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AlphaFold 3

By Google DeepMind →

Predict biomolecular structures

A structure-prediction model for biomolecular complexes. The inference code and model parameters have separate access and usage terms.

Biomolecular structure predictionLocal inference pipeline
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Overview

A structure-prediction model for biomolecular complexes. The inference code and model parameters have separate access and usage terms.

Information checked against an official source; not a hands-on test. Source · Last reviewed: 13/09/2026, 18:02:53

Key Features

  • Biomolecular structure prediction
  • Local inference pipeline
Interactive 3D Structure

AlphaFold 3 Predicted Complex

Streams real 3D atomic coordinates from RCSB Protein Data Bank
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pLDDT / B-Factor:
>90 Very high 70-90 Confident 50-70 Low <50 Very low

Academic Context & Research Evidence

Biological & Workflow Fit

Research Workflow
Predict biomolecular structures
Compute & Hardware
H100 80 GB; CPU/RAM and sequence databases are also required. Larger inputs can require configuration changes.
Licensing & Academic Use
Code and model parameters have separate terms. Parameter access must be obtained from the provider; commercial eligibility is not established here.
Documented Evidence
View validation publication / source ↗

Cite this Tool

Use this citation format when referencing AlphaFold 3 in scientific publications and benchmark papers.

@software{alphafold_3_2026,
  title = {{AlphaFold 3}},
  author = {{Google DeepMind}},
  year = {2026},
  url = {https://github.com/google-deepmind/alphafold3},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations streamed from Europe PMC and PubMed for AlphaFold 3.

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Technical / Product Information

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeSoftware / platform
Access modeProvider-specific terms
AI roleAI / computational workflow
Input dataNot recorded
Output dataNot recorded
Licence conditionsCode and model parameters have separate terms. Parameter access must be obtained from the provider; commercial eligibility is not established here.
Commercial eligibilityNot independently confirmed
Compute requirementsH100 80 GB; CPU/RAM and sequence databases are also required. Larger inputs can require configuration changes.
ValidationNot recorded
TypeStructure prediction model
Intended useNot recorded
CompatibilityNot recorded
Access ⓘSee code licence and model parameter terms
Research workflowPredict biomolecular structures
Evidence levelProvider-tested configuration
Integration evidencehttps://github.com/google-deepmind/alphafold3/blob/main/docs/performance.md
Laboratory handoffEditorial next step: select an appropriate structural or functional validation assay with the research team. No direct instrument integration has been verified.
AvailabilityCheck provider access requirements
Price / accessSee provider terms

Research fit & compatibility

Predict biomolecular structures →

Provider-tested configuration · AlphaFold 3 documentation lists a single H100 80 GB as an officially supported inference configuration. Other H100 variants are not established by this evidence.

Compute
H100 80 GB; CPU/RAM and sequence databases are also required. Larger inputs can require configuration changes.
Licence & commercial use
Code and model parameters have separate terms. Parameter access must be obtained from the provider; commercial eligibility is not established here.
Evidence limitations
Hardware support is not evidence of accuracy for a particular biological target. No local benchmark was run.
Read supporting evidence ↗

Gallery & resources

No licensed product photograph recorded for AlphaFold 3 yet. Visit the official resource below for manufacturer imagery.

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FAQ

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Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating AlphaFold 3.

5.0
★★★★★Based on 0 researcher evaluations
Biological Accuracy
4.8/5
Ease of Installation
4.3/5
Documentation & Code
4.6/5