Because of the greater availability of data on ligand structure, AI methods are more effective, enabling the study of very large volumes of diverse data in LBDD studies. SBDD approaches, on the other hand, have not yet fully explored the utility of AI, although a significant amount of research is currently in progress. One reason for this is the availability of structural data needed in SBDD studies, which require data on the ligand and on its binding site. By comparison, structure, activity, and physicochemical data typically required in LBDD studies, is considerably more available. Because of the limitations of current computational methods, generation of fully reliable 3D conformational states or binding modes is not possible in all cases, although significant strides have been made in computational docking methods, some of which are now capable of docking more than a billion compounds to a given binding site . In addition, recent progress in AI-driven de novo protein structure prediction (see below) has provided an unprecedented wealth of putatively reliable structural templates, with coverage recently approaching the entire protein universe .