ArticleNature methods2024
Accurate RNA 3D structure prediction using a language model-based deep learning approach.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.
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Who cites it
110 citing papers in PubMed.
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- Crosstalk between RNA secondary and three-dimensional structure prediction: a comprehensive study.RNA biology · 2026Article
- Hierarchical breakdown of RNA structure prediction in CASP16: from reliable local helices to speculative multimer assembly.Bioinformatics (Oxford, England) · 2026Article
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- In silico designing of small molecules for targeting RNA: current landscape and future directions.Journal of computer-aided molecular design · 2026Review
- The trRosettaRNA server for RNA structure prediction.Nature protocols · 2026Review
- Accurate RNA-Ligand Binding Site Prediction Based on a Multi-Channel Graph Neural Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Molecular Engineering of Aptamers for Glioblastoma Therapy: From Simple Antagonists to AI-Driven Approaches, a Narrative Review.International journal of molecular sciences · 2026Review
- PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants.Cell genomics · 2026Article
- Mammalian therapeutic riboswitches: Engineering and custom ligands.Molecular therapy. Nucleic acids · 2026Review
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
- A large-scale cryo-EM RNA motif dataset and benchmark for machine learning-based structure modeling.Computational biology and chemistry · 2026Article
- Limits of deep-learning-based RNA prediction methods.Nucleic acids research · 2026Article
- Interpretable distillation reveals that deep learning splicing models suffer from pervasive confounders and blind spots.Genome biology · 2026Article
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- Review
- PlanarFold: a coarse-grained molecular dynamics model of RNA in two-dimensional space.Nature communications · 2026Article
50 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors.
Funding
Abstract
Accurate prediction of RNA three-dimensional (3D) structures 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 the 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 pretrained 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 the superiority of RhoFold+ 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 interhelical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.