The results indicate that the ml-QM-GNN models generally match the accuracy of the standard GNN models in most datasets (Table ). However, one of our main focuses is to determine whether the benefits of introducing QM descriptors are more significant in smaller datasets, a scenario commonly encountered in experimental and de novo molecular Leveraging QM descriptors in D-MPNNs proves advantageous not only for QM datasets, where a significant benefit is not surprising because both the target properties and input descriptors are derived from similar computational chemistry calculations, but also for predicting experimental physical chemistry properties (Figure ). For instance, augmenting the D-MPNN model with QM descriptors substantially enhances its performance in predicting ionization potential (IP) when the training set contains fewer than 500 samples. However, this marked improvement in predicting experimental IP is partially anticipated, as calculated IP values are included as additional molecular features in the model. Similarly, the QMaugmented model demonstrates improved performance in predicting lipophilicity when the training set comprises fewer than 300 samples. We also observe that the ml-QM-GNN model outperforms the standard GNN in the small-data regime for predicting critical properties of fluids. These findings indicate that QM descriptors can potentially assist D-MPNN in capturing relevant information that improves its predictive performance for experimentally measured properties, beyond those derived from QM calculations, particularly when data is scarce. In the context of biophysics (Figure ) and physiology (Figure ), QM-augumented models exhibit a modest reduction in prediction errors for various tasks when data is limited, except for the Tox21 dataset. The positive influence of QM descriptors on certain property prediction tasks in biophysics and physiology is particularly intriguing, considering the seemingly weak physical correlation or intuitive relationship between generic DFT descriptors and specific targets, such as inhibition of HIV replication. This observation suggests that while the improvements due to QM descriptors are not always substantial, there are indications that QM descriptors might assist D-MPNN models in capturing certain fundamental molecular characteristics that may potentially contribute to the prediction of more complex biological properties, even in cases where a strong direct physical relationship is not immediately apparent. However, further research is needed to confirm this hypothesis and understand the precise mechanisms involved.