Machine learning×Biological system

The Frontier Brief for this collision is still being written. The evidence below already reflects the current model run.

Pre-company space. No named players have staked this collision yet — an emerging pairing where the field, not a firm, is the story so far.

61 researchers publish on both sides of this collision without the fields themselves having met. Every name below is counted from papers in the corpus — not inferred.

• David SilverThe University of Tokyo
  9/1
• Demis HassabisGoogle (United States)
  8/1
• Koray KavukcuogluUniversity of Cambridge
  7/1
• Karen SimonyanNational Institute of Informatics
  5/1
• Laurent SifreUniversity of Cambridge
  5/1
• Alex BridglandEuropean Bioinformatics Institute
  3/1
• Augustin ŽídekEuropean Bioinformatics Institute
  3/1
• Tim GreenEuropean Bioinformatics Institute
  3/1
• David T. JonesThe London College
  3/1
• James KirkpatrickUniversity College London
  2/1

• Stanford UniversityUS
  131/15
• Harvard UniversityUS
  72/17
• University of California, BerkeleyUS
  132/8
• University of CambridgeGB
  65/15
• Massachusetts Institute of TechnologyUS
  63/15
• University of OxfordGB
  66/13

• Improved protein structure prediction using potentials from deep learning2020 · 3,583 citations · DOI ↗
  2
• Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects2021 · 70 citations · DOI ↗
  2
• Two-step machine learning enables optimized nanoparticle synthesis2021 · 239 citations · DOI ↗
  1
• Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information2024 · 82 citations · DOI ↗
  1

Counted from the corpus. Institution counts use best-effort affiliation (every author on a paper is paired with every institution on it), so read them as presence, not headcount.