Automating the several stages of the docking process all in a single pipeline could significantly reduce the need for expert involvement, which would enhance the accessibility of docking as a technology. An ideal pipeline would simplify the preparation of the docking model for those with less experience while still allowing experts the option to adjust the model as needed. Moreover, beyond merely create a docking model, an optimal pipeline would also optimize the model's parameters to ensure that its performance is at least comparable to that of a model produced by an expert provided the same initial data. For this to be possible, the pipeline must first be capable of evaluating the quality of candidate models. Typically, this evaluation is performed using retrospective docking . This method involves assessing a model's ability to (1) accurately predict the binding characteristics of known ligand structures, such as pose, and (2) consistently assign them more favorable docking scores compared to designated decoy molecules. These decoy molecules may be property-matched to the known ligands or selected by other methods .