In drug design and development, visualization methods are key resources in data mining and information extraction from constantly increasing data sets. Indeed, visualization methods are important tools for rationalizing and interpreting experimental and calculated data . A key example is the chemical space concept, defined as "an M-dimensional cartesian space in which compounds are located by a set of M physicochemical and/or chemoinformatic descriptors" . Thus, chemical space allows the simultaneous study of different data types, such as structural, chemical, physicochemical, biological, clinical, and/or post-market data, to name a few examples. Since the chemical space depends directly on the descriptors used to define the M-dimensional cartesian space, it is important to mention that it is possible for the coexistence of parallel (or alternative) chemical spaces for the same set of molecules, namely, a multiverse chemical space. Also, it is possible to combine the alternative chemical spaces to create a single "consensus" chemical space . The chemical space application has demonstrated improvement in drug design, making decisions related to the prediction of structureproperties relationships (SPR), side effects, and polypharmacology drug activity, to mention a few of the most recent advances .