{"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/e2efold-3d-end-to-end-deep-learning-method","title":"Accurate RNA 3D structure prediction using a language model-based deep learning approach","arxiv_id":"2207.01586","date":"2022-07-04","proceeding":null,"authors":["Tao Shen","Zhihang Hu","Siqi Sun","Di Liu","Felix Wong","Jiuming Wang","Jiayang Chen","YiXuan Wang","Liang Hong","Jin Xiao","Liangzhen Zheng","Tejas Krishnamoorthi","Irwin King","Sheng Wang","Peng Yin","James J. Collins","Yu Li"],"abstract":"Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to scarcity of experimentally determined data, complicates computational prediction efforts. Here, we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pre-trained on ~23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate RhoFold+'s superiority over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and inter-helical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies.","url_abs":"https://arxiv.org/abs/2207.01586v3","url_pdf":"https://arxiv.org/pdf/2207.01586v3.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":"e2efold-3d-end-to-end-deep-learning-method","repo_url":"https://github.com/ml4bio/rhofold","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"e2efold-3d-end-to-end-deep-learning-method","repo_url":"https://github.com/ml4bio/rna-fm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"e2efold-3d-end-to-end-deep-learning-method","repo_url":"https://github.com/rish-16/rna-backbone-design","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"rna-3d-structure-prediction","task_name":"RNA 3D STRUCTURE PREDICTION"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2207.01586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01586"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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