Even though the number of entries in the training set for DBX2 was lower than other models, it exhibited significantly improved performances in predicting binding affinity against hold-out set with respect to GNN-MPNN pure interaction (I) models from Volkov et al and Pafnucy model , as evident from the Rp and RMSE values, and comparable performances with GNN models that include protein and ligand structural information explicitly. Importantly, DBX2 is entirely based on energetic ensemble representations that do not consider any structural information about ligand and/or protein structures, differently than the models from and . This observation suggests that DBX2 could (at least partially) overcome the hidden biases causing memorization of 2D molecular patterns that these models display, as described in the study by Volkov et al , while significantly outperforming the success rate of generalizable pure interaction models.