While the re-docking performance of a deep learning model is often reported to evaluate its performance, correctly predicting the binding mode of a new ligand, possibly to a new target, is more important in SBDD. The new target may even lack a crystal structure, so vHTS will need to rely on a closely related structural model or a homology model. We therefore trained and evaluated AtomNet PoseRanker on a cross-docked data set (Methods). Table reports the AUC for AtomNet PoseRanker compared to smina docking. Unsurprisingly, distinguishing favorable docking poses from unfavorable poses is more challenging in cross-docking compared to self-docking, reflected by reduced AUCs for AtomNet PoseRanker and smina docking. There are numerous challenges in cross-docking: 1) the binding site often has a different size and/or shape owing to different (equilibrium) backbone and sidechain positions; 2) amino acid insertions, deletions, or substitutions can alter the steric and electrostatics characteristics of the site; 3) changes in solvation characteristics; and 4) experimental conditions like crystal packing, etc. Accordingly, test AUC were reduced slightly compared to self-docking across all data splits (Table ).