{"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/generative-models-for-graph-based-protein","title":"Generative Models for Graph-Based Protein Design","arxiv_id":null,"date":"2019-12-01","proceeding":"ICLR Workshop DeepGenStruct 2019","authors":["John Ingraham","Vikas Garg","Regina Barzilay","Tommi Jaakkola"],"abstract":"Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, with the task of finding a viable design often referred to as the inverse protein folding problem. We develop relational language models for protein sequences that directly condition on a graph specification of the  target structure. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. Our framework significantly improves in both speed and robustness over conventional and deep-learning-based methods for structure-based protein sequence design, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.","url_abs":"http://papers.nips.cc/paper/9711-generative-models-for-graph-based-protein-design","url_pdf":"http://papers.nips.cc/paper/9711-generative-models-for-graph-based-protein-design.pdf","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":"generative-models-for-graph-based-protein","repo_url":"https://github.com/jingraham/neurips19-graph-protein-design","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"protein-design","task_name":"Protein Design"},{"task_slug":"protein-folding","task_name":"Protein Folding"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[{"slug":"protein-structures-ingraham","name":"Protein structures Ingraham","full_name":"Dataset of protein backbones and sequences"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}