esm

name	esm
description	ESM protein language models for embeddings, sequence scoring, structure prediction, and binder design. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) or mutation-effect scores, (2) Getting protein embeddings for clustering or filtering, (3) Predicting complex structures with ESMFold2, (4) Designing binders by inverting ESMFold2, (5) Filtering designs by sequence plausibility. For diffusion-based structure prediction, use boltz or chai. For QC thresholds, use protein-qc. For gradient-based multi-objective design, use mosaic.
license	MIT
category	design-tools
tags	["sequence-design","embeddings","scoring","structure-prediction","binder"]
proteinbase_slug	esm2-optimization
proteinbase_url	https://proteinbase.com/design-methods/esm2-optimization
biomodals_script	modal_esm2_predict_masked.py

ESM Protein Language Models

The ESM line is maintained at github.com/Biohub/esm (Chan Zuckerberg Biohub, MIT license; the older evolutionaryscale/esm URL redirects here). The current generation ships three artifacts: ESM C (language model), ESMFold2 (structure prediction), and ESM Atlas (a map of predicted structures). Weights are on huggingface.co/biohub; the hosted API is at biohub.ai.

This skill covers ESM C, ESMFold2, and legacy ESM2. ESM3 is not covered because its open weights are non-commercial.

Which model to use

Task	Model
Embeddings, PLL, mutation scoring	ESM C (ESMC-6B), or ESM2 for a lighter run
Complex structure prediction	ESMFold2
High-throughput single-sequence folding	ESMFold2 fast mode
Binder design	ESMFold2 inversion (see below), or the mosaic / bindcraft skills
Variant effect / zero-shot scoring	ESM C or ESM2

Prerequisites

Requirement	Minimum	Recommended
Python	3.10+	3.11
PyTorch	2.0+	Latest
CUDA	12.0+	12.1+
GPU VRAM	24GB (ESM2 / small ESMC)	80GB (ESMC-6B, ESMFold2)

ESM C: embeddings and scoring

ESM C is the successor to ESM2. It improves long-range structural understanding as model scale grows and is the default choice for embeddings, pseudo-log-likelihood, and mutation-effect scoring.

Python (Hugging Face)

from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch

model_id = "biohub/ESMC-6B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForMaskedLM.from_pretrained(
    model_id, output_hidden_states=True, torch_dtype=torch.bfloat16
).().cuda()

batch = tok([], return_tensors=).to()
 torch.no_grad():
    out = model(**batch)

logits = out.logits                      
embeddings = out.hidden_states[-]