esm ยท diff
v1.3 to v2.0
148 added, 288 removed. Audit A to A.
---
name: esm
- description: Use when working directly with the `esm` Python SDK, ESM3 or ESMC model IDs, Forge/Biohub inference clients, or ESMFold2 folding workflows.
+ description: Uses the Biohub esm Python SDK for ESM3 protein generation, ESMC embeddings, and ESMFold2 all-atom folding. Applies to local model inference and Biohub hosted clients, including former Forge workflows; distinguishes the separate legacy fair-esm distribution.
license: MIT license
+ compatibility: Requires Python 3.12+ and esm 3.4.1.post1. Local pretrained inference needs model weights and sufficient RAM or GPU memory; hosted inference needs network access and ESM_API_KEY. Use an isolated environment, separate from fair-esm.
metadata:
- version: "1.3"
+ version: "2.0"
skill-author: K-Dense Inc.
+ last-reviewed: "2026-09-30"
+ upstream-version: "3.4.1.post1"
---
- # ESM: Evolutionary Scale Modeling
-
- ## Overview
-
- ESM provides protein language models for understanding, generating, and designing proteins. Use this skill for current EvolutionaryScale/Biohub workflows: ESM3 for generative design, ESMC for representation learning and embeddings, hosted Forge/Biohub inference, and ESMFold2 all-atom structure prediction.
-
- ## Core Capabilities
-
- ### 1. Protein Sequence Generation with ESM3
-
- Generate novel protein sequences with desired properties using multimodal generative modeling.
-
- **When to use:**
- - Designing proteins with specific functional properties
- - Completing partial protein sequences
- - Generating variants of existing proteins
- - Creating proteins with desired structural characteristics
-
- **Basic usage:**
-
- ```python
- from esm.models.esm3 import ESM3
- from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig
-
- # Load local open weights after accepting the license on Hugging Face.
- model: ESM3InferenceClient = ESM3.from_pretrained("esm3-open").to("cuda")
-
- # Create protein prompt
- protein = ESMProtein(sequence="MPRT___KEND") # '_' represents masked positions
-
- # Generate completion
- protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))
- print(protein.sequence)
- ```
-
- **For remote/cloud usage via Forge API:**
-
- ```python
- import os
- import esm
- from esm.sdk.api import ESMProtein, GenerationConfig
-
- # Same interface as local ESM3; token from ESM_API_KEY (see Authentication)
- model = esm.sdk.client("esm3-medium-2024-08", token=os.environ["ESM_API_KEY"])
-
- # Generate
- protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))
- ```
-
- See `references/esm3-api.md` for detailed ESM3 model specifications, advanced generation configurations, and multimodal prompting examples.
-
- ### 2. Structure Prediction and Inverse Folding
-
- Use ESM3's structure track for structure prediction from sequence or inverse folding (sequence design from structure).
-
- **Structure prediction (illustrative; requires the ESM3 model loaded above):**
-
- ```python
- from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig
-
- # Predict the missing structure track from a complete amino-acid sequence.
- protein = ESMProtein(sequence="MPRTKEINDAGLIVHSPQWFYK")
- protein_with_structure = model.generate(
- protein,
- GenerationConfig(track="structure", num_steps=8)
- )
-
- # Access predicted structure. Model inference has not been run for this example.
- coordinates = protein_with_structure.coordinates # 3D coordinates
- pdb_string = protein_with_structure.to_pdb_string()
- ```
-
- Use a positive step count for the track being generated: a complete input sequence
- has no `_` masks but still needs structure generation. `to_pdb(path)` writes a
- file; `to_pdb_string()` returns text. See the [upstream API](https://github.com/Biohub/esm/blob/main/esm/sdk/api.py).
