Instructions to use Aurigene-AI/esmfold_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aurigene-AI/esmfold_v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, EsmForProteinFolding tokenizer = AutoTokenizer.from_pretrained("Aurigene-AI/esmfold_v1") model = EsmForProteinFolding.from_pretrained("Aurigene-AI/esmfold_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Mirrored by Aurigene AI
Discovery stage: Target identification
End-to-end protein structure prediction from a single sequence, with no MSA step. Folds orphan sequences and designed constructs in seconds.
Upstream:
facebook/esmfold_v1- all credit to the original authors; the model card and licence below are theirs.Explore the rest of the catalogue: Molecule Explorer - Protein Target Explorer - Drug Discovery Model Hub
ESMFold
ESMFold is a state-of-the-art end-to-end protein folding model based on an ESM-2 backbone. It does not require any lookup or MSA step, and therefore does not require any external databases to be present in order to make predictions. As a result, inference time is very significantly faster than AlphaFold2. For details on the model architecture and training, please refer to the accompanying paper.
If you're interested in using ESMFold in practice, please check out the associated tutorial notebook.
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