Structure Prediction Comparison
Boltz-2 vs AlphaFold3 vs Chai-1: Next-Gen Structure + Affinity Prediction (2026)
Last updated: 2026-04-16
AlphaFold3 set the bar for biomolecular complex prediction in 2024, but its restrictive license and closed weights left a gap. Boltz-2 (MIT, June 2025) fills it — matching AF3 on structure while adding binding affinity prediction that rivals physics-based FEP methods at 1000× the speed. Chai-1 offers a strong open-source alternative with unique single-sequence capabilities. Here's how the three compare for drug discovery in 2026.
Boltz-2
MIT + Recursion Pharmaceuticals
AlphaFold3
Google DeepMind / Isomorphic Labs
Chai-1
Chai Discovery
Head-to-Head
Structured comparison across key dimensions.
| Dimension | Boltz-2 | AlphaFold3 | Chai-1 |
|---|---|---|---|
| Architecture | Diffusion-based (AF3-class) + affinity confidence head | Diffusion-based with Pairformer trunk | Diffusion-based (AF3-class) |
| Predicts binding affinity? | Yes — Pearson r=0.62 on FEP+ benchmark (comparable to OpenFE) | No | No |
| Co-folding accuracy | Competitive with AF3; slightly behind on antibody-antigen complexes | Best overall, especially on protein-protein and antibody-antigen | Strong on multimers in single-sequence mode; slightly behind AF3 overall |
| Speed | Affinity prediction 1000× faster than physics-based FEP | Minutes per complex (AlphaFold Server has queue times) | Fast — no MSA search needed in single-sequence mode |
| Requires MSA? | Optional (supports MSA but works without) | Yes (server runs MSA automatically) | No — single-sequence mode works well |
| Open weights? | Yes — fully open (MIT license) | No — server access only; code released but weights restricted | Yes — fully open (Apache 2.0) |
| License | MIT (fully commercial) | Restricted academic license; commercial use requires agreement | Apache 2.0 (fully commercial) |
| CASP16 performance | Outperformed all entries on CASP16 affinity challenge (retrospective) | Top performer on CASP15/16 structure prediction | Competitive but not top on CASP16 structure tasks |
| On Platform | Yes | Via AlphaFold Server | Yes |
| Key limitation | Lags AF3 on antibody-antigen; affinity limited to relative ranking (not absolute ΔG) | Closed weights; restricted license; no affinity prediction | No affinity prediction; not consistently superior to AF3 on any task class |
When to Use Each
Boltz-2
You need structure prediction + binding affinity in one model. You want open weights with MIT license for commercial use. You need FEP-competitive affinity ranking at 1000× the speed.
AlphaFold3
You need the highest structural accuracy, especially for antibody-antigen complexes. You're in academia or can use the AlphaFold Server. You don't need binding affinity prediction.
Chai-1
You need fast co-folding without MSA (single-sequence mode). You want Apache 2.0 licensing. You're running high-throughput multimer prediction where speed matters more than peak accuracy.
Practitioner Verdict
Use AlphaFold3 when you need the highest co-folding accuracy and can work within its academic license (or use the AlphaFold Server). Use Boltz-2 when you need both structure prediction AND binding affinity in one model with full commercial rights — it's the only open tool that does both. Use Chai-1 for fast co-folding without MSA and when you want Apache 2.0 licensing.
Stay updated on these tools
Weekly briefing on AI tool releases, benchmarks, and what works in drug discovery.