  The secondary structure of ribonucleic acid (RNA) is more stable and
accessible in the cell than its tertiary structure, making it essential for
functional prediction. Although deep learning has shown promising results in
this field, current methods suffer from poor generalization and high
complexity. In this work, we reformulate the RNA secondary structure prediction
as a K-Rook problem, thereby simplifying the prediction process into
probabilistic matching within a finite solution space. Building on this
innovative perspective, we introduce RFold, a simple yet effective method that
learns to predict the most matching K-Rook solution from the given sequence.
RFold employs a bi-dimensional optimization strategy that decomposes the
probabilistic matching problem into row-wise and column-wise components to
reduce the matching complexity, simplifying the solving process while
guaranteeing the validity of the output. Extensive experiments demonstrate that
RFold achieves competitive performance and about eight times faster inference
efficiency than the state-of-the-art approaches. The code and Colab demo are
available in (http://github.com/A4Bio/RFold).
