ArticleScience (New York, N.Y.)2021
Geometric deep learning of RNA structure.
Article in Science (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 197 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
197 citing papers in PubMed.
- Tryptoline stereoprobe elaboration identifies inhibitors of the GRPEL1-HSPA9 complex.Nature chemical biology · 2026Article
- Accurate RNA-Ligand Binding Site Prediction Based on a Multi-Channel Graph Neural Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Building RNA coarse-grained force fields: Design principles and training strategies.Biophysical journal · 2026Review
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Tumour-Derived Extracellular Vesicles Containing dsRNA Induce Degradation of Ribosomal Protein mRNA in Platelets.Journal of extracellular vesicles · 2026Article
- Nucleic Acid Therapeutics for "Undruggable" Cancer Targets: Mechanisms, Challenges, and Prospects.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Ethylene signaling and metabolite reprogramming in salt tolerance of Triticum aestivum.Scientific reports · 2026Article
- Are we there yet with XNA aptamers?RSC advances · 2026Review
- Spectral Graph Features for Reference-free RNA 3D Quality Assessment.bioRxiv : the preprint server for biology · 2026Article
- Graph-based deep learning approach for high-throughput protein-DNA interaction scoring.Acta pharmacologica Sinica · 2026Article
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- CDsyn: A comprehensive database for deleterious human synonymous variation prediction.iScience · 2026Article
- Functional characterization of dynamic nascent RNA folding ensembles in real time.Science advances · 2026Article
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- Integrated experimental and AI innovations for RNA structure determination.Nature biotechnology · 2026Review
- Harnessing deep learning to accelerate the development of antibodies and aptamers.Acta pharmaceutica Sinica. B · 2026Review
- Article
- Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps.Communications biology · 2026Article
- Sequence optimization of a DNA aptamer inhibiting COVID-19 infection guided by analysis of secondary structure distribution.Computational and structural biotechnology journal · 2026Article
- PARSEbp: pairwise agreement-based RNA scoring with emphasis on base pairings.Bioinformatics advances · 2026Article
137 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.
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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 OpenQuestion graph.