AI RESEARCH
RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion
arXiv CS.LG
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ArXi:2602.16548v2 Announce Type: replace 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.