Ligand replacement is an avenue available for engineering single-molecule magnets, but the variable-length Cartesian representation prevents the application of machine learning techniques useful in the discovery of promising molecules. The Atomic Environment Vector (AEV) allows the application of machine learning techniques by mapping a ligand’s Cartesian representation of a ligand onto a fixed-length vector. The AEV loses information about the spatial configuration of the ligand’s atoms and must be mapped back into the Cartesian representation in order to be useful in quantum chemistry codes. The reverse Monte Carlo (RMC) method was implemented to recover the Cartesian representation from an arbitrary AEV. A modified data type, the Conic Atomic Environment Vector (CAEV), was created to correct for the loss of information attributed to the standard AEV by utilizing the conic geometry of ligand’s being attached to magnetic core regions. Though the RMC method had difficulty in reproducing molecules under both AEV representations, the CAEV produced more meaningful outputs. The development of more accurate inverse modeling methods could unlock the potential utility of machine learning for ligand searches and ultimately progress the development of conventional and quantum computing applications of single-molecule magnets.