Zero-Shot RNA Modification Site Prediction with Molecular Side Information

RNA modification site prediction can prioritize candidate loci for experimental validation, but site annotations are highly uneven across modification types. A modification can be chemically characterized before a sufficiently large transcriptome-wide site map is available because profiling depends on modification-specific assays and curation. Conventional sequence predictors treat modification identity as a fixed supervised label, so they cannot use this known molecular identity when the corresponding site labels are absent. We address this gap by augmenting MultiRM with standardized annotations of complete modified nucleosides and using them as molecular side information in a shared RNA-modification compatibility framework. The resulting modification embedding conditions RNA-context selection and final scoring, allowing one model to evaluate a held-out modification without a target-specific output head. Across twelve provenance-aware inductive leave-one-modification-out tasks and stricter source-separated and grouped-substructure holdouts, molecular side information improves zero-shot ranking. We further evaluate transfer to ac4C, which is not included among the twelve modification types in MultiRM. Transfer is strongest when training includes modifications with related molecular substructures and becomes less important as direct target labels accumulate. The framework therefore supports candidate-site prioritization for unseen modification types and for modifications with few annotated sites when their molecular identity is known. Resources are available at: https://anonymous.4open.science/status/RNA_modification-1178.