OpenFold3 (AlphaFold3)
Open-source AlphaFold3 reproduction achieving near-parity accuracy across biomolecular modalities.
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OpenFold3 (OF3) is an open-source, bitwise reproduction of AlphaFold3 that delivers near-parity performance for comprehensive biomolecular structure prediction across proteins, nucleic acids, and protein–ligand complexes. Developed by a consortium including Columbia University, Lawrence Livermore National Laboratory, Novo Nordisk, AWS, Seoul National University, the Chan Zuckerberg Initiative, Absci, SandboxAQ, NVIDIA, and others, OF3 replicates the diffusion-based multimodal architecture of AlphaFold3 while incorporating stability and correctness improvements. Key refinements include updated LayerNorm handling, corrected inter-chain masking logic, revised distance binning, and updated sequence and structure databases. Benchmarks across CASP16, FoldBench, and Runs N’ Poses demonstrate performance within experimental error of AlphaFold3 on RNA and monomeric proteins, and competitive results on multimeric and ligand-bound complexes. OpenFold3 is fully open-source with inference and training code, model weights, and documentation to support community-driven research in structural biology and drug discovery.
Estimated resource cost: 0.001178
Categories: Structure Prediction & Folding
Tags: Co-Folding Conformation Generation Nucleotides Protein Folding Proteins Protein–Ligand Docking Protein–Protein Docking Small Molecules
Key Capabilities
- Implements AlphaFold3-compatible diffusion-based architecture for multimodal structure prediction.
- Supports proteins, RNA, and small-molecule complexes through unified pair representations.
- Improved normalization, masking, and binning logic for stable and accurate training.
- Benchmarked on CASP16, FoldBench, and Runs N’ Poses datasets, matching or approaching AF3 performance.
Runtime Statistics
| Metric | Value |
|---|---|
| runtime_mean | 650 |
| runtime_median | 498 |
| runtime_std | 1248 |
| runtime_90th_percentile | 977 |
| runtime_max | 17730 |
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Estimated Credits: 0.001178
Invite-only, limited-time access. Please contact ztang@getantibody.com.
Invite-only, limited-time access. Please contact ztang@getantibody.com.