One thing we have been missing in our own research on catalyst optimization for TMCs is a synthetic accessibility (SA) score that works for TMCs . So far we have applied a synthetic accessibility score designed for drug-like molecules for the isolated ligands which steers the proposed ligands to look like drug-like molecules . A similar score for TMCs would clearly be desirable for generative models proposing new TMCs. This SMILES-based dataset of the CSD serves as a good starting point of generating such a SA score. Kerstjens et al. recently proposed a method for correcting molecules from generative models based on a "familiarity" concept that takes into account which atom/bond types as well as fragments are present in a reference library . While they applied it to a reference library consisting of drug-like molecules we are currently applying it to our CSD SMILES dataset since the method requires molecules to be parsable by RDKit to get a score for whether a new TMC is "familiar" to the CSD.