Krueger Ryan K, Ward Max
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, United States.
Department of Computer Science and Software Engineering, The University of Western Australia, Crawley, WA 6009, Australia.
Bioinformatics. 2025 May 6;41(5). doi: 10.1093/bioinformatics/btaf203.
Differentiable 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.
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).
可微折叠是RNA设计中一种新兴的范例，其中通过梯度下降优化概率序列表示。然而，由于对所有RNA序列的预期配分函数进行微分存在显著的内存开销，现有的概念验证算法仅适用于长度≤50个核苷酸的序列。我们展示了JAX-RNAfold，这是一个开源软件包，用于我们大幅改进的可微折叠算法，该算法在单个GPU上可扩展到1250个核苷酸。我们的软件允许自然地将可微折叠作为一个模块纳入更大的深度学习管道，以及复杂的RNA设计程序，如具有灵活目标函数的mRNA设计。
JAX-RNAfold托管在GitHub（https://github.com/rkruegs123/jax-rnafold）上，可以作为Python包在本地安装。所有源代码也存档在Zenodo（https://doi.org/10.5281/zenodo.15003072）上。
