The representation of a set of molecules in a two-or three-dimensional plot based on a set of pre-defined descriptors is usually known as visualization of the chemical space. In this work, the statistical analysis called t-distributed stochastic neighbor embedding (t-SNE) was done to represent chemical space occupied by datasets, with a perplexity equal to 10, a learning rate equal to 100, using to Jaccard metric and descriptor-based in fingerprint ECFP4. Also, constellation plots recently developed were built for the datasets; this plot integrates a methodology of analogs series with the visualization of the chemical space, associate the molecular analogs to several molecules and their biological activity. The rest of the molecules were found in scientific articles. More than 70 percent of molecules are shared with ChEMBL. LasR is TF with a higher number of molecules. Furthermore, the LasR dataset has the highest proportion of active molecules between all datasets.