When developing Dedenser, we utilized Python 3 and various packages/libraries to enable the functionalities surrounding the Dedenser algorithm and user centric utilities. Regarding the implementation of the Dedenser algorithm various libraries were used as follows: scikit-learn for clustering with HDBSCAN and Gaussian Mixture Models, SciPy for convex hull calculations, Alphashape 65 for concave hull calculations, and Point Cloud Utils for Poisson-disk downsampling. Utility functions from the following libraries were used: pandas 66 for data handling reading/writing of files, openpyxl for extending pandas read/write features to Excel, scikit-learn for standard scalar calculations, Matplotlib for visualization, RDKit and Mordred for chemical descriptor calculations, and UMAP for embedding descriptor into chemical point clouds. Additionally, NumPy was used for various data manipulations and reading/writing operations, with all further dependencies captured in the requirements.txt and ddenser.yml files hosted on GitHub.