ArticleJournal of molecular biology2024
RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction.
Article in Journal of molecular biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- gCoSRNA: Generalizable Coaxial Stacking Prediction for RNA Junctions Using Secondary Structure.Biomolecules · 2026Article
- Assessment of Nucleic Acid Structure Prediction in CASP16.Proteins · 2026Article
- ERNIE-RNA: an RNA language model with structure-enhanced representations.Nature communications · 2025Article
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- All-at-once RNA folding with 3D motif prediction framed by evolutionary information.Nature methods · 2025Article
- Modeling RNA duplex dynamics with Gibbs sampling enhances base-pair prediction accuracy and reveals structural activity profiles.NAR genomics and bioinformatics · 2025Article
- RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation.bioRxiv : the preprint server for biology · 2025Article
- ECSFinder: optimized prediction of evolutionarily conserved RNA secondary structures from genome sequences.Nucleic acids research · 2025Article
- Assessment of nucleic acid structure prediction in CASP16.bioRxiv : the preprint server for biology · 2025Article
- Has AlphaFold3 achieved success for RNA?Acta crystallographica. Section D, Structural biology · 2025Article
- Semi-supervised segmentation of RNA 3D structures using density-based clustering.Computational and structural biotechnology journal · 2025Article
- Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.Biology methods & protocols · 2025Article
- RNA Structure: Past, Future, and Gene Therapy Applications.International journal of molecular sciences · 2024Review
- Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.PLoS computational biology · 2024Article
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Abstract
With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser availability and lower structural diversity of the experimentally resolved RNA structures in comparison to protein structures. These challenges are often poorly addressed by the existing literature, many of which report inflated performance due to using training and testing sets with significant structural overlap. Further, the most recent Critical Assessment of Structure Prediction (CASP15) has shown that deep learning models for RNA structure are currently outperformed by traditional methods. In this paper we present RNA3DB, a dataset of structured RNAs, derived from the Protein Data Bank (PDB), that is designed for training and benchmarking deep learning models. The RNA3DB method arranges the RNA 3D chains into distinct groups (Components) that are non-redundant both with regard to sequence as well as structure, providing a robust way of dividing training, validation, and testing sets. Any split of these structurally-dissimilar Components are guaranteed to produce test and validations sets that are distinct by sequence and structure from those in the training set. We provide the RNA3DB dataset, a particular train/test split of the RNA3DB Components (in an approximate 70/30 ratio) that will be updated periodically. We also provide the RNA3DB methodology along with the source-code, with the goal of creating a reproducible and customizable tool for producing structurally-dissimilar dataset splits for structural RNAs.
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