While organic chemistry and solid-state chemistry can be pretty easily handled by machine learning methods , the investigation of molecular inorganic compounds by ML methods is very limited because of the insufficient string representation of coordination compounds and organometallics as Table illustrates for the example of ferrocene. Neither the SMILES versions nor the "old" InChI can handle the bonds in ferrocene appropriately and the connectivity is lost. Only the TUCAN representation makes an exception here with an identical line notation for the first two depictions. Currently, metal-ligand bonds are normally disconnected by the InChI algorithm because it was developed with a focus on organic compounds. In that approach, interactions with metal atoms by default were considered to be ionic in nature, and consequently all connectivity between organic fragments and metal centers were removed by the algorithm. While this is adequate for consistent treatment of organic molecules and classical ions, it creates serious problems for the treatment of many inorganic species, particularly coordination and organometallic compounds, where there is stereochemical information associated with the connections to the metal atom/ion, or where binding to a central atom introduces stereochemical elements to a ligand, for example by reducing the symmetry present in the absence of the metal atom. Such disconnections led to loss of all stereochemical information associated with the presence of ligands to a central atom. Furthermore, there are problems with the proper representation of haptic bonding in sandwich and half-sandwich compounds. Until now, most of the line notations only handle covalent bonds. 2) Conversion of Input to an Internal Chemical Object: