This model was contributed to Hugging Face Transformers on 2026-08-19.

ESMFold2

Overview

ESMFold2 is an all-atom protein structure prediction model. It predicts 3D coordinates and per-residue confidence (pLDDT, PAE, PDE) directly from an amino-acid sequence, using the ESMC protein language model as its backbone. The architecture combines a sliding-window atom encoder with 3D rotary position embeddings, a pairwise folding trunk applied iteratively, a diffusion-based structure head, and a confidence head.

The model checkpoint is available on the Hugging Face Hub at biohub/ESMFold2-hf.

Usage example

import torch

from transformers import EsmFold2Model

# The ESMC backbone is bundled in the checkpoint and loaded with the model.
# bf16 is the recommended inference precision.
model = EsmFold2Model.from_pretrained("biohub/ESMFold2-hf", dtype=torch.bfloat16, device_map="auto")

pdb_string = model.infer_protein_as_pdb("MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQ")
print(pdb_string)

infer_protein returns the raw outputs (atom coordinates, distogram logits and confidence metrics) as an [~models.esmfold2.modeling_esmfold2.EsmFold2Output] if you need them instead of a PDB string. You may get slightly different predictions if you run the same sequence multiple times. Set a manual seed if you want exactly reproducible structures.

ESMFold2 draws config.structure_head.num_diffusion_samples structures per fold. infer_protein_as_pdb renders the best-ranked one (highest pTM); pass sample_idx to pick a specific sample instead. The PDB carries per-residue pLDDT in the b-factor column, on the same 0-1 scale as the plddt output.

forward vs fold

A structure prediction has two halves. EsmFold2Model.forward is the first: it runs the folding trunk over the featurized inputs and returns the refined pair representation plus the distogram, as an [~models.esmfold2.modeling_esmfold2.EsmFold2TrunkOutput]. It does not produce 3D coordinates — ESMFold2 gets those by iterative denoising, and that sampling loop (the noise schedule, Kabsch alignment and the ODE/SDE update) lives in EsmFold2FoldingMixin along with the confidence head call:

Call fold or infer_protein for an actual structure. Reach for forward when you only need the distogram, or when you want to drive the diffusion sampler yourself: fold calls forward once and then hands its output to EsmFold2DiffusionModule, whose own forward is the single denoising step.

Faster inference with a fused kernel

The folding trunk's dominant cost is the triangle-multiplication update. Passing use_kernels=True to [~PreTrainedModel.from_pretrained] swaps it for a fused Triton kernel loaded from the Hub via the kernels library, leaving the prediction unchanged. It is inference-only and CUDA-only; on CPU or without the kernel installed the model transparently falls back to the pure-PyTorch implementation. Make sure the model is on a CUDA device when kernelization happens (e.g. with device_map).

import torch

from transformers import EsmFold2Model

model = EsmFold2Model.from_pretrained(
    "biohub/ESMFold2-hf", dtype=torch.bfloat16, device_map="cuda", use_kernels=True
)

pdb_string = model.infer_protein_as_pdb("MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQ")

EsmFold2Config

[[autodoc]] EsmFold2Config

EsmFold2PreTrainedModel

[[autodoc]] EsmFold2PreTrainedModel

EsmFold2Model

[[autodoc]] EsmFold2Model - forward - fold - infer_protein - infer_protein_as_pdb

EsmFold2Output

[[autodoc]] models.esmfold2.modeling_esmfold2.EsmFold2Output

EsmFold2TrunkOutput

[[autodoc]] models.esmfold2.modeling_esmfold2.EsmFold2TrunkOutput

EsmFold2AtomInputs

[[autodoc]] models.esmfold2.modeling_esmfold2.EsmFold2AtomInputs