ESM2 Perplexity

License: ESM2 is open source and free for academic and commercial use under an MIT license. Please refer to the license for full terms.

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evo-design/proto-language/proto_language/constraint/sequence_scoring/esm2_perplexity_constraint.py

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@article{lin2023esm2,
  title={Evolutionary-scale prediction of atomic-level protein structure with a language model},
  author={Lin, Zeming and Akin, Halil and Rao, Roshan and Hie, Brian and Zhu, Zhongkai and Lu, Wenting and Smetanin, Nikita and Verkuil, Robert and Kabeli, Ori and Shmueli, Yaniv and others},
  journal={Science},
  volume={379},
  number={6637},
  pages={1123--1130},
  year={2023},
  publisher={American Association for the Advancement of Science},
  doi={10.1126/science.ade2574}
}

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Constraint contributors

Forward ESM2 protein scoring via masked pseudo-log-likelihood.

API Reference

ConfigESM2PerplexityConfig Source

Configuration for ESM2 perplexity scoring (forward + gradient).

enum

default:"esm2_t33_650M_UR50D"

ESM2 model checkpoint to useOptions: esm2_t6_8M_UR50D, esm2_t12_35M_UR50D, esm2_t30_150M_UR50D, esm2_t33_650M_UR50D, esm2_t36_3B_UR50D, esm2_t48_15B_UR50D

number

required

Softmax temperature for ESM2 (fixed for this constraint, not varied per optimizer step).

boolean

default:"True"

Hard one-hot forward pass with soft-probability gradients.

string

default:"cuda"

Device for ESM2 execution, e.g. ‘cuda’ or ‘cuda:0’.

integer

AA positions per ESM2 forward pass. Lower if OOM, higher for throughput.

enum

default:"nll"

Return raw mean NLL by default, or ESM2 perplexity when set to ‘ppl’.Options: nll, ppl

number

default:"1.0"

Pre-scale raw logits before ESM2; gradients are scaled back by the same factor.

SequenceLogitBiasConfig

Declarative sequence-symbol bias (canonical 20-AA protein) added before ESM2.

Usage

python

from proto_language.core import Constraint
from proto_language.constraint import esm2_perplexity_constraint, ESM2PerplexityConfig

constraint = Constraint(
    inputs=[segment],
    function=esm2_perplexity_constraint,
    function_config=ESM2PerplexityConfig(
        # Configure parameters here
    ),
)

scores = constraint.evaluate()

Metadata

Property	Value
Key	`esm2-perplexity`
Function	`esm2_perplexity_constraint`
Category	`sequence_scoring`
Mode	`dual`
Uses GPU	`True`
Supported Types	`protein`

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ESM2 Perplexity

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