  Accurate RNA secondary structure prediction is vital for understanding
cellular regulation and disease mechanisms. Deep learning (DL) methods have
surpassed traditional algorithms by predicting complex features like
pseudoknots and multi-interacting base pairs. However, traditional distance
measures can hardly deal with such tertiary interactions and the currently used
evaluation measures (F1 score, MCC) have limitations. We propose the
Weisfeiler-Lehman graph kernel (WL) as an alternative metric. Embracing
graph-based metrics like WL enables fair and accurate evaluation of RNA
structure prediction algorithms. Further, WL provides informative guidance, as
demonstrated in an RNA design experiment.
