to outperform other architectures in tasks such as node classification, link prediction, and graph classification in both accuracy and speed. In the realm of biomolecules, 3DGNNs have demonstrated superior performance in predicting molecular properties compared to other architectures. Graph-based models are particularly effective due to their invariance or equivariance to complex orientation when properly defined, making them more efficient than other models such as 3DCNNs. Additionally, 3DGNNs require less computational power than 3DCNNs. PIGNet. The previously published PIGNet incorporates physics-based knowledge to improve generalization in binding affinity prediction. The model parameterizes equations describing ). However, some caution is needed because the averaged values were adversely affected by one or two very poor correlations. Supplementary table shows the correlation metrics for the disaggregated training test sets. Due to the strategies employed to avoid overfitting, such as the simplified descriptors and using decoys etc. (see Methods), modest performance in these test sets was expected. Interestingly, the 2DCNN showed a noticeable improvement in correlation and errors when using internal data and a superior performance versus the rest of the models. The 3DCNN and PIGNet showed no major differences when trained with or without the additional internal data, whereas the absolute error in prediction for the 3DGNN improved substantially when trained with additional internal data. Also noteworthy, the GLIDE score correlation is similar to the majority of the DL models. Overall, the initial results from the training set performance did not show substantial improvements versus docking, except for the 2DCNN trained using additional internal data. A key interest here was to investigate the performance on unseen external test sets and therefore the generalizability of the models, and whether they learn interactions and features that describe 3D protein ligand bioactivity. PDBbind Core Set Testing. Here we used the PDBbind core set as an external test of the performance of the different models. The PDBbind dataset is widely used as a standard benchmark for assessing the performance of ML and DL models in predicting binding affinity and evaluating docking tools and empirical scoring functions. However, as mentioned above, previous studies have identified potential biases in this dataset that can lead to overoptimistic performance.