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20252 events
  1. reported 2025-07metric
    about 30 GDT better than AlphaFold 1; equivariance explains two or three GDT
    “AlphaFold 2 was about 30 GDT better than AlphaFold 1, and equivariance explains two or three of that”
    15:32inNobel Laureate John Jumper: AI is Revolutionizing Scientific DiscoveryYouTube
  2. reported 2025-07metric
    trained on 1% of available data, it was as accurate as or more accurate than AlphaFold 1
    “AlphaFold 2 trained on 1% of the data was as accurate as, or more accurate than, AlphaFold 1”
    13:23inNobel Laureate John Jumper: AI is Revolutionizing Scientific DiscoveryYouTube

Talks 1

27:26
Nobel Laureate John Jumper: AI is Revolutionizing Scientific Discovery

John Jumper describes how AlphaFold uses machine learning to predict protein structures, helping researchers investigate biology and develop new scientific hypotheses. He explains that its progress came from combining public protein structure data, compute, and many research ideas, with the research contributing far more than simply scaling the data. Blind assessments helped show that AlphaFold was a major advance, while open code and a public prediction database let biologists test it on their own work and build trust. He closes with examples of scientists using AlphaFold in unexpected ways, arguing that AI tools amplify experimental science and may lead to broader scientific capabilities.

Jul 15, 2025· 27 minDiscussed 13:23