RfamFlow: Nested Guidance for RNA Family Generation with Discrete Flow Matching

Designing RNA sequences that belong to a target family is central to RNA engineering. Existing generators either train a separate model per family, failing when a family has few known sequences, or condition a single autoregressive model on the family label, still degrading on the many small and medium families of the highly imbalanced Rfam database. We present RfamFlow, a conditional discrete flow-matching model that generates RNA sequences for any Rfam family from its tag alone, with no structural input. RfamFlow couples a Diffusion Transformer (DiT) with a nested classifier-free guidance scheme that follows the Rfam taxonomy, allowing a data-poor family to borrow a prior from its coarser taxonomic levels. The single trained model supports de novo generation, masked reconstruction, and zero-shot fitness prediction without task-specific fine-tuning. RfamFlow outperforms the comparable family-conditioned baseline models across large, medium, and small families, with its nested guidance helping most where family data are scarcest. Furthermore, it matches far larger RNA foundation models on reconstruction and fitness prediction.