from their true values could severely impair the model performance (Table ). Furthermore, results from models trained on downsampled sets indicate that, compared to using exact values of QM descriptors, increased uncertainties in QM descriptors cause the benefit of using additional descriptors to diminish more rapidly as the number of training data points increases (Figure ). Consequently, errors in on-the-fly QM descriptor prediction can hinder the performance of ml-QM-GNN models, especially for tasks with small training sets. This observation could explain why the ml-QM-GNN models underperformed for the ESOL and FreeSolv datasets (Figure ). To further investigate, we computed QM descriptors for these two datasets using the ωB97XD/def2-SVP model chemistry and the same procedure as discussed in the Methods section. Subsequently, we assessed the performance of the corresponding D-MPNN models using these calculated QM descriptors. The results show that the QM-GNN model consistently outperforms ml-QM-GNN models in all random split downsampled scenarios and can substantially outperform the baseline GNN models when trained with fewer than 200 data points (Figure ). Additional results and analyses of the errors associated with predicted QM descriptors and the distribution of molecular motifs in the ESOL and FreeSolv datasets compared to the QM descriptors dataset can be found in Section S11. To further assess the generalizability of the QM-GNN model compared to the baseline GNN model, we evaluated test performance of models trained on both random split and scaffold split subsets containing 100 data points each sampled from ESOL and FreeSolv, respectively. The scaffold split provides a more rigorous assessment of a model's generalizability compared to the random split because it often results in a greater structural divergence between the training and test-set molecules. More specifically, in the scaffold split experiment, we trained the models using only molecules without any Bemis-Murcko scaffolds and then tested them on molecules containing scaffolds. This is a challenging test because molecules without scaffolds have no rings in their backbones, essentially requiring the models to learn from non-ring species to predict the properties of ring-containing species.