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Computational RNA design tasks are often posed as inverse problems, wheresequences are designed based on adopting a single desired secondary structurewithout considering 3D conformational diversity. We introduce gRNAde, ageometric RNA design pipeline operating on 3D RNA backbones to design sequencesthat explicitly account for structure and dynamics. gRNAde uses a multi-stateGraph Neural Network and autoregressive decoding to generates candidate RNAsequences conditioned on one or more 3D backbone structures where theidentities of the bases are unknown. On a single-state fixed backbone re-designbenchmark of 14 RNA structures from the PDB identified by Das et al. (2010),gRNAde obtains higher native sequence recovery rates (56% on average) comparedto Rosetta (45% on average), taking under a second to produce designs comparedto the reported hours for Rosetta. We further demonstrate the utility of gRNAdeon a new benchmark of multi-state design for structurally flexible RNAs, aswell as zero-shot ranking of mutational fitness landscapes in a retrospectiveanalysis of a recent ribozyme. Experimental wet lab validation on 10 differentstructured RNA backbones finds that gRNAde has a success rate of 50% atdesigning pseudoknotted RNA structures, a significant advance over 35% forRosetta. Open source code and tutorials are available at:https://github.com/chaitjo/geometric-rna-design

gRNAde: Geometric Deep Learning for 3D RNA inverse design