The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential for functional prediction. Although deep learning has shown promising results in this field, current methods suffer from poor generalization and high complexity. In this work, we present RFold, a simple yet effective RNA secondary structure prediction in an end-to-end manner. RFold introduces a decoupled optimization process that decomposes the vanilla constraint satisfaction problem into row-wise and column-wise optimization, simplifying the solving process while guaranteeing the validity of the output. Moreover, RFold adopts attention maps as informative representations instead of designing hand-crafted features. Extensive experiments demonstrate that RFold achieves competitive performance and about eight times faster inference efficiency than the state-of-the-art method. The code and Colab demo are available in \href{http://github.com/A4Bio/RFold}{http://github.com/A4Bio/RFold}.

翻译：核糖核酸（RNA）的二级结构在细胞中比其三级结构更稳定且更易获取，因此对功能预测至关重要。尽管深度学习在该领域已展现出有前景的结果，但现有方法仍面临泛化能力差和计算复杂度高的问题。本研究提出RFold，一种简洁且有效的端到端RNA二级结构预测方法。RFold引入解耦优化过程，将原始的约束满足问题分解为行优化和列优化，在简化求解过程的同时确保输出结果的合法性。此外，RFold采用注意力图作为信息表示，而非设计手工特征。大量实验证明，RFold在达到竞争性性能的同时，其推理速度比最先进方法快约八倍。代码和Colab演示可在\href{http://github.com/A4Bio/RFold}{http://github.com/A4Bio/RFold}获取。