# Accurate RNA 3D structure prediction using a language model-based deep learning approach

arXiv: https://arxiv.org/abs/2207.01586 · HTML: https://mlsanity.com/paper/2207.01586/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 53 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RNA Secondary Structure Prediction By Learning Unrolled Algorithms](https://mlsanity.com/paper/2002.05810/) (2020)

## Built on by (most cited first, 19 of 19)

- [Path Planning for Masked Diffusion Model Sampling](https://mlsanity.com/paper/2502.03540/) (2025)
- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [Protein Language Models and Structure Prediction: Connection and Progression](https://mlsanity.com/paper/2211.16742/) (2022)
- [Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion](https://mlsanity.com/paper/2401.06151/) (2023)
- [RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design](https://mlsanity.com/paper/2301.10774/) (2023)
- [From Sentences to Sequences: Rethinking Languages in Biological System](https://mlsanity.com/paper/2507.00953/) (2025)
- [RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion](https://mlsanity.com/paper/2602.16548/) (2026)
- [Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification](https://mlsanity.com/paper/2502.07299/) (2025)
- [RiboGen: RNA Sequence and Structure Co-Generation with Equivariant MultiFlow](https://mlsanity.com/paper/2503.02058/) (2025)
- [Flash Invariant Point Attention](https://mlsanity.com/paper/2505.11580/) (2025)
- [Predicting Distance matrix with large language models](https://mlsanity.com/paper/2409.16333/) (2024)
- [Evidencing Unauthorized Training Data from AI Generated Content using Information Isotopes](https://mlsanity.com/paper/2503.20800/) (2025)
- [Prediction-Augmented Trees for Reliable Statistical Inference](https://mlsanity.com/paper/2510.16937/) (2025)
- [RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics](https://mlsanity.com/paper/2510.24736/) (2025)
- [Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances](https://mlsanity.com/paper/2511.03354/) (2025)
- [Deep Learning Framework for RNA Inverse Folding with Geometric Structure Potentials](https://mlsanity.com/paper/2601.00895/) (2025)
- [Scaling RNA Foundation Models through Iterative Refinement](https://mlsanity.com/paper/2610.13106/) (2026)
- [MORAD: Reinforcement Learning with Multi-Objective Rewards for RNA Inverse Design](https://mlsanity.com/paper/2610.13305/) (2026)
- [TANGO: RNA Topology and Geometry Co-Design](https://mlsanity.com/paper/2610.20277/) (2026)

## Abstract (arXiv)

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.