As there was considerable variation in the accuracy of the docked poses generated by Smina, we investigated how the quality of the docked pose, or poses, used to train and test our models affected binding affinity prediction. For this, we trained and tested each model using five-fold cross-validation of the PDBbind Training Set. In our first set of experiments, to determine the influence of the ligand's pose on model performance, we trained the models using a single minimised pose (obtained by minimising the crystallographic binding pose using Smina) of each ligand in the training folds and tested on all of the (up to) 20 docked poses of each ligand in the test fold. We performed three experiments investigating strategies for making a single prediction of binding affinity using multiple docked poses of the test ligand. These involved using our models to predict the binding affinity for: (1.1) the pose ranked first by Smina; Table : Pearson correlation coefficient between predicted and experimental binding affinity under five-fold cross-validation on the PDBbind Training Set. Random Forest models were trained using ligand-based (LB) features, structure-based (SB) features derived from a single minimised pose, or using both LB and SB features (HB), and tested on up to 20 docked poses, or the crystallographic pose of each ligand. The Pearson correlation coefficient was computed between the experimental and predicted binding affinity values obtained in four experiments: (1.1) scoring the pose ranked best by Smina, (1.2) scoring all poses and taking the maximum score, (1.3) scoring all poses and taking the mean score, and (1.4) training and testing using the experimentally-determined binding pose. As expected, the LB model is insensitive to the docked ligand pose(s). The best performance for each scoring strategy is underlined. . In all cases, our models perform better when trained on docked poses rather than minimised crystallographic poses. This difference is marginal when taking the highest predicted affinity of the docked poses of the test ligands (2.2), but noticeable when using just the docked pose ranked best by Smina (2.1), or when taking the mean predicted of the poses (2.3). This suggests that training on docked poses helps our models to generalise to docked poses of varying accuracy, and that when presented with multiple docked poses, the highest score assigned by our models is likely the most accurate, reflective of the actual protein-ligand binding affinity.