DiffDock-L is one of the most popular deep-learning models for docking. Not only has it shown competitive performance among ML-based docking programs regarding pose prediction accuracy, physicochemical plausibility, and protein-ligand interaction recovery rate, but it is also one of very few algorithms that support blind docking (i.e., docking without the necessity to define a ligand binding site).