-
- **Inverse folding (sequence from structure):**
-
- ```python
- # Design sequence for a target structure
- protein_with_structure = ESMProtein.from_pdb("target_structure.pdb")
- protein_with_structure.sequence = None # Remove sequence
-
- # Generate sequence that folds to this structure
- designed_protein = model.generate(
- protein_with_structure,
- GenerationConfig(track="sequence", num_steps=50, temperature=0.7)
- )
- ```
-
- ### 3. Protein Embeddings with ESM C
-
- Generate high-quality embeddings for downstream tasks like function prediction, classification, or similarity analysis.
+ # ESM: protein generation, embeddings, and folding
- **When to use:**
- - Extracting protein representations for machine learning
- - Computing sequence similarities
- - Feature extraction for protein classification
- - Transfer learning for protein-related tasks
+ ## When to use
- **Basic usage:**
+ Use for Biohub's `esm` SDK: ESM3 masked multimodal generation, ESMC sequence
+ representations, and ESMFold2 all-atom prediction. The former Forge platform
+ migrated to `https://biohub.ai`, including ESM3; SDK class names still contain
+ `Forge`. This skill targets the released **esm 3.4.1.post1**, verified against
+ its wheel and current official documentation.
- ```python
- from esm.models.esmc import ESMC
- from esm.sdk.api import ESMProtein, LogitsConfig
+ `fair-esm` is Meta's separate, older ESM2/ESMFold/ESM-IF distribution. Both
+ packages import as `esm`; install them in separate environments. The legacy
+ `esm.pretrained.esm2_*` interface is not the Biohub ESMC interface. Hugging Face
+ Transformers' native ESMC implementation is another API: do not interchange its
+ version requirements or output types with this SDK.
- # Load ESM C model
- model = ESMC.from_pretrained("esmc_300m").to("cuda")
+ ## Setup and model choice
- # Get embeddings
- protein = ESMProtein(sequence="MPRTKEINDAGLIVHSP...")
- protein_tensor = model.encode(protein)
- logits_output = model.logits(
- protein_tensor,
- LogitsConfig(sequence=True, return_embeddings=True),
- )
- embeddings = logits_output.embeddings
+ ```bash
+ uv venv --python 3.12 .venv-esm
+ uv pip install --python .venv-esm/bin/python "esm==3.4.1.post1"
```
- **Batch processing:**
+ The release declares Python >=3.12, Torch >=2.11,<2.12 and Transformers
+ >=4.57.6,<5. Python 3.12, Torch 2.11.0 and Transformers 4.57.6 were tested on
+ CPU. Linux x86_64 installs include GPU-specific dependencies; use a supported
+ inference machine and budget disk space. Flash Attention is optional; it is
+ unnecessary for the tiny CPU tests. Do not install this stack into a shared
+ environment containing incompatible Transformers or Torch pins.
- ```python
- # Encode multiple proteins
- proteins = [
- ESMProtein(sequence="MPRTKEIND..."),
- ESMProtein(sequence="AGLIVHSPQ..."),
- ESMProtein(sequence="KTEFLNDGR...")
- ]
+ | Task | Local model/API | Hosted client/model |
+ | --- | --- | --- |
+ | ESM3 sequence/structure/function generation | `ESM3.from_pretrained("esm3-sm-open-v1")` | `client("esm3-medium-2024-08")`; small/large IDs in the ESM3 reference |
+ | ESMC embeddings | `EsmcForMaskedLM` and `EsmcTokenizer`; `biohub/ESMC-300M`, `biohub/ESMC-600M`, `biohub/ESMC-6B` | `esmc_client("esmc-600m-2024-12")`; 300M/6B also documented |
+ | ESMFold2 all-atom structures | `EsmFold2Model`, `ESMFold2InputBuilder`; `biohub/ESMFold2` | `esmfold2_client("esmfold2-fast-2026-05")` |
- embeddings_list = [
- model.logits(
- model.encode(p),
- LogitsConfig(sequence=True, return_embeddings=True),
- ).embeddings
- for p in proteins
- ]
- ```
+ ESMC 6B weights are now available locally. Current Biohub model cards identify
+ MIT licensing, with ESMC cards also linking third-party notices; review the
+ exact selected artifact's card and access requirements.
