Chemical space has been defined as an M-dimensional cartesian space, and each dimension represents the descriptors or features encoding a molecule. The length of descriptor sets defines the number of dimensions of each chemical space. Chemical multiverse is a natural extension of the concept of chemical space and it has been conceptualized as a group of alternative chemical spaces of a set of compounds, each defined by a distinct set of molecular descriptors. To generate a visual representation of the chemical space we used two dimensionality reduction techniques, namely, t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA) based on the similarity matrices computed with the Tanimoto coefficient and MACCS keys, Morgan2, and Morgan3 fingerprints. t-SNE and PCA were done using a Python script previously developed by our research group. 31