Structure Prediction Comparison
AlphaFold3 vs Boltz-1 vs Chai-1 vs Protenix: AF3-Class Shootout (2026)
Last updated: 2026-04-17
AlphaFold3 (DeepMind, May 2024) set a new bar for biomolecular complex prediction — proteins, ligands, nucleic acids, ions, all in one shot. But its restrictive license and server-only access sparked a race to reproduce it. Within months, three credible open-source alternatives emerged: Boltz-1 (MIT, MIT license), Chai-1 (Chai Discovery, Apache 2.0), and Protenix (ByteDance, Apache 2.0). The FoldBench assessment and multiple independent benchmarks have now compared these head-to-head. Here's where they actually stand.
AlphaFold3
Google DeepMind / Isomorphic Labs
Boltz-1
MIT Jameel Clinic
Chai-1
Chai Discovery
Protenix
ByteDance Research
Head-to-Head
Structured comparison across key dimensions.
| Dimension | AlphaFold3 | Boltz-1 | Chai-1 | Protenix |
|---|---|---|---|---|
| Developer | Google DeepMind | MIT (Generating Biomolecular Structure lab) | Chai Discovery | ByteDance Research |
| Architecture | Diffusion-based; Pairformer + diffusion module; full MSA processing | AF3-inspired diffusion; clean reimplementation with optional MSA | AF3-inspired diffusion; strong single-sequence mode; trunk confidence heads | Faithful AF3 reproduction; Pairformer + diffusion; full training pipeline |
| Protein-ligand accuracy | Best — highest success rate on FoldBench protein-ligand targets | Competitive — within ~5% of AF3 on most benchmarks; strong on CrossDocked | Good — slightly behind AF3/Boltz-1 on protein-ligand; better on protein-protein | Good — approaching AF3 accuracy; some variance on peptide complexes |
| Protein-peptide accuracy | Strong — best overall on peptide benchmarks (bioRxiv 2025) | Lower — weaker on short peptide complexes in head-to-head (TM-score 0.40 on some targets) | Strong — comparable to AF3; benefits from MSA (78.8% vs 70.7% without) | Moderate — approaching AF3 but with more variance across targets |
| MSA required? | Yes — uses MSA by default; no single-sequence mode | Optional — works with or without MSA | Optional — single-sequence mode works well; MSA improves accuracy ~8% | Optional — supports MSA and MSA-free inference |
| Training code available? | No — inference only via AlphaFold Server | No — inference weights released; training code not public | No — inference weights released; training code not public | Yes — full training pipeline released; can retrain from scratch |
| License | Restricted — server-only; no commercial use; no code modification | MIT — fully open, commercial use allowed | Apache 2.0 — fully open, commercial use allowed | Apache 2.0 — fully open, commercial use allowed; includes training code |
| Local installation | No — AlphaFold Server only (Google-hosted) | Yes — pip install; runs on single GPU | Yes — pip install; runs on single GPU; commercial API also available | Yes — pip install; runs on single GPU; supports multi-GPU training |
| Code quality | N/A — code not released | Excellent — clean, minimal, well-documented; easiest to hack | Good — well-structured; commercial-grade API | Good — comprehensive; heavier codebase reflecting full AF3 reproduction |
| Key limitation | No local install; no commercial rights; no code access; server rate limits | Weaker on peptide complexes; no training code; smaller development team | Slightly behind on protein-ligand accuracy; no training code | Newer — less community adoption; accuracy still converging on some targets |
When to Use Each
AlphaFold3
You need the highest accuracy on protein-ligand and protein-nucleic acid complexes. You're fine using the AlphaFold Server (no local installation). You don't need commercial use rights or code modification.
Boltz-1
You need a commercially usable (MIT license) AF3-class model. You want clean, hackable code for integration into drug discovery pipelines. You value MSA-optional prediction with competitive accuracy.
Chai-1
You need strong single-sequence (no MSA) prediction for high-throughput screening. You're modeling protein-protein or antibody-antigen complexes. You want Apache 2.0 licensing with a commercial API option.
Protenix
You want the most complete AF3 reproduction including full training code. You need to retrain or fine-tune on custom data. You want ByteDance's engineering quality with Apache 2.0 licensing.
Practitioner Verdict
AlphaFold3 remains the accuracy leader on most benchmarks, but the gap is small and shrinking. Boltz-1 is the most hackable open-source option with MIT license and clean code — best for integration into pipelines. Chai-1 excels at MSA-free prediction and multimer interfaces. Protenix is the most faithful AF3 reproduction with full training code — best for researchers who want to retrain or modify the architecture.
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