+ The older `ESMC` class remains as a deprecated compatibility wrapper. Local
+ model sizes, hosted availability and account quotas are different constraints;
+ a larger model does not guarantee better performance on a particular assay.
- See `references/esm-c-api.md` for ESM C model details, efficiency comparisons, and advanced embedding strategies.
+ ## Workflow
- ### 4. Function Conditioning and Annotation
+ 1. Specify the sequence/chain/residue mapping and whether the objective is
+ generation, representation extraction or folding. Preserve input IDs and
+ source provenance. Reject accidental `...`, spaces, FASTA headers or gaps
+ in plain single-chain sequences rather than silently deleting them.
+ 2. Choose a local checkpoint or explicit hosted model ID. Record SDK, checkpoint
+ revision, model settings, mask locations and random seed where supported.
+ 3. Check every SDK result for `ESMProteinError`. Hosted failures may be returned
+ as values. Use finite request timeouts, context managers and bounded
+ concurrency. See [hosted contracts](references/forge-api.md).
+ 4. Validate the output contract: sequence length and fixed residues, residue-only
+ embedding axes, or chain/atom mapping and confidence. Keep model predictions
+ separate from experimental validation.
+ 5. Save sequences/structures with settings and identifiers. Avoid caches keyed
+ only by sequence when model, structure, function or generation settings differ.
- Use ESM3's function track to generate proteins with specific functional annotations or predict function from sequence.
+ ## ESM3 completion and fresh structure prediction
- **Function-conditioned generation:**
+ Illustrative pretrained inference; no weights or hosted jobs were run in this
+ refresh. This toy sequence demonstrates API mechanics, not a functional design.
```python
- from esm.sdk.api import ESMProtein, FunctionAnnotation, GenerationConfig
+ import torch
+ from esm.models.esm3 import ESM3
+ from esm.sdk.api import ESMProtein, ESMProteinError, GenerationConfig
- # Create protein with desired function
- protein = ESMProtein(
- sequence="_" * 200, # Generate 200 residue protein
- function_annotations=[
- FunctionAnnotation(label="fluorescent_protein", start=50, end=150)
- ]
+ model = ESM3.from_pretrained("esm3-sm-open-v1", device=torch.device("cpu"))
+ prompt = "MPRT___KEND"
+ completed = model.generate(
+ ESMProtein(sequence=prompt),
+ GenerationConfig(track="sequence", num_steps=3, temperature=0.7),
)
+ if isinstance(completed, ESMProteinError):
+ raise completed
+ assert completed.sequence is not None and len(completed.sequence) == len(prompt)
+ assert "_" not in completed.sequence
+ assert all(a == "_" or a == b for a, b in zip(prompt, completed.sequence))
- # Generate sequence with specified function
- functional_protein = model.generate(
- protein,
- GenerationConfig(track="sequence", num_steps=200)
+ # A fresh sequence-only prompt prevents old coordinates conditioning the check.
+ folded = model.generate(
+ ESMProtein(sequence=completed.sequence),
+ GenerationConfig(track="structure", num_steps=8),
)
+ if isinstance(folded, ESMProteinError):
+ raise folded
+ assert folded.coordinates is not None
+ folded.to_pdb("candidate.pdb")
```
- ### 5. Chain-of-Thought Generation
-
- Iteratively refine protein designs using ESM3's chain-of-thought generation approach.
-
- ```python
- from esm.sdk.api import GenerationConfig
-
- # Multi-step refinement
- protein = ESMProtein(sequence="MPRT" + "_" * 100 + "KEND")
-
- # Step 1: Generate initial structure
- config = GenerationConfig(track="structure", num_steps=50)
- protein = model.generate(protein, config)
-
- # Step 2: Refine sequence based on structure
- config = GenerationConfig(track="sequence", num_steps=50, temperature=0.5)
- protein = model.generate(protein, config)
-
- # Step 3: Predict function
- config = GenerationConfig(track="function", num_steps=20)
- protein = model.generate(protein, config)
- ```
+ Generation fills masked positions. Calling it again on a completed track does
+ not implement refinement or temperature annealing; explicitly remask selected
+ positions or clear the track. ESM3 structure generation and ESMFold2 prediction
+ use different models and result types. See [ESM3](references/esm3-api.md) for
+ inverse folding, function vocabulary and coordinate conventions.
