ESMFold
Meta AI (FAIR)
Single-sequence protein structure prediction using the ESM-2 protein language model (15B parameters). No MSA required — fast inference directly from sequence.
Best For
Rapid screening of large sequence libraries where speed outweighs accuracy
License
Open Source (MIT)
Strengths
- +No MSA needed
- +Very fast inference
- +MIT license
Limitations
- −Lower accuracy than AF2
- −Protein-only, no ligands
- −Known to hallucinate incorrect folds
R&D Pipeline Coverage
Related Tools
AlphaFold2
Google DeepMind
Predicts single-chain and multimer protein 3D structures from amino acid sequence using MSA-based deep learning. Set the modern benchmark on CASP14.
OmegaFold
HeliXon Protein
Single-sequence structure prediction using a protein language model plus geometry-inspired transformer. First MSA-free method to approach AF2 accuracy.
More in Structure Prediction
AlphaFold2
Google DeepMind
Predicts single-chain and multimer protein 3D structures from amino acid sequence using MSA-based deep learning. Set the modern benchmark on CASP14.
ColabFold
Steinegger Lab (Seoul National University)
Wraps AlphaFold2 with MMseqs2-based MSA generation, making AF2 runs 40-60x faster. Accessible via Google Colab or local install.
Boltz-1
MIT Jameel Clinic
First fully open-source model achieving AlphaFold3-level accuracy for joint structure prediction of proteins, nucleic acids, and small molecules.
Stay updated on ESMFold
Weekly newsletter covering AI tool releases, benchmarks, and what practitioners actually use.