用语言模型直接生成匹配目标结构的RNA序列，速度快且效果优。
Designing RNAs with Language Models
- 将RNA设计转为条件序列生成任务，用自回归语言模型建模结构到序列的映射。
- 在4个数据集上提升玻尔兹曼概率，速度比现有方法快1.7倍。
- 适合需要快速设计高精度RNA序列的研究者，尤其擅长复杂结构生成。
RNA设计旨在寻找能折叠成目标二级结构的序列，具有广泛生物医学应用，但因序列空间指数级增长和竞争性折叠众多而计算困难。传统方法将其视为优化问题，依赖实例特定启发式或约束搜索。本文将RNA设计重新定义为条件序列生成，并引入可复用的神经近似器——自回归语言模型（LM），直接从目标结构生成序列。先在随机诱导的结构-序列对上进行监督训练，再通过强化学习（RL）端到端优化关键指标。提出一种小样本选择策略，显著提升RL效率与质量。在四个数据集上，该方法在玻尔兹曼概率等关键指标上超越现有最先进系统，且速度提升1.7倍，确立了条件语言模型生成作为可扩展、任务无关的替代方案。代码与数据已公开于 https://github.com/KuNyaa/RNA-Design-LM。
原文摘要 · Abstract (English)
RNA design, the task of finding a sequence that folds into a target secondary structure, has broad biological and biomedical impact but remains computationally challenging due to the exponentially large sequence space and exponentially many competing folds. Traditional approaches treat it as an optimization problem, relying on per-instance heuristics or constraint-based search. We instead reframe RNA design as conditional sequence generation and introduce a reusable neural approximator, instantiated as an autoregressive language model (LM), that maps target structures directly to sequences. We first train our model in a supervised setting on random-induced structure-sequence pairs, and then use reinforcement learning (RL) to optimize end-to-end metrics. We also propose methods to select a small subset for RL that greatly improves RL efficiency and quality. Across four datasets, our approach outperforms state-of-the-art systems on key metrics such as Boltzmann probability while being 1.7x faster, establishing conditional LM generation as a scalable, task-agnostic alternative to per-instance optimization for RNA design. Our code and data are available at https://github.com/KuNyaa/RNA-Design-LM.
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