Eigencages: Learning a Latent Space of Porous Cage Molecules

Porous organic cage molecules harbor nano-sized cavities that can selectively adsorb gas molecules, lending them applications in separations and sensing. The geometry of the cavity strongly influences adsorptive selectivity.

For comparing cages and predicting their adsorption properties, we embed/encode the cavities of a set of 74 porous organic cage molecules into a low-dimensional, latent "cage space".

We first scan the cavity of each cage to generate a 3D image of its porosity. Leveraging the singular value decomposition, in an unsupervised manner, we then learn across all cages an approximate, lower-dimensional subspace in which the 3D cage cavity images lay. The "eigencages" are the set of characteristic 3D cage cavity images that span this lower-dimensional subspace. A latent representation/encoding of each cage then follows from expressing it as a combination of the eigencages.

We show that the learned encoding captures salient features of the cavities of porous cages and is predictive of properties of the cages that arise from cavity shape.