Compositional Representation Learning for RNA Modifications via Chemical Transformation Equivariance

Predicting nanopore responses for RNA modifications with limited or no response labels requires transferring knowledge across chemical types and sequence contexts. Recurring chemical transformations provide a basis for this sharing: they act across nucleoside substrates and compose along valid chemical paths. We formulate **Chemical Transformation Equivariance (CTE)**, under which valid chemical transformations are approximately preserved by corresponding actions on molecular representations. **RiboCTE** learns these actions in a shared latent subspace from precursor–product relations and valid paths, using frozen molecular embeddings as prediction targets. The same actions transform sequence-conditioned states, and a shared decoder predicts changes in nanopore substitution-plus-deletion rates relative to canonical controls. We introduce **RiboModBench** to evaluate cross-modification response prediction alongside chemical-action transfer and composition. RiboCTE achieves the highest mean response-ranking performance among the compared methods in modification/context holdouts and external evaluation. It reaches Spearman correlations of $0.184$ when both modifications and sequence contexts are held out and $0.268$ on external modifications without source response labels. Shared chemical actions thus provide an effective inductive bias for RNA modification representation learning. Code and RiboModBench are available at https://anonymous.4open.science/r/cte-7B11.