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Evidence Comparison

AlphaFold 3 vs Boltz-2: Evidence Comparison

When does an open structure-and-affinity workflow change the choice between AlphaFold 3 and Boltz-2?

BioAtlas does not create a universal winner. The useful choice depends on scientific task, inputs, access, evaluation protocol, context of use, uncertainty and required validation.

Structure Prediction

AlphaFold 2 / 3

The model that solved the 50-year protein-folding problem.

OrganizationGoogle DeepMind
AccessLimited open access
DeploymentHybrid
Evidence coverage4/7 evidence fields documented
BenchmarkCASP14 / complex evaluations
Metric / taskStructure accuracy and confidence

Inputs

Biomolecular sequences · Ligand / ion identities · Optional templates and MSA depending on release

Outputs

3D biomolecular structures · Confidence estimates

Known limitations

  • Performance depends on the evaluation dataset and operating conditions.
  • Task-specific benchmark results should not be compared across unlike domains.
  • Outputs require task-specific scientific and experimental validation.
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Structure Prediction

Boltz-1 / Boltz-2

Open-source AF3-quality structure — plus binding affinity.

OrganizationMIT (Barzilay & Jaakkola labs)
AccessOpen source
DeploymentSelf-hosted
Evidence coverage4/7 evidence fields documented
BenchmarkPoseBusters and affinity benchmarks
Metric / taskStructure and affinity

Inputs

Biomolecular complex specification

Outputs

Complex structures · Binding-affinity predictions

Known limitations

  • Performance depends on the evaluation dataset and operating conditions.
  • Task-specific benchmark results should not be compared across unlike domains.
  • Outputs require task-specific scientific and experimental validation.
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Benchmark comparability · none

Not directly comparable

The claims evaluate different scientific tasks.

A direct comparison requires aligned task, dataset, metric and split/protocol context. Otherwise BioAtlas treats the evidence as partial or contextual rather than manufacturing a winner.

Decision rules

What should determine the choice?

Scientific task

Confirm that both systems are being evaluated for the same task. Structure, pose, affinity and design claims are not interchangeable.

Protocol comparability

Only compare benchmark results when dataset, split, metric and implementation conditions align closely enough to support the comparison.

Access and reproducibility

Code, weights, API access, commercial terms and deployment constraints can materially change whether a model is usable in a governed programme.

Validation plan

Use the comparison to design the next validation step—not as a substitute for prospective evaluation on your own scientific problem.

Why?

Why might one model be chosen over the other?

Why this model?

Choose the model whose task, access constraints, evidence and deployment fit the actual scientific decision—not the one with the most impressive headline metric.

Why not the alternative?

A model can be scientifically strong yet inappropriate when its benchmark context, licensing, inputs, reproducibility or validation burden does not match your programme.

What evidence is missing?

If benchmark comparability is partial or contextual, the next step should be a matched evaluation on the same data, protocol and decision-relevant endpoint.