In contrast to 2D GNNs, which solely encode the information stored in a graph representation of a molecule and pass messages between covalently bonded atoms, 3D GNNs generally encode point clouds representing the coordinates of a specific 3D conformation of the molecule. Under this paradigm, edges in the 3D graph no longer solely correspond to covalent bonds, but additionally include non-covalent edges between any two atoms separated by a distance within a defined cutoff. By explicitly encoding the local geometry of these 3D graphs (e.g., in terms of distances, angles, and dihedrals), 3D GNNs have the capacity to directly capture 3D geometric features present in the conformers provided as input to the model. We hypothesized that this enhanced geometric expressivity would improve the surrogate descriptor prediction models, particularly for geometry-sensitive descriptors such as Sterimol values and NBO partial charges.