The assignment of a numeric value to the distortion of each molecule allows for structural chemistry databases to be interrogated as a function of shape, where similarly shaped molecules will form clusters in the 3N-6dimensional principal component space. While this cluster space would be available without a preceding symmetrydelineation, this method allows precise information to be determined with the minimum of input coordinate sets, with the chemically reasonable assumption that orthogonal representations have minimal influence on one another. PCA, cluster identification and other methods to develop quantitative three-dimensional quantitative structure-activity relationships (3D-QSAR) are included in the library; analysis can be performed through a python notebook interface, such as Jupyter. Decomposition into symmetric mode vector groups followed by analysis of principal components of these groups is found to be a good method of parameter reduction in structural chemistry, potentially very useful for rational constraint of crystallographic or computational molecular refinement. PCA is discussed further below.