RNA inverse folding stops reading only the backbone: treating molecular surface as a design constraint tops sequence recovery on all three benchmarks
RNA inverse folding recovers a nucleotide sequence from a target 3D structure, and earlier methods leaned on secondary structure and backbone topology while rarely constraining the molecular surface that shapes intermolecular interactions. gRFold fuses a surface point cloud, a backbone graph and secondary structure through hierarchical cross-attention to drive autoregressive decoding, then re-ranks candidates at inference by agreement between predicted folds and the target base-pairing pattern. On the RNA-protein complex, monomeric RNA and RNA-Puzzles benchmarks, gRFold reaches sequence recovery of 49.65%, 51.67% and 45.00%, the highest among the compared methods, and ablations show the surface and backbone branches contribute complementary gains.