- ### 6. Batch Processing with Forge API
+ ## ESMC embeddings with correct residue pooling
- Process multiple proteins efficiently using Forge's async methods.
+ Illustrative pretrained loading; the same API and pooling were executed with a
+ tiny randomly initialized model on CPU. Add this skill's `scripts/` directory to
+ `PYTHONPATH` when importing the bundled helper.
```python
- import os
- import asyncio
- import esm
- from esm.sdk.api import ESMProtein, GenerationConfig
-
- client = esm.sdk.client("esm3-medium-2024-08", token=os.environ["ESM_API_KEY"])
-
- # Async batch processing
- async def batch_generate(proteins_list):
- tasks = [
- client.async_generate(protein, GenerationConfig(track="sequence"))
- for protein in proteins_list
- ]
- return await asyncio.gather(*tasks)
-
- # Execute
- proteins = [ESMProtein(sequence=f"MPRT{'_' * 50}KEND") for _ in range(10)]
- results = asyncio.run(batch_generate(proteins))
- ```
-
- See `references/forge-api.md` for detailed Forge API documentation, authentication, rate limits, and batch processing patterns.
-
- ## Model Selection Guide
-
- **ESM3 Models (Generative):**
- - `esm3-open` (1.4B) - Open weights, local usage after accepting the Hugging Face license
- - `esm3-medium-2024-08` (7B) - Best balance of quality and speed (Forge only)
- - `esm3-large-2024-03` (98B) - Highest quality, slower (Forge only)
-
- **ESM C Models (Embeddings):**
- - `esmc_300m` / `esmc-300m-2024-12` (30 layers) - Lightweight, fast inference (open weights, local)
- - `esmc_600m` / `esmc-600m-2024-12` (36 layers) - Balanced performance (open weights, local)
- - `esmc-6b-2024-12` (80 layers) - Maximum quality (Forge API; local 6B weights require Forge or SageMaker)
-
- Local `ESMC.from_pretrained()` examples use underscore aliases (`esmc_300m`, `esmc_600m`). Hosted API clients use dated model IDs such as `esmc-600m-2024-12`.
-
- **Selection criteria:**
- - **Local development/testing:** Use `esm3-open` or `esmc_300m`
- - **Production quality:** Use `esm3-medium-2024-08` via Forge
- - **Maximum accuracy:** Use `esm3-large-2024-03` or `esmc-6b-2024-12` via Forge
- - **High throughput:** Use Forge or Biohub APIs with explicit async concurrency limits
- - **Cost optimization:** Use smaller models, implement caching strategies
-
- ## Installation
-
- The ESM3 and legacy ESMC examples target [`esm==3.2.3`](https://pypi.org/project/esm/3.2.3/) (Oct 14, 2025), not an assertion about the latest SDK. This pinned release requires **Python >=3.12,<3.13**. Match newer Biohub/ESMFold2 examples to their documented SDK release separately.
-
- **Basic installation:**
-
- ```bash
- uv pip install "esm==3.2.3"
- ```
-
- **With Flash Attention (recommended for faster inference on NVIDIA GPUs):**
+ from esm.models.esmc import EsmcForMaskedLM, EsmcTokenizer
+ from esm_embeddings import embed_sequences
- ```bash
- uv pip install "esm==3.2.3"
- uv pip install flash-attn --no-build-isolation
+ model = EsmcForMaskedLM.from_pretrained("biohub/ESMC-300M", device="cpu").eval()
+ tokenizer = EsmcTokenizer()
+ sequences = ["MPRTKEINDAGLIVHSPQWFYK", "ACDEFGHIK"]
+ features = embed_sequences(model, tokenizer, sequences)
+ assert features.shape == (2, 960)
```
- The Forge client ships with the `esm` package - no extra install for ESM3 or ESMC Forge inference.
