AlphaFold Protein Structure Database
webAlphaFold is frequently cited in AI safety contexts as a prominent example of transformative AI capability emerging rapidly; relevant to discussions of capability jumps, beneficial AI, and the dual-use nature of advanced AI systems.
Metadata
Importance: 42/100tool pagetool
Summary
AlphaFold DB, developed by Google DeepMind and EMBL-EBI, provides open access to over 200 million AI-predicted protein 3D structures derived from amino acid sequences. It represents a landmark achievement in AI applied to scientific discovery, achieving accuracy competitive with experimental methods. The database covers nearly the entire UniProt protein sequence repository and is freely available to the global research community.
Key Points
- •Contains over 200 million protein structure predictions, covering broad swaths of the known protein universe via UniProt integration.
- •AlphaFold AI system achieves structure prediction accuracy competitive with experimental methods like X-ray crystallography.
- •Ranked top in CASP14 (the critical assessment of protein structure prediction) by a large margin, validating its scientific impact.
- •Demonstrates transformative real-world capability of deep learning, relevant to AI safety discussions about rapid capability jumps.
- •Open-access database and open-source code represent a case study in responsible deployment of powerful AI systems in science.
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| Scientific Research Capabilities | Capability | 68.0 |
| Google DeepMind | Organization | 37.0 |
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AlphaFold DB provides open access to over 200 million protein structure predictions to accelerate scientific research.
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