(2) quantitative methods to measure the similarity between these representations. Structural representation can be further classified into 2D representation (2D molecular fingerprints, chemical graphs, etc.) and 3D representation (shape/pharmacophore Gaussian sphere models, molecular fields, etc.). Regardless of representation, similarity can be assessed using the Tanimoto coefficient, Tversky coefficient, Euclidean distance, Hamming distance, Dice index, and cosine coefficient, with the Tanimoto coefficient being the most used. 2D similarity methods, despite their efficiency and the advantage of not requiring structural alignment, can underestimate similarity and overlook key structural and functional features. Attempting to overcome these limitations, 3D shape-based similarity analysis has become a method of choice in many virtual screening campaigns. Molecular shape methods maximize the shape overlap between two molecules, in which molecular shape or volume are typically represented by the Gaussian sphere model. Several tools have adopted Gaussian overlays to measure shape similarity, including ROCS, Phase Shape, Shapeit, SHAFTS, WEGA, ShaEP 23 , and shapescreen . Despite the plethora of available tools, access to user-friendly, benchmarked, and supported open-source software to measure both shape and pharmacophore overlap has been limited.