+ `output.last_hidden_state` has shape `(B,T,D)` and includes CLS, EOS and padding;
+ `T` is not the raw residue count. [scripts/esm_embeddings.py](scripts/esm_embeddings.py)
+ performs one real padded batch, excludes special/padding tokens, validates the
+ residue count, and returns `(B,D)` CPU features in input order. Choose batch size
+ by sequence lengths and available memory. It does not truncate, download weights
+ or contact a service. See [ESMC](references/esm-c-api.md) for hosted output types,
+ per-residue extraction, gradient behavior and migration details.
- ## Authentication
+ ## Hosted authentication and folding
- Forge API access requires an API key. Never hardcode tokens in scripts or commit them to version control.
+ Read only the intended credential from the environment; pass it explicitly so
+ it is resolved when the client is created. SDK factory defaults capture
+ `ESM_API_KEY` at import time. Create keys in the
+ [Biohub developer console](https://biohub.ai/developer-console/api-keys).
- 1. Check whether `ESM_API_KEY` is already set in the environment.
- 2. If not, check a local `.env` for `ESM_API_KEY` only (do not load unrelated secrets).
- 3. If still missing, create a key in the [Biohub developer console](https://biohub.ai/developer-console/api-keys) for Biohub APIs or [Forge](https://forge.evolutionaryscale.ai) for legacy Forge-hosted ESM3/ESMC access.
+ Illustrative authenticated inference:
```python
import os
+ from esm.sdk import esmfold2_client
+ from esm.sdk.api import ESMProteinError, FoldingConfig
+ from esm.utils.structure.input_builder import ProteinInput, StructurePredictionInput
- token = os.environ["ESM_API_KEY"] # raises KeyError if unset
+ fold_input = StructurePredictionInput(
+ sequences=[ProteinInput(id="A", sequence="MPRTKEINDAGLIVHSPQWFYK")]
+ )
+ with esmfold2_client(
+ model="esmfold2-fast-2026-05", url="https://biohub.ai",
+ token=os.environ["ESM_API_KEY"], request_timeout=300,
+ ) as client:
+ result = client.fold_all_atom(fold_input, config=FoldingConfig())
+ if isinstance(result, ESMProteinError):
+ raise result
+ with open("candidate.cif", "w") as handle:
+ handle.write(result.complex.to_mmcif())
```
- `esm.sdk.client()` reads `ESM_API_KEY` automatically when `token` is omitted. Keep endpoint URLs fixed to trusted hosts such as `https://forge.evolutionaryscale.ai` or `https://biohub.ai`; do not take API hosts from untrusted user input.
-
- **Biohub platform:** EvolutionaryScale and Forge now surface current hosted models through [biohub.ai](https://biohub.ai). SDK class names may still reference "Forge". See `references/biohub-platform.md` for ESMFold2 and Biohub-specific setup.
-
- ## Common Workflows
-
- For detailed examples and complete workflows, see `references/workflows.md` which includes:
- - Novel GFP design with chain-of-thought
- - Protein variant generation and screening
- - Structure-based sequence optimization
- - Function prediction pipelines
- - Embedding-based clustering and analysis
-
- ## References
-
- This skill includes comprehensive reference documentation:
-
- - `references/esm3-api.md` - ESM3 model architecture, API reference, generation parameters, and multimodal prompting
- - `references/esm-c-api.md` - ESM C model details, embedding strategies, and performance optimization
- - `references/forge-api.md` - Forge platform documentation, authentication, batch processing, and deployment
- - `references/biohub-platform.md` - Biohub API migration, ESMFold2 structure prediction, and developer-console auth
- - `references/workflows.md` - Complete examples and common workflow patterns
-
- These references contain detailed API specifications, parameter descriptions, and advanced usage patterns. Load them as needed for specific tasks.
