  Computational RNA design tasks are often posed as inverse problems, where
sequences are designed based on adopting a single desired secondary structure
without considering 3D geometry and conformational diversity. We introduce
gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design
sequences that explicitly account for structure and dynamics. Under the hood,
gRNAde is a multi-state Graph Neural Network that generates candidate RNA
sequences conditioned on one or more 3D backbone structures where the
identities of the bases are unknown. On a single-state fixed backbone re-design
benchmark of 14 RNA structures from the PDB identified by Das et al. [2010],
gRNAde obtains higher native sequence recovery rates (56% on average) compared
to Rosetta (45% on average), taking under a second to produce designs compared
to the reported hours for Rosetta. We further demonstrate the utility of gRNAde
on a new benchmark of multi-state design for structurally flexible RNAs, as
well as zero-shot ranking of mutational fitness landscapes in a retrospective
analysis of a recent RNA polymerase ribozyme structure. Open source code:
https://github.com/chaitjo/geometric-rna-design
