AlphaFold3
Google DeepMind / Isomorphic Labs
Joint structure prediction of proteins, DNA, RNA, small molecules, ions, and covalent modifications in a single diffusion-based model.
Best For
Highest accuracy co-folding, but restricted to non-commercial use
License
Non-commercial only
Strengths
- +Best accuracy across most tasks
- +Protein + ligand + nucleic acid + ions
Limitations
- −Non-commercial license only
- −No public commercial API
- −2,500-residue limit
- −Cannot rank binding affinities
R&D Pipeline Coverage
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Boltz-1
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First fully open-source model achieving AlphaFold3-level accuracy for joint structure prediction of proteins, nucleic acids, and small molecules.
Chai-1
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Multi-modal foundation model for joint structure prediction of proteins, small molecules, DNA, RNA, and glycosylations. Performs well in single-sequence mode.
Protenix
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Fully open-source PyTorch reproduction of AlphaFold3 architecture. Protenix-v1 (Feb 2026) reported to outperform AF3 across diverse benchmarks.
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AlphaFold2
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Predicts single-chain and multimer protein 3D structures from amino acid sequence using MSA-based deep learning. Set the modern benchmark on CASP14.
ColabFold
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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.
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