How close can an open model get to AlphaFold3-level accuracy when it matches training data, model scale and inference budget? ByteDance has introduced Protenix-v1, a comprehensive AlphaFold3 (AF3) reproduction for biomolecular structure prediction, released with code and model parameters under Apache 2.0. The model targets AF3-level performance across protein, DNA, RNA and ligand structures while keeping the entire stack open and extensible for research and production.
The core release also ships with PXMeter v1.0.0, an evaluation toolkit and dataset suite for transparent benchmarking on more than 6k complexes with time-split and domain-specific subsets.
What is Protenix-v1?
Protenix is described as ‘Protenix: Protein + X‘, a foundation model for high-accuracy biomolecular structure prediction. It predicts all-atom 3D structures for complexes that can include:
- Proteins
- Nucleic acids (DNA and RNA)
- Small-molecule ligands
The research team defines Protenix as a comprehensive AF3 reproduction. It re-implements the AF3-style diffusion architecture for all-atom complexes and exposes it in a trainable PyTorch codebase.
The project is released as a full stack:
- Training and inference code
- Pre-trained model weights
- Data and MSA pipelines
- A browser-based Protenix Web Server for interactive use
AF3-level performance under matched constraints
As per the research team Protenix-v1 (protenix_base_default_v1.0.0) is ‘the first fully open-source model that outperforms AlphaFold3 across diverse benchmark sets while adhering to the same training data cutoff, model scale, and inference budget as AlphaFold3.‘
The important constraints are:
- Training data cutoff: 2021-09-30, aligned with AF3’s PDB cutoff.
- Model scale: Protenix-v1 itself has 368M parameters; AF3 scale is matched but not disclosed.
- Inference budget: comparisons use similar sampling budgets and runtime constraints.
On challenging targets such as antigen–antibody complexes, increasing the number of sampled candidates from several to hundreds yields consistent log-linear improvements in accuracy. This gives a clear and documented inference-time scaling behavior rather than a single fixed operating point.
PXMeter v1.0.0: Evaluation for 6k+ complexes
To support these claims, the research team released PXMeter v1.0.0, an open-source toolkit for reproducible structure prediction benchmarks.
PXMeter provides:
- A manually curated benchmark dataset, with non-biological artifacts and problematic entries removed
- Time-split and domain-specific subsets (for example, antibody–antigen, protein–RNA, ligand complexes)
- A unified evaluation framework that computes metrics such as complex LDDT and DockQ across models
The associated PXMeter research paper, ‘Revisiting Structure Prediction Benchmarks with PXMeter,‘ evaluates Protenix, AlphaFold3, Boltz-1 and Chai-1 on the same curated tasks, and shows how different dataset designs affect model ranking and perceived performance.
How Protenix fits into the broader stack?
Protenix is part of a small ecosystem of related projects:
- PXDesign: a binder design suite built on the Protenix foundation model. It reports 20–73% experimental hit rates and 2–6× higher success than methods such as AlphaProteo and RFdiffusion, and is accessible via the Protenix Server.
- Protenix-Dock: a classical protein–ligand docking framework that uses empirical scoring functions rather than deep nets, tuned for rigid docking tasks.
- Protenix-Mini and follow-on work such as Protenix-Mini+: lightweight variants that reduce inference cost using architectural compression and few-step diffusion samplers, while keeping accuracy within a few percent of the full model on standard benchmarks.
Together, these components cover structure prediction, docking, and design, and share interfaces and formats, which simplifies integration into downstream pipelines.
Key Takeaways
- AF3-class, fully open model: Protenix-v1 is an AF3-style all-atom biomolecular structure predictor with open code and weights under Apache 2.0, targeting proteins, DNA, RNA and ligands.
- Strict AF3 alignment for fair comparison: Protenix-v1 matches AlphaFold3 on critical axes: training data cutoff (2021-09-30), model scale class and comparable inference budget, enabling fair AF3-level performance claims.
- Transparent benchmarking with PXMeter v1.0.0: PXMeter provides a curated benchmark suite over 6k+ complexes with time-split and domain-specific subsets plus unified metrics (for example, complex LDDT, DockQ) for reproducible evaluation.
- Verified inference-time scaling behavior: Protenix-v1 shows log-linear accuracy gains as the number of sampled candidates increases, giving a documented latency–accuracy trade-off rather than a single fixed operating point.
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