An open structure-guided RNA foundation model for generalizable prediction

Abstract RNA language models have achieved strong performance across diverse down-stream tasks by leveraging large-scale sequence data. However, RNA function is fundamentally shaped by hierarchical structure, making structural information important for pre-training. Existing approaches often depend on noisy annotations or task-specific objectives that can limit generalizability. Here we present structRFM, a structure-guided RNA foundation model pre-trained on 21 million RNA sequence–structure pairs by integrating base-pairing interactions into masked language modelling through a pair-matching operation. We also introduce MUSES, a multi-source ensemble of secondary structures designed to mitigate predictor bias, and a dynamic masking ratio that balances structure-guided and nucleotide-level masking. structRFM learns joint sequence and structure representations as sequence-level, nucleotide-level and pairwise features for broad downstream adaptation. It ranks among the leading models in zero-shot homology classification and establishes new benchmarks for secondary-structure prediction. Its tertiary-structure adaptation, Zfold, consistently improves both three-dimensional predictions and their extracted secondary structures, with performance comparable to AlphaFold3 [31] across CASP15-natural, CASP16 and RNA-Puzzles. structRFM also improves performance on function-prediction tasks and generalizes to newly released RNA families and long non-coding RNAs. Together, these results demonstrate the effectiveness of structure-guided pre-training and highlight multimodal RNA language models for robust structural and functional inference. The complete 21-million-pair dataset, model weights and source code are openly available to support multimodal RNA foundation-model development.