SBDD has reached notable maturity over the past decades, especially structure-based virtual screening, despite its intrinsic limitations. In recent years, DL has been used in attempts to improve the performance of SBDD methods further. Perhaps the most well-known example of this is the usage of DL for protein structure prediction. De novo structure prediction with Alphafold and RoseTTAfold or other programs has yielded many protein models of near-experimental accuracy which has further expanded the opportunities and applicability domain of homology modeling. Other uses of AI in SBDD include but are not limited to potentials similar to quantum-chemical descriptions (ANAKIN-ME) force field development; Boltzmann generators trained to identify transition states f; protein-ligand interaction fingerprints such as SPLIF or extended connectivity interaction features (ECIF), and scoring functions like GNINA. Recently, the geometric DL approach was used to learn distance distribution and ligand-target interactions or predict the binding conformation of bioactive compounds. This potential performs similarly or better than well-established scoring functions. Geometry DL uses a mesh on the protein surface as a molecular representation.