Mapping molecular databases in a chemical space where distances represent similarities between molecules helps to understand their structural similarities and identify relationships that can provide critical insights for drug development and related fields. However, molecular similarity can be computed in multiple ways, typically using various molecular fingerprints, resulting in a confusing multiplicity of possible chemical space representations. To overcome this limitation and create a chemical space map considering various similarity measures simultaneously, we report a new approach of applying reaction informatics tools to map and analyze drug pairs, namely the differential reaction fingerprint (DRFP) and the Transformerbased RXNMapper model, respectively (Figure ). These tools were initially designed to analyze chemical reactions. However, they can also be applied to processes that do not obey Lavoisier's principle, the conservation of mass, such as the alchemical transmutation of lead into gold. Here, we apply them to transmutations between pairs of molecules selected for their similarity according to various molecular fingerprints as similarity measures, an approach related to the recent development of transformer models for drug optimization. We start by using DRFP, which encodes chemical reactions by storing the symmetric difference of two sets containing the circular molecular n-grams generated from the molecules of the molecular pair as a binary fingerprint, to represent the chemical space of drug pairs as a TMAP (tree-map). A TMAP lays out the minimum spanning tree of the nearest neighbor graphs according to a selected similarity measure, here DRFP, and represents a remarkably efficient dimensionality reduction method for high-dimensional datasets. The DRFP TMAP visualization provides a global similarity perspective across drug pairs combining the selected similarity measures. We then use RXNMapper, a model trained on one million reactions documented in the USPTO dataset to pair corresponding atoms between reactants and products in a chemical reaction, to identify the structural relationship between drugs. The confidence score of this transformer appears not to correlate with any of the molecular similarity measures used. It allows us to distinguish drug pairs related by feasible chemical processes, such as matched molecular pairs corresponding to substituent exchanges, from those related by more esoteric, alchemical transmutations including scaffold-hopping changes. We demonstrate this approach with the example of FDA-approved drugs as a diversity set, as well as for a series of EGFR inhibitors and polymyxin B analogs as two high similarity sets chosen among small molecule drugs and peptide macrocyclic drugs, respectively.