  RNA design shows growing applications in synthetic biology and therapeutics,
driven by the crucial role of RNA in various biological processes. A
fundamental challenge is to find functional RNA sequences that satisfy given
structural constraints, known as the inverse folding problem. Computational
approaches have emerged to address this problem based on secondary structures.
However, designing RNA sequences directly from 3D structures is still
challenging, due to the scarcity of data, the non-unique structure-sequence
mapping, and the flexibility of RNA conformation. In this study, we propose
RiboDiffusion, a generative diffusion model for RNA inverse folding that can
learn the conditional distribution of RNA sequences given 3D backbone
structures. Our model consists of a graph neural network-based structure module
and a Transformer-based sequence module, which iteratively transforms random
sequences into desired sequences. By tuning the sampling weight, our model
allows for a trade-off between sequence recovery and diversity to explore more
candidates. We split test sets based on RNA clustering with different cut-offs
for sequence or structure similarity. Our model outperforms baselines in
sequence recovery, with an average relative improvement of $11\%$ for sequence
similarity splits and $16\%$ for structure similarity splits. Moreover,
RiboDiffusion performs consistently well across various RNA length categories
and RNA types. We also apply in-silico folding to validate whether the
generated sequences can fold into the given 3D RNA backbones. Our method could
be a powerful tool for RNA design that explores the vast sequence space and
finds novel solutions to 3D structural constraints.
