ESM3 reasons over the sequence, structure, and function of proteins. All three modalities are represented by tokens, and are input and output as separate tracks that are fused into a single latent space within the model. ESM3 is trained with a generative masked language modeling objective. User:
ESM3 masked language modeling noise schedule representation learning User:
Tokenization Protein structures Discrete auto-encoder Invariant geometric attention mechanism Local reference frames Global frame Computational primitives Attention Sequence of discrete tokens User:
ESM3 decoder autoencoder geometric loss CAMEO RMSD Fig. S3
ESM3 geometric attention projection transformer atomic coordinates tokenized structure SS8 SASA function tokenized keyword sets
ESM3 is a bidirectional transformer. While extensive research has gone into creating specialized architectures and training objectives for proteins, we find that tokenization paired with a standard masked language modeling objective and the basic transformer architecture is highly effective for both representation learning and generative modeling. Sequence, structure, and function tracks are input as tokens, which are embedded and fused, then processed through a transformer architecture. User:
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ESM3 ESM3 architecture Structure tokenization Models are trained at three scales Unconditional generations from ESM3 98B User:
ESM3 proteins sequence structure databases experimentally determined predicted structures inverse folding model synthetic sequences functional annotations hidden markov models training data unique tokens Appendix A.2.1.8
ESM3 models are trained at three scales: 1.4 billion, 7 billion, and 98 billion parameters. In a series of experiments to evaluate representation learning performance, we found that increasing depth had a greater response than increasing width. This led us to choose relatively deep networks for the final architectures, with the 98 billion parameter model incorporating 216 Transformer blocks.
ESM3 ESMFold CAMEO LDDT pLDDT pTM TM score