RNAcoder: Tokenizing RNA Structures with All-Atom Autoencoders

Tokenizing protein structures, by reconciling atomic resolution and the efficiency of coarse-grained modeling, has led to notable successes in homology search, protein design and property prediction. Extending tokenization to RNA holds great potential to capture its structural richness despite data scarcity. Yet, existing approaches overlook two key features of RNA: the centrality of base pairs and dinucleotides as building blocks, and the prevalence of chemical modifications. We introduce RNAcoder, a modification-aware, all-atom RNA structure tokenizer. Unlike previous approaches, RNAcoder tokenizes dinucleotides and base pairs alongside single nucleotides, each through a strictly local autoencoder. RNAcoder's learned tokens align with known Leontis-Westhof base pair families and the semantics of chemical modifications, although never trained on these labels. Integrated into coarse-grained graphs to represent whole RNA structures, RNAcoder tokens improve performance on small molecule binding site prediction, RNA-protein interface prediction, and ligand chemical class prediction. Our code is available anonymously at: https://anonymous.4open.science/r/RNAcoder-233E