Transformers
Safetensors
English
esmfold2
biology
esm
protein
protein-structure-prediction
structure-prediction
protein-design
3d-structure
confidence-estimation
molecular-dynamics
Instructions to use biohub/ESMFold2-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMFold2-Fast with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("biohub/ESMFold2-Fast") model = AutoModel.from_pretrained("biohub/ESMFold2-Fast", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Model Details
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ESMCFold is a state-of-the-art protein structure prediction model that combines ESMC (6B parameter) language model representations with a diffusion-based structure prediction architecture inspired by AlphaFold3.
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ESMCFold-Fast is an Inference optimized single-sequence structure prediction model and is not MSA conditioned.
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## Model Details
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ESMCFold is a state-of-the-art protein structure prediction model that combines ESMC (6B parameter) language model representations with a diffusion-based structure prediction architecture inspired by AlphaFold3.
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The model predicts high-resolution, all-atom 3D protein structures directly from amino acid sequences, with optional multiple sequence alignment (MSA) input for enhanced accuracy on challenging targets. The model outputs comprehensive structural information including all-atom coordinates (backbone and side chains), confidence metrics (pLDDT, pAE, pTM, iPTM), and optional distogram predictions for detailed analysis of predicted structures. Unlike ESMFold, ESMCFold is able to predict structures for all biomolecules, including small molecules, DNA, RNA, and modified amino acids.
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ESMCFold-Fast is an Inference optimized single-sequence structure prediction model and is not MSA conditioned.
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