We implemented the architectures published by Moon et al. These architectures include a 3DCNN, GNN and the physics informed graph neural network PIGNet. We modified the code to allow additional data input (LO datasets). We also modify the training process by splitting the loss in two terms: public and LO losses. The losses were scaled to account for the difference in the dynamic range found in LO datasets in comparison to PDBbind. By normalizing the loss, we ensure that the different sources of data are equally weighted during the backpropagation process independently of their affinity range. These models were trained with the PDBbind dataset as well as augmented data, including docked decoys (292518), inactive complexes generated using IBS molecules (831885) and cross-docked inactive compounds generated from PDBbind (527682).