-
- ## Best Practices
-
- **For generation tasks:**
- - Start with smaller models for prototyping (`esm3-open`)
- - Use temperature parameter to control diversity (0.0 = deterministic, 1.0 = diverse)
- - Implement iterative refinement with chain-of-thought for complex designs
- - Validate generated sequences with structure prediction or wet-lab experiments
-
- **For embedding tasks:**
- - Batch process sequences when possible for efficiency
- - Cache embeddings for repeated analyses
- - Normalize embeddings when computing similarities
- - Use appropriate model size based on downstream task requirements
+ Use the [Biohub/ESMFold2 reference](references/biohub-platform.md) for MSA,
+ confidence and complex-input conventions. The fast hosted model ignores MSAs.
+ Public source and mocked requests validate the client contract; they do not
+ establish account access, service availability or prediction quality.
- **For production deployment:**
- - Use Forge API for scalability and latest models
- - Implement error handling and retry logic for API calls
- - Monitor token usage and implement rate limiting
- - Consider AWS SageMaker deployment for dedicated infrastructure
+ ## Scientific checks
- ## Resources and Documentation
+ - Function annotations use supported tokenizer labels and **1-based inclusive**
+ ranges; arbitrary labels such as `enzymatic_activity` are not valid prompts.
+ - ESM3 coordinates are tensors in atom37 layout; missing atoms use NaN. Tensor
+ copies use `.clone()`. Specify a PDB chain instead of assuming the default
+ selects one chain; the current default is `chain_id="all"`.
+ - Predicted coordinates, pLDDT and pTM do not measure thermodynamic stability,
+ binding affinity or catalytic activity. Inverse-folded sequences need fresh
+ prediction, matched-residue structural comparison and experimental screening.
+ - Embedding similarity is not a homology/function guarantee. Evaluate supervised
+ models with homology-aware splits; fit normalization and dimensionality
+ reduction on training data. Report uncertainty and independent holdout metrics.
+ - See [worked workflows](references/workflows.md) for variant libraries,
+ structure-conditioned design, and clustering without unsupported stability
+ scores or fixed PCA/t-SNE settings that fail on tiny datasets.
- - **GitHub Repository:** https://github.com/Biohub/esm (current ESMC/ESMFold2/Biohub docs; ESM3 docs remain linked from the repository)
- - **Forge Platform:** https://forge.evolutionaryscale.ai
- - **Biohub Platform:** https://biohub.ai
- - **Scientific Paper:** Hayes et al., Science (2025) - https://www.science.org/doi/10.1126/science.ads0018
- - **Blog Posts:**
- - ESM3 Release: https://www.evolutionaryscale.ai/blog/esm3-release
- - ESM C Launch: https://www.evolutionaryscale.ai/blog/esm-cambrian
- - **Community:** Slack community at https://bit.ly/3FKwcWd
- - **Model Weights:** Hugging Face EvolutionaryScale and Biohub organizations
+ ## Verification and sources
- ## Responsible Use
+ [references/review.md](references/review.md) records official sources, executed
+ CPU tests and limitations. Run `python tests/run_all.py --isolated esm` from the
+ repository to exercise synthetic local models, pooling, input serialization,
+ PDB round trips and mocked hosted contracts. Pretrained ESM3/ESMC/ESMFold2,
+ CUDA inference and authenticated services were not executed.
- ESM is designed for beneficial applications in protein engineering, drug discovery, and scientific research. Follow the Responsible Biodesign Framework (https://responsiblebiodesign.ai/) and Biohub Acceptable Use Policy (https://biohub.org/acceptable-use-policy/) when designing novel proteins. Consider biosafety and ethical implications of protein designs before experimental validation.
+ Follow the selected model's terms and the
+ [Biohub acceptable-use policy](https://biohub.org/acceptable-use-policy/).
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