RNA-DFM: Masked Discrete Flow Matching for 3D RNA Inverse Design

Designing RNA sequences that adopt desired three-dimensional structures is central to engineering functional RNA molecules, yet efficient generation with high structural fidelity remains challenging under limited structural supervision. We introduce RNA-DFM, a masked discrete flow matching model that predicts multiple nucleotide identities in parallel conditioned on the target backbone. To expand structural supervision beyond the limited coverage of experimentally determined RNA structures, we augment training with Rfam sequences paired with filtered structure predictions. We further adopt best-of-N distillation, combining structurally selected candidates with native sequences to finetune the generator on training backbones. The resulting model improves structural quality on unseen backbones under limited candidate budgets, with experiments demonstrating gains in both structural quality and sampling efficiency; Rfam augmentation further improves TM-score and native sequence recovery while reducing RMSD. Among the evaluated methods, pretrained RNA-DFM achieves the highest TM-score, the lowest RMSD, and the highest native sequence recovery of 55.9%.