Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure.
 Experimental identification of a protein's structure can be time-consuming, prohibitively expensive, and not always possible. 
 Alternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results.
 GraphQA is a graph-based method to estimate the quality of protein models, that possesses favorable properties such as representation learning, explicit modeling of both sequential and 3D structure, geometric invariance and computational efficiency. 
 In this work, we demonstrate significant improvements of the state-of-the-art for both hand-engineered and representation-learning approaches, as well as carefully evaluating the individual contributions of GraphQA.
GraphQA is a graph-based method for protein Quality Assessment that improves the state-of-the-art for both hand-engineered and representation-learning approaches