RiboSBDD and RiboSBDDBench: Structure Based Small Molecule Generation for RNA

Structure based drug design (SBDD) for RNA is limited by scarce RNA ligand structures and flexible binding pockets. We introduce **RiboSBDD**, an $\mathrm{SE}(3)$ equivariant diffusion framework for RNA conditioned 3D ligand generation. Despite different backbones, interfaces between ligands and proteins or RNA share local packing principles. A unified all atom representation lets us transfer these geometric priors through protein pretraining and RNA specific fine tuning. To learn local pocket adjustment without receptor trajectories, we restrict motion to nucleobase rotations about glycosidic bonds with a fixed backbone. Synthetic rotations provide analytical inverse targets. This couples base correction with ligand denoising through a differentiable pocket transformation. To retain protein derived geometric guidance after RNA specialization, an adaptive sampler combines coordinate proposals from the frozen protein source and RNA target using local equal density composition and potential guided particle resampling. We leave atom types to the target. We also establish **RiboSBDDBench**, with 8,006 curated RNA pocket ligand instances, grouped structural splits, and evaluation across multiple metrics. On 713 test pockets, RiboSBDD improves contact recovery and steric compatibility over TargetDiff after RNA fine tuning. Together, RiboSBDD and RiboSBDDBench provide a generative framework that combines transfer with local flexibility and a standardized evaluation foundation for RNA targeted small molecule design.