The performance of each model is tested on held-out test sets for both random and scaffold splits. As shown in Figures and, the models generally show a good correlation between predicted values and DFT values for most descriptors, suggesting that our D-MPNN models can predict QM descriptors well. For some properties, such as Parr functions and bonding electrons, significant errors can be observed within certain data regimes. These errors can be attributed to the underlying data distribution of these descriptors. It is probable that the model predictions tend to gravitate towards the values around which the majority of the data points are concentrated, as it seeks to optimize its performance based on the prevalent patterns in the data. Table summarizes the model performance on each QM descriptor prediction for random and scaffold splits. The testing mean absolute error (MAE) and root mean square error (RMSE) for scaffold splits are also close to random splits, suggesting that the presented models can generalize beyond their training set and be used to predict QM descriptors for molecules with backbones that have not been seen during model training.