{"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/torchproteinlibrary-a-computationally","title":"TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure","arxiv_id":"1812.01108","date":"2018-11-23","proceeding":null,"authors":["Derevyanko Georgy","Lamoureux Guillaume"],"abstract":"Predicting the structure of a protein from its sequence is a cornerstone task\nof molecular biology. Established methods in the field, such as homology\nmodeling and fragment assembly, appeared to have reached their limit. However,\nthis year saw the emergence of promising new approaches: end-to-end protein\nstructure and dynamics models, as well as reinforcement learning applied to\nprotein folding. For these approaches to be investigated on a larger scale, an\nefficient implementation of their key computational primitives is required. In\nthis paper we present a library of differentiable mappings from two standard\ndihedral-angle representations of protein structure (full-atom representation\n\"$\\phi,\\psi,\\omega,\\chi$\" and backbone-only representation\n\"$\\phi,\\psi,\\omega$\") to atomic Cartesian coordinates. The source code and\ndocumentation can be found at https://github.com/lupoglaz/TorchProteinLibrary.","url_abs":"http://arxiv.org/abs/1812.01108v1","url_pdf":"http://arxiv.org/pdf/1812.01108v1.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":"torchproteinlibrary-a-computationally","repo_url":"https://github.com/lupoglaz/TorchProteinLibrary","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"protein-folding","task_name":"Protein Folding"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}