The PIGNet and 3DGNN models top the performance table with almost identical performance (r 0.79). The 2DCNN model shows the biggest improvement with the dense LO data with a Pearson r increasing from 0.5 to 0.72. This dependence can be attributed to the fact that the 2DCNN model parameters and hyperparameters were optimized using LO datasets, as such the parameters may not be ideal for PDBbind core data alone. To our surprise the performance on this test set (Table ) was better than seen in the internal validation discussed previously (Table ).