{
  "patent_number": "US 20240412809",
  "country": "US",
  "title": "AI Predicts 3D Protein Shapes from Genetic Code",
  "original_title": "Protein Structure Prediction from Amino Acid Sequences Using Self-Attention Neural Networks",
  "summary": "This patent describes how a computer uses self-attention neural networks to predict the complex three-dimensional shape of a protein based on its simple amino acid sequence, a process crucial for understanding biology and developing new medicines.",
  "what_it_does": "This method, performed by computers, predicts a protein's 3D structure from its amino acid sequence. First, it takes the amino acid sequence and creates an initial 'embedding' for every possible pair of amino acids in that sequence (Claim 1). Optionally, this initial embedding can be improved by comparing the protein's sequence to similar proteins from other species, creating a 'multiple sequence alignment' (Claim 2). Next, these initial pair embeddings are fed into a 'pair embedding neural network' that uses multiple 'self-attention neural network layers' to refine them into 'final embeddings' (Claim 1). Each self-attention layer updates an embedding by looking at other related embeddings, sometimes focusing only on pairs in the same 'row' or 'column' of a 2D arrangement (Claims 3-6). Finally, the computer determines the protein's predicted 3D structure using these refined final embeddings (Claim 1). For example, if you input the sequence for insulin, the system would output its predicted folded shape.",
  "what_it_does_not_cover": [
    "Does not cover methods that determine protein structure using physical experiments like X-ray crystallography or cryo-electron microscopy.",
    "Does not cover protein structure prediction systems that do not use self-attention neural network layers for processing pair embeddings.",
    "Does not cover methods that predict protein structure without first generating embeddings for pairs of amino acids.",
    "Does not cover systems that predict protein function or interactions without determining the 3D structure.",
    "Does not cover predictions based on inputs other than an amino acid sequence, such as a protein's physical properties or environment."
  ],
  "filed": "2024-08-23",
  "granted": null,
  "expires": "2044-08-23",
  "status": "active",
  "holder": "DeepMind Technologies",
  "holder_url": "https://patentbrief.org/company/deepmind-technologies",
  "inventors": [
    {
      "name": "John Jumper",
      "url": "https://patentbrief.org/inventor/john-jumper"
    },
    {
      "name": "Andrew W. Senior",
      "url": "https://patentbrief.org/inventor/andrew-w-senior"
    },
    {
      "name": "Russell James Bates",
      "url": "https://patentbrief.org/inventor/russell-james-bates"
    },
    {
      "name": "Alexander Pritzel",
      "url": "https://patentbrief.org/inventor/alexander-pritzel"
    },
    {
      "name": "Richard Andrew Evans",
      "url": "https://patentbrief.org/inventor/richard-andrew-evans"
    },
    {
      "name": "Mikhail Figurnov",
      "url": "https://patentbrief.org/inventor/mikhail-figurnov"
    },
    {
      "name": "Timothy Frederick Goldie Green",
      "url": "https://patentbrief.org/inventor/timothy-frederick-goldie-green"
    }
  ],
  "times_cited": 0,
  "tags": [
    "biotech",
    "ai_ml",
    "software",
    "pharmaceutical",
    "telecommunications"
  ],
  "abstract": "Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a predicted structure of a protein that is specified by an amino acid sequence. In one aspect, a method comprises: obtaining a multiple sequence alignment for the protein; determining, from the multiple sequence alignment and for each pair of amino acids in the amino acid sequence of the protein, a respective initial embedding of the pair of amino acids; processing the initial embeddings of the pairs of amino acids using a pair embedding neural network comprising a plurality of self-attention neural network layers to generate a final embedding of each pair of amino acids; and determining the predicted structure of the protein based on the final embedding of each pair of amino acids.",
  "url": "https://patentbrief.org/patent/us/20240412809/protein-structure-prediction-from-amino-acid-sequences-using-self-attention-neur",
  "markdown_url": "https://patentbrief.org/patent/us/20240412809/protein-structure-prediction-from-amino-acid-sequences-using-self-attention-neur/md",
  "google_patents_url": "https://patents.google.com/patent/US20240412809",
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}