Machine learning for RNA secondary structure prediction: a review of current methods and challenges

Schematic representation of deep learning methods for RNA secondary structure prediction (not including foundation models). Dotted arrows indicate steps that are only included in training, and squared brackets indicate optional inputs. Ab initio methods predict structure from a single RNA sequence only; evolutionary methods leverage multiple sequence alignments (MSA) to capture coevolutionary signals; hybrid methods integrate deep learning with thermodynamic models or experimental data.