PRIME is a five-level hierarchical equivariant graph model for proteins that uses physics-informed deterministic operators to exchange information across scales and achieves state-of-the-art results on fold classification and reaction class prediction.
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.Nature methods, 17(2):184–192
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MIMIC is a split-track encoder-decoder foundation model that unifies sequence reconstruction, prediction, and constrained design across nucleic acids, proteins, and regulatory context using partially observed multimodal inputs.
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PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
PRIME is a five-level hierarchical equivariant graph model for proteins that uses physics-informed deterministic operators to exchange information across scales and achieves state-of-the-art results on fold classification and reaction class prediction.
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MIMIC: A Generative Multimodal Foundation Model for Biomolecules
MIMIC is a split-track encoder-decoder foundation model that unifies sequence reconstruction, prediction, and constrained design across nucleic acids, proteins, and regulatory context using partially observed multimodal inputs.