Initially, in drug discovery, chemical space concept proved useful to understand and generate knowledge of the pharmacokinetic properties and molecular diversity of biologically relevant compounds . As the number of chemical compounds and their information in databases increased, more addressing the dimensionality problem . Because it is complicated to visualize multidimensional spaces, coordinate-based approaches usually rely on dimensionality reduction techniques to transform high-dimensional data into two or three dimensions. Over the past two decades, several research groups have implemented different dimensionality reduction techniques to analyze chemical space. Such advances were extensively reviewed in a previous study . The most common techniques include principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) , and selforganizing map (SOM) . Previous studies have discussed the exploration of SPR in the context of chemical spaces .