Geometric Deep Learning on Molecular Representations

Molecular surfaces are defined by the surface enclosing the 3D structure of a molecule at a certain distance from each atom center. Each point on such a continuous surface is characterized by its chemical (e.g., hydrophobicity, electrostatics) and geometric features (e.g., shape, curvature). From a geometrical perspective, molecular surfaces are considered as 3D meshes, i.e., a set of polygons called faces described in terms of a set of vertices that describe how the mesh coordinates exist in the 3D space [ahmed2018survey]. The vertices can be represented by a 2D grid structure (where four vertices on the mesh define a pixel) or by a 3D graph structure. The grid- and graph-based structures of meshes enable applications of 2D CNNs and GNNs to learn on mesh-based molecular surfaces.