Regardless of the types of ligand poses used for training and testing, the LB model consistently outperformed the SB model under cross-validation. Even better performance was obtained using the HB model when trained on the docked pose ranked highest by Smina, and using this to predict the affinities across all poses and taking the best binding affinity; indeed, this performance (2.2; ρ P = 0.744) was comparable to our SB model when trained and tested on crystal poses (1.4; ρ P = 0.746), suggesting that augmenting structure-based features with ligand-based features in a machine learning-based scoring function can help to correct for errors that might be introduced by the use of docked poses. for each ligand, using the same binding affinity value for each pose of a ligand. We used our ML models to score all poses of the ligands in the test fold and took the maximum predicted binding affinity as the predicted value for that ligand. To control for the effect of substantially increasing the size of the training set, we repeated this cross-validation, but for each training ligand used the same number of copies of the pose ranked top by Smina as the number of diverse docked poses output for that ligand by Smina (up to 20). Table shows the mean Pearson correlation coefficient achieved by each model over the five crossvalidation folds, when trained on multiple diverse or redundant poses. Overall, there is little difference between training on a single pose and training on multiple poses or redundant poses, with the SB model performing slightly better when trained on all poses instead of one pose (ρ P = 0.699 vs ρ P = 0.676), and the HB model performing slightly worse (ρ P = 0.723 vs.