AlphaFold (v1) tops the CASP13 protein-structure prediction assessment
Result confirmed
Importance: major (4 of 5)The takeaway
DeepMind’s first AlphaFold ranked first in the CASP13 blind assessment of protein structure prediction, an early sign that deep learning could crack the protein folding problem.
Status
- Claim
Result confirmed
- Our reporting
- Medium confidence
- Verification
- Peer-reviewed
- Importance
- Major (4 of 5)
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
Key facts
- CASP13 results announced December 2018
- Predicted inter-residue distances with a deep network, then optimized structures
- Nature paper published January 2020
- Precursor to AlphaFold 2, which essentially solved single-chain structure prediction at CASP14 (2020)
What happened
AlphaFold placed first overall among ~100 groups in the free-modeling category of CASP13.
Why it matters
Marked AI’s entry into a grand challenge of biology and set up the 2020 AlphaFold 2 breakthrough.
Sources
2 sources from 2 sites. Numbers match the chips in the text.
2 sources: 1 primary, 1 reaction
Primary
- Improved protein structure prediction using potentials from deep learning (Nature, DOI)doi.org, paper
Reactions
- Wikipedia: AlphaFolden.wikipedia.org, discussion
Changes
- Added science block (science & math tab)
- Filed