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  "docSlug": "e8bbfe027845ab55cba77860ad12e49b",
  "documentTitle": "Absci | Investor Conference Presentation Deck | 22 slides",
  "authorId": "absci",
  "