The HB model, which combines structure-based and ligand-based features, consistently outperforms the SB and LB models when using docked poses (in Figure and 1B, the dotted yellow line is always above the dotted red line and blue line). Furthermore, the HB model trained and tested on docked poses has comparable performance to the SB model when trained and tested on crystal poses (in Figure , the dotted yellow line is above solid red line; in Figure , the dotted yellow line is nearly identical to the solid red line for a Tanimoto similarity threshold greater than 0.5). Furthermore, the HB model is less deleteriously affected by the use of docked poses in place of crystal poses than the SB model (see, in Figure , the dotted yellow line much closer to the solid yellow line than the dotted red line is to the solid red line). This suggests not only that combining ligandbased and structure-based features leads to more accurate binding affinity predictions, but that the addition of ligand-based features to a structure-based scoring function can help to compensate for the errors in affinity prediction that may result from the use of potentially inaccurate docked poses.