Simple descriptors of cavities in porous cages such as the void and window diameters can be computed with pywindow. In this work, our goal is to map porous organic cage molecules to a lower-dimensional latent space * on the basis of their intrinsic porosity. i.e., we aim to develop an informationrich, low-dimensional vector representation-a fingerprint-of each porous organic cage that encodes the salient features of the size and shape of its cavity and windows. Such a fingerprint is useful for a few reasons. First, the latent representation would serve as a predictor of adsorption in a regression or classification model as in Refs.; see Le et al. for a review on quantitative structure-property modeling. Second, the latent representation lends a notion of similarity between two cages. Suppose a highly shape-selective porous cage molecule is composed of expensive or toxic precursors or suffers from instability. Within the latent cage space, we can then identify its nearest neighbors for alternative, existing cage molecules possessing the most similar cavity shapes, but composed of cheaper/safer precursors and showing higher stability. Finally, embedding porous cage molecules into a low-dimensional latent space and analyzing the clusters can shed light on the diversity of cavity shapes among the cages that have been synthesized.