ArticleBioinformatics (Oxford, England)2025
JAX-RNAfold: scalable differentiable folding.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Gradient-based optimization for mRNA sequence design.Bioinformatics (Oxford, England) · 2026Article
- Simulation of CRISPR/Cas9-mediated gene editing for the Vitellogenin gene in Apis mellifera.Scientific reports · 2026Article
- Physics-guided design of intrinsically disordered proteins.bioRxiv : the preprint server for biology · 2026Article
- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- What does it take to learn the rules of RNA base pairing? A lot less than you may think.Communications biology · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- A method for estimating energy parameters of RNAs by differentiating base-pairing probabilities.NAR genomics and bioinformatics · 2025Article
- Deep generative optimization of mRNA codon sequences for enhanced mRNA translation and therapeutic efficacy.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
- Pre-clinical evaluation of mRNA-lipid nanoparticles' potency and toxicity: current practices and future directions.In vitro models · 2025Review
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2 authors.
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Abstract
summaryDifferentiable folding is an emerging paradigm for RNA design in which a probabilistic sequence representation is optimized via gradient descent. However, given the significant memory overhead of differentiating the expected partition function over all RNA sequences, the existing proof-of-concept algorithm only scales to ≤50 nucleotides. We present JAX-RNAfold, an open-source software package for our drastically improved differentiable folding algorithm that scales to 1,250 nucleotides on a single GPU. Our software permits the natural inclusion of differentiable folding as a module in larger deep learning pipelines, as well as complex RNA design procedures such as mRNA design with flexible objective functions. AVAILABILITY AND IMPLEMENTATION: JAX-RNAfold is hosted on GitHub (https://github.com/rkruegs123/jax-rnafold) and can be installed locally as a Python package. All source code is also archived on Zenodo (https://doi.org/10.5281/zenodo.15003072).
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