Machine learning methods to aid with assessment and de novo generation of molecules and chemical libraries in the drug development process are now commonplace. These machine learning methods allow researchers to generate libraries and rank compounds based on various chemical features of molecules ranging from distinct physical chemical properties, their predicted efficacy, toxicity, synthetic accessibility, to other targeted properties . Ensuring an appropriate degree of chemical diversity is a desired feature when working up chemical libraries, resulting in the development of a plethora of cheminformatic tools for the analysis and synthesis of chemical libraries and their diversity. The visualization of libraries can provide intuitive means of assessment and is a powerful addition to the various metrics developed by computational chemists for quantitative assessment and comparison of chemical libraries. By taking higher dimensional representations of molecules, often quantitative structure-activity relationship (QSAR) descriptors, and embedding them as 2D or 3D vectors, the chemical space of libraries can be visualized. Once embedded and visualized, researchers are able to interactively analyze their chemical libraries and perform tasks such as clustering and selecting representative molecules. Numerous approaches to clustering have been applied to chemical libraries, exploring various ways of selecting compounds from clusters. Considering nearest neighbors to cluster centroids has been a common way of producing representative sets of compounds. In the field of computer graphics, 3D collections of points are referred to as point clouds such that chemical clusters will be referred to as point clouds throughout this discussion. While various applications surrounding point clouds exist, they are primarily used for 3D modeling and visualization of real world objects and spaces. By processing and analyzing point cloud data, it is possible to recreate the geometry and appearance of complex objects with high fidelity. Computer scientists have developed and adapted various algorithms for tasks such as data merging, 36 segmentation, filtering, and visualizations to accomplish many tasks surrounding the application of point clouds.