Protein Structure Ranking with Atom-level Geometric Representation Learning

The recent technical advance in geometric deep learning, especially the successful application of GNNs to model graph structures, makes the study of non-Euclidean data has seen sharply growing popularity over the last few years. Proteins, as the building blocks for all living organisms, play an important role in different domains. The unique 3D structure of a protein determines its function. Although several works achieved a promising breakthrough in the modeling of protein 3D structures, it is not guaranteed to be the same as the native structure of proteins. Thus the assessment of a protein structure's quality is crucial to estimating the given protein structure. This work takes advantage of the atom-level structural representations of the protein 3D graphs and presents a generic equivariant message passing based graph neural network. We demonstrate the robustness of the proposed framework through extensive experiments and prove the potential of geometric graph representation learning for future works.