The inverse design of RNA three-dimensional (3D) structures is crucial for engineering functional RNAs in synthetic biology and therapeutics. While recent deep learning approaches have advanced this field, they are typically optimized and evaluated using native sequence recovery, which is a limited surrogate for structural fidelity, since different sequences can fold into similar 3D structures and high recovery does not necessarily indicate correct folding. To address this limitation, we propose RIDER, an RNA Inverse DEsign framework with Reinforcement learning that directly optimizes for 3D structural similarity. First, we develop and pre-train a GNN-based generative diffusion model conditioned on the target 3D structure, achieving a 9% improvement in native sequence recovery over state-of-the-art methods. Then, we fine-tune the model with an improved policy gradient algorithm using four task-specific reward functions based on 3D self-consistency metrics. Experimental results show that RIDER improves structural similarity by over 100% across all metrics and discovers designs that are distinct from native sequences.

翻译：RNA三维结构的逆向设计对于合成生物学和治疗学中功能性RNA的工程化至关重要。尽管近期的深度学习方法已推动该领域发展，但这些方法通常使用天然序列恢复率进行优化和评估，而该指标作为结构保真度的替代标准存在局限，因为不同序列可折叠成相似的三维结构，且高恢复率未必意味着正确折叠。为解决此局限，我们提出RIDER——一种基于强化学习的RNA逆向设计框架，可直接优化三维结构相似性。首先，我们开发并预训练了基于图神经网络的生成扩散模型，该模型以目标三维结构为条件，在天然序列恢复率上相比现有最优方法提升9%。随后，我们采用改进的策略梯度算法，基于四种三维自洽度量指标构建的任务特定奖励函数对模型进行微调。实验结果表明，RIDER在所有度量指标上均实现超过100%的结构相似性提升，并能发现与天然序列截然不同的设计方案。