The selection of the neighbor distance, 𝜖, must be tailored to the individual dataset. A good choice of the numeric value of 𝜖 sets the clustering resolution and finds the point of diminishing returns for further clustering based on the distribution of distances . We call the different natural breaks in the similarity matrix -which can serve to divide ligands into groups similarity substructures. Similarity substructures can become visually apparent in a heatmap of distances, such as that shown in Fig. . The visual representation of distance data in the heatmap (Fig. ) reveals diagonal block structures that could form groups of ligands. We would like to automatically cluster the groups of similar ligands indicated by these similarity substructures.