Nüwa.RNA: An RNA Foundation Model for Unified Representation with Deep Structure Infusion

RNA function arises from a hierarchical folding process in which primary sequences form secondary structures that govern biological activity. While foundation models have proven effective for learning transferable RNA representations, most existing approaches rely on primary sequence alone or incorporate structural information only superficially during pre-training. Three problems limit current approaches: experimentally determined secondary structures are scarce, computational pseudo-labels are noisy, and no existing model integrates structure at multiple levels across masking, architecture, and supervision. Here, we present \textbf{N\"uwa.RNA}, a pre-trained foundation model built on a \emph{structure-driven multi-level pre-training framework} that integrates secondary structure at all three stages of pre-training simultaneously. N\"uwa.RNA introduces dual-granularity masking that combines single-token masking with structure motif masking, treating entire structural elements as indivisible units; a noisy structural attention bias that injects curriculum-corrupted contact maps into every Transformer layer to model pseudo-label uncertainty; and a structure denoising objective that recovers clean base-pairing states from corrupted input, completing a closed denoising loop. Pre-trained on a large-scale non-coding RNA corpus, N\"uwa.RNA achieves state-of-the-art performance on 10 of 13 BEACON benchmark tasks, with up to $+$7.83 F1 improvement on structure prediction, and attains the highest zero-shot secondary structure prediction accuracy among single-sequence RNA foundation models.