Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation
Multimodal models that jointly reason over protein sequences, structures, and function annotations within a unified representation hold immense potential for integrating multimodal data and generating new proteins with designed functional properties. To utilize transformer architectures, such models require a tokenizer that converts protein structure from continuous atomic coordinates into discrete representations suitable for scalable multimodal training. The quality of such models are fundamentally upper bounded by the fidelity and expressiveness of the underlying tokenized structure. However, existing tokenizers prioritize reconstruction over generative abilities. To address these gaps...
| Authors | Nabin Giri, Steven Farrell, Kristofer E. Bouchard |
|---|---|
| Primary category | q-bio.BM |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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