Machine learning and language models for RNA structure prediction: Progress and perspectives

RNA structure is central to the function of every RNA class yet the gap between annotated sequences and experimentally determined structures remains large. Computational methods to fill this gap have evolved from thermodynamic free energy minimization through supervised deep learning to self-supervised RNA language models trained on millions of sequences, progressively improving structure prediction. Here we review the state of the art in RNA structure prediction, covering key training datasets, community benchmarks, and the performance of current models. We further discuss perspectives on integrating other data modalities, such as chemical probing signals and RNA modifications, as well as the emerging role of generative models. Challenges in generalization, handling of noncanonical interactions, and contextual structure prediction remain open frontiers for the field.