Regardless of the maximum level of protein or ligand similarity permitted between the training and test sets, both the SB and HB models perform worse when trained and tested on docked poses (dotted lines) than when trained and tested on crystallographic poses (solid lines; in Figure , the dotted lines are always below the corresponding solid lines for the SB and HB models). The LB model performs identically when using docked and crystallographic poses: the RDKit molecular descriptors used by the model are independent of the pose of the ligand, so the model is unaffected by the use of different poses. The LB model actually performs better than the SB model using docked poses when no data are excluded from the training set. The LB model's performance drops below that of the SB model when complexes with 100% protein sequence identity to those in the test set (Figure ), or with a ligand whose Tanimoto similarity was greater than 0.6 to those in the test set (Figure ), are excluded from the training set. Regardless of the level of similarity between the complexes in the training and test sets, the LB model always achieves a Pearson correlation coefficient greater than 0.55, comparable to the performance of many classical scoring functions, indicating that these ligand-based features capture useful information for affinity prediction.