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[2602.16548] RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion
Summary
RIDER introduces a novel framework for 3D RNA inverse design using reinforcement learning, significantly enhancing structural similarity in RNA design compared to existing methods.
Why It Matters
This research addresses a critical gap in RNA design by improving the accuracy of structural predictions, which is essential for advancements in synthetic biology and therapeutics. By optimizing for 3D structural fidelity rather than just sequence recovery, RIDER could lead to more effective RNA-based applications.
Key Takeaways
- RIDER optimizes RNA design for 3D structural similarity.
- The framework uses a GNN-based generative diffusion model.
- Improvements in native sequence recovery by 9% over state-of-the-art methods.
- Structural similarity metrics show over 100% improvement.
- RIDER discovers designs distinct from native sequences.
Computer Science > Machine Learning arXiv:2602.16548 (cs) [Submitted on 18 Feb 2026] Title:RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion Authors:Tianmeng Hu, Yongzheng Cui, Biao Luo, Ke Li View a PDF of the paper titled RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion, by Tianmeng Hu and 3 other authors View PDF HTML (experimental) Abstract: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...
