In this study, we used three well-known dimensionality reduction methods: t-SNE, PCA, and TMAPs although additional visualization methods can be used. Briefly, t-SNE generates plots that organize compounds. Similar compounds form clusters, and dissimilar compounds are distant from each other. PCA is a linear dimensionality reduction technique to transform data with many dimensions (i.e., descriptors) into a lower dimensional space and keep the different relationships between the data points as much as possible . PCA was generated from six whole molecule descriptors (MW, HB, HBA, SlogP, TPSA, and RB). TMAPs allow visualizing many chemical compounds through the distance between clusters and the detailed structure of these through branches and sub-branches. Local sensitive hashing allows each compound to be grouped hierarchically according to common substructures using molecular fingerprints. In this work, we use MACCS keys (166-bits) fingerprint.