Cross-Virus RNA Language Models for Within-Family RNA–RNA Interaction Prediction in SARS-CoV-2 and Porcine Deltacoronavirus

RNA large language models (RNA LLMs) show potential for predicting viral RNA–RNA interactions (RRIs), but their ability to transfer between viruses remains unclear. Here, we evaluated three nucleotide language models using vRIC-seq-derived RRI datasets from two members of the Coronaviridae family, SARS-CoV-2 and porcine deltacoronavirus (PDCoV). We compared within-virus five-fold cross-validation with bidirectional cross-virus testing. DNABERT achieved within-virus AUC values of 0.9595 for PDCoV and 0.9741 for SARS-CoV-2. Under cross-virus transfer, its AUC decreased to 0.8372 when trained on PDCoV and tested on SARS-CoV-2 and to 0.8116 in the reverse direction, with corresponding F1 scores of 0.7847 and 0.7617. RNAErnie retained similar but slightly lower cross-virus discrimination, whereas the Nucleotide Transformer approached random discrimination. These results provide a two-virus proof of concept that sequence-based models can retain partially shared discriminative signals across coronavirus genera. They do not establish functional, tropism-related, or receptor-dependent transferability, and broader evaluation across additional viruses and orthogonal structural validation will be required before application to emerging-virus screening.