ArticleNature communications2025
Generative and predictive neural networks for the design of functional RNA molecules.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
16 citing papers in PubMed.
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- Crowdsourced riboregulators reveal design principles for programmable RNA switching.bioRxiv : the preprint server for biology · 2026Article
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Toehold-VISTA: a machine learning approach to decipher programmable RNA sensor-target interactions.Nucleic acids research · 2026Article
- Programmable fluorescent aptamer-based RNA switches for rapid identification of point mutations.Nature chemistry · 2025Article
- Generating functional plasmid origins with OriGen.Nucleic acids research · 2025Article
- An automated decision support platform for rural environmental adaptive design based on VAE-ANAS integration.Scientific reports · 2025Article
- ROS-induced voltage-gated ion channel expression and electrophysiological remodeling in malignant human cells.NPJ systems biology and applications · 2025Article
- RNAtranslator: Modeling protein-conditional RNA design as sequence-to-sequence natural language translation.PLoS computational biology · 2025Article
- A generalized and efficient approach for complete mRNA design improves translation, stability and specificity.bioRxiv : the preprint server for biology · 2025Article
- Generative and predictive neural networks for the design of functional RNA molecules.Nature communications · 2025Article
- A generative framework for enhanced cell-type specificity in rationally designed mRNAs.bioRxiv : the preprint server for biology · 2024Article
- Article
- Applications of artificial intelligence and machine learning in dynamic pathway engineering.Biochemical Society transactions · 2023Review
Corrections and comments
- Update of
Authors and funding
4 authors.
Funding
Abstract
RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence, structure, and function of RNA often necessitates extensive experimental screening of candidate sequences. Here we present a generalized, efficient neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions across a diverse range of settings. We pair these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of a diverse range of functional RNA molecules with targeted experimental attributes. This approach enables the design of novel sequence candidates that outperform those encountered during training or returned by classical thermodynamic algorithms, and can be deployed using as few as 384 example sequences. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of RNA molecules with improved function.
Indexed as
Identifiers
What OpenQuestion holds
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.