Unlike point cloud centric/specific algorithms, clustering has been employed as a major component of cheminformatics and machine learning application surrounding chemistry, such as during library design, data driven synthesis, molecular dynamics simulations, and mass spectrometry . Dedenser utilizes HDBSCAN, a variant of DBSCAN, that employs hierarchical trees to define clusters within a given dataset. Briefly, the HDBSCAN algorithm is executed by: 1) calculating mutual reachability distances between data points based on their distances and local densities, 2) building of a minimum spanning tree from the mutual reachability distances, 3) converting the minimum spanning tree into a hierarchical tree, 4) condensing the hierarchical tree such that nodes are clusters of data points, and 5) extraction of clusters from the condensed tree.