{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/graphqa-protein-model-quality-assessment","title":"GraphQA: Protein Model Quality Assessment using Graph Convolutional Network","arxiv_id":null,"date":"2019-09-25","proceeding":null,"authors":["Federico Baldassarre","David Menéndez Hurtado","Arne Elofsson","Hossein Azizpour"],"abstract":"Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure.\nExperimental identification of a protein's structure can be time-consuming, prohibitively expensive, and not always possible. \nAlternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results.\nGraphQA 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. \nIn 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.","url_abs":"https://openreview.net/forum?id=HyxgBerKwB","url_pdf":"https://openreview.net/pdf?id=HyxgBerKwB","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"graphqa-protein-model-quality-assessment","repo_url":"https://github.com/baldassarrefe/protein-quality-gn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"protein-folding","task_name":"Protein Folding"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}