Structure Prediction Comparison
AlphaFold2 vs Boltz-1 vs Chai-1: Structure Prediction Compared (2026)
Last updated: 2026-04-16
The protein structure prediction landscape shifted dramatically in 2025-2026. AlphaFold2 remains the production workhorse for single-chain prediction, but the 'AF3 generation' — models that jointly predict protein-ligand-nucleic acid complexes — has arrived. Boltz-1 and Chai-1 are the two leading open-source, commercially usable alternatives to the restricted AlphaFold3. Here's how they compare for drug discovery use cases.
Head-to-Head
Structured comparison across key dimensions.
| Dimension | AlphaFold2 | Boltz-1 | Chai-1 |
|---|---|---|---|
| Architecture | MSA-based, Evoformer + Structure Module | Diffusion-based (AF3-class) | Diffusion-based (AF3-class) |
| Predicts ligands? | No — protein only | Yes — protein + small molecule + nucleic acid | Yes — protein + small molecule + nucleic acid + glycosylations |
| Predicts affinity? | No | No (use Boltz-2 for affinity) | No |
| Requires MSA? | Yes (via ColabFold or full DB search) | Optional (supports MSA but works without) | No — works well in single-sequence mode |
| License | Apache 2.0 (fully commercial) | MIT (fully commercial) | Apache 2.0 (fully commercial) |
| On Platform | Yes | Yes | Yes |
| Maturity | Production — 214M+ structures in database, thousands of citations | Research+ — rapidly adopted, MIT-backed, FoldBench validated | Research+ — strong benchmarks, commercial API available |
| Speed | Minutes per structure (with ColabFold) | Minutes per complex | Minutes per complex (faster without MSA) |
| Best accuracy on | Single-chain proteins, especially well-represented families | Protein-ligand complexes (competitive with AF3) | Multimers in single-sequence mode; protein-protein interfaces |
| Key limitation | Cannot model ligands or nucleic acids | No template support; training data cutoff 2021 | Not consistently superior to AF3 across all tasks |
When to Use Each
AlphaFold2
You need reliable single-chain or multimer protein structure prediction. You want the most validated tool with the largest database of pre-computed structures. You don't need ligand co-folding.
Boltz-1
You need to predict protein-ligand, protein-nucleic acid, or multi-component complexes. You need a commercially usable license (MIT). You want AF3-level accuracy without the AF3 restrictions.
Chai-1
You need co-folding without running MSA (faster for high-throughput). You want Apache 2.0 licensing. You're modeling antibody-antigen or protein-protein complexes where single-sequence mode works well.
Practitioner Verdict
Use AlphaFold2/ColabFold for routine single-chain structure prediction — it's battle-tested and fast. Use Boltz-1 when you need protein-ligand co-folding with full commercial rights (MIT license). Use Chai-1 when you need co-folding without MSA (single-sequence mode works well for multimers). If you need binding affinity prediction too, look at Boltz-2 instead.
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