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La-Ribo advances generative AI in computational biology by employing geometry-latent flow matching to solve the complex problem of joint RNA sequence and 3D structure design. This approach significantly improves designability and codesignability over existing baselines, addressing a critical bottleneck where global folding must be coordinated with nucleotide-level detail under limited supervision. The method's ability to generate large-scale structural corpora without additional training marks a notable step forward in AI-driven molecular discovery.
La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching
arXiv:2610.12236v1 Announce Type: cross Abstract: RNA function arises from the coupling of nucleotide sequence and three-dimensional structure, motivating their joint design. Coordinating global folding with nucleotide-level detail remains challenging under limited structural supervision. We introduce La-Ribo, a generative framework for RNA sequence-structure co-design via geometry-latent flow matching. La-Ribo retains a sparse phosphate-sugar--base scaffold and encodes nucleotide identity and local conformation in residue-wise latents. A shared flow network generates both jointly, and an RNA-specific decoder then reconstructs all heavy atoms. To expand supervision, we construct a quality-controlled corpus of 168,561 RNA structures, integrating experimental data with predictions from three folding models, including 10,631 MSA-supported structures generated in this work. La-Ribo improves designability and codesignability over the evaluated baselines across sampling budgets and two refolding models, and the same prior supports scaffold-conditioned inverse folding without additional training.
Abstract:RNA function arises from the coupling of nucleotide sequence and three-dimensional structure, motivating their joint design. Coordinating global folding with nucleotide-level detail remains challenging under limited structural supervision. We introduce La-Ribo, a generative framework for RNA sequence-structure co-design via geometry-latent flow matching. La-Ribo retains a sparse phosphate-sugar--base scaffold and encodes nucleotide identity and local conformation in residue-wise latents. A shared flow network generates both jointly, and an RNA-specific decoder then reconstructs all heavy atoms. To expand supervision, we construct a quality-controlled corpus of 168,561 RNA structures, integrating experimental data with predictions from three folding models, including 10,631 MSA-supported structures generated in this work. La-Ribo improves designability and codesignability over the evaluated baselines across sampling budgets and two refolding models, and the same prior supports scaffold-conditioned inverse folding without additional training.
Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI)
arXiv:2610.12236 [q-bio.BM]
(or arXiv:2610.12236v1 [q-bio.BM] for this version)
https://doi.org/10.48550/arXiv.2610.12236 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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