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AlphaFold 2 solves protein structure prediction at CASP14

★★★★★scienceDeepMindconfidence: high

AlphaFold 2 achieved a median GDT score of 92.4 at CASP14, accuracy competitive with experimental methods, widely seen as solving the 50-year-old protein folding problem for single chains.

Key facts

Science result

Field
biology / structural biology
Problem
Protein folding / structure prediction problem (open since 1972)
Result
Median GDT_TS of 92.4 across CASP14 targets — accuracy comparable to experimental structures for most single-chain proteins; later used to predict 200M+ structures.
AI system
AlphaFold 2
Human role
Human-designed system; predictions autonomous in blind assessment
Verification
Blind community assessment (CASP14); peer-reviewed in Nature (2021); widely experimentally corroborated
Status
confirmed
Why surprising
CASP co-founder John Moult said the 50-year-old problem had been 'in a sense solved' — years or decades earlier than most structural biologists expected.

What happened

Using an attention-based architecture (Evoformer) trained on known structures, AlphaFold 2 predicted 3D protein structures from amino-acid sequence with near-experimental accuracy.

Why it matters

The clearest case of AI producing a major scientific breakthrough; used by millions of researchers and recognized with a Nobel Prize.

Changelog

  • 2026-09-29: created
  • 2026-09-29: added science block (science & math tab)

Related events

  1. AlphaFold (v1) tops the CASP13 protein-structure prediction assessment ★★★★
  2. AlphaFold 3 predicts structures and interactions of all life's molecules ★★★★
  3. Nobel Prize in Chemistry for protein design and AlphaFold ★★★★★
  4. FT: Google DeepMind has broken up its Nobel-winning AlphaFold team; Jumper, Adler and Pritzel now at Anthropic ★★★

Sources (3)

id: 2020-11-30-alphafold-2 · updated 2026-09-29 · open in the interactive timeline