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v2.0.0_ Protenix-v2 Model Released source code.zip (60.1 MB)
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| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| README.md | 2026-04-07 | 3.4 kB | |
| v2.0.0_ Protenix-v2 Model Released source code.tar.gz | 2026-04-07 | 60.0 MB | |
| v2.0.0_ Protenix-v2 Model Released source code.zip | 2026-04-07 | 60.1 MB | |
| Totals: 3 Items | 120.0 MB | 6 | |
## What's changed
🚀 Key Highlights
• Protenix-v2 Model Released: Introduced protenix-v2, an enhanced-capacity model (464M parameters). It delivers significant accuracy improvements in predicting challenging antibody-antigen complex structures and updates ligand-related plausibility.
• Training-Free Guidance (TFG) Module: Introduced a powerful new guidance module enforcing geometric and physical constraints (Steric, Torsion, Bond, etc.) during diffusion sampling without the need for retraining.
✨ New Features & Enhancements
• Inference Efficiency Breakthrough: protenix-v2 shows remarkable efficiency gains. Utilizing only 5 sampling seeds, it successfully
outperforms protenix-v1 at 1000 seeds.
• Configurable TFG Capabilities: Exposed via the --use_tfg_guidance CLI flag. Supported geometries include VinaStericPotential,
ExperimentalTorsionPotential, and PairwiseDistancePotential.
📖 Documentation & Assets
• Bumped protenix version to 2.0.0.
• Published the new Protenix-v2 Technical Report (docs/PX2.pdf).
• Updated README.md and docs/supported_models.md with the latest Protenix-v2 benchmarks, showcasing a 9 to 13 percentage points absolute success rate gain over Protenix-v1 at the DockQ > 0.23 threshold.
