ChemGPS-NP was one of the first chemographic models used to comprehensively describe chemical space of natural products (NPs) using physicochemical properties and has proven to be useful in various applications . ChemGPS-NP is a PCA-based model of physicochemical properties, defined by a training set of carefully selected compounds that act as "satellites" or reference structures with extreme properties. ChemGPS-NP projects or "positions" new molecules into the chemical space by comparing their physicochemical properties with those of the reference structures. Although PCA-based mapping is fast and easy to compute, it omits nonlinear interactions and some map regions are overloaded with data. Some non-linear algorithms that have been implemented for chemical space visualization are t-SNE , and more recently, uniform manifold approximation and projection (UMAP) . These types of algorithms effectively visualize clusters or groups of data points and their relative proximities. Another frequently used method is SOM , a grid-based method that has been used to support lead discovery efforts and target prediction. Examples of the latter include SOM-based prediction of drug equivalence relationships and target inference generator .