequently, we employ the predicted descriptors as supplementary features in the D-MPNN models (as described in Methods) to predict the corresponding target properties. The following analysis focuses on three key aspects: (1) the influence of training dataset size and the correlation between descriptors and targets on model performance, (2) the importance of refined feature selection in improving model accuracy and interpretability, and (3) the effects of incorporating QM descriptors on model sensitivity and generalizability.