# RNA Inverse Folding

RNA inverse folding has long relied on iterative optimization scored by an external folding algorithm, which is sensitive to initialization and prone to local optima. This work defines multinomial diffusion directly over nucleotide sequences, guides denoising with a target secondary structure, and compares a 2D contact-map projection with a purely 1D pairing-aware architecture. On the in-distribution test set, the 2D and 1D models reach median normalized RNAforester similarities of 0.82 and 0.79, while generated sequences stay close to random-sequence diversity.

RNA inverse folding has long relied on iterative optimization scored by an external folding algorithm, which is sensitive to initialization and prone to local optima. This work defines multinomial diffusion directly over nucleotide sequences, guides denoising with a target secondary structure, and compares a 2D contact-map projection with a purely 1D pairing-aware architecture. On the in-distribution test set, the 2D and 1D models reach median normalized RNAforester similarities of 0.82 and 0.79, while generated sequences stay close to random-sequence diversity.

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.

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.

Structure-conditioned RNA inverse design previously either trained a separate policy per target backbone or relied on offline preference optimization, leaving online post-training of a reusable backbone-conditioned model largely unexplored. MORAD maps six design measurements to bounded desirability scores, combines them with a weighted geometric mean, and uses group-relative normalization to drive a forward-process diffusion update. On 153 evaluation targets it raises GDT-TS from 0.3300 to 0.3827 and pairing MCC from 0.6110 to 0.7295, while cutting normalized ensemble defect from 0.3906 to 0.2830.

Structure-conditioned RNA inverse design previously either trained a separate policy per target backbone or relied on offline preference optimization, leaving online post-training of a reusable backbone-conditioned model largely unexplored. MORAD maps six design measurements to bounded desirability scores, combines them with a weighted geometric mean, and uses group-relative normalization to drive a forward-process diffusion update. On 153 evaluation targets it raises GDT-TS from 0.3300 to 0.3827 and pairing MCC from 0.6110 to 0.7295, while cutting normalized ensemble defect from 0.3906 to 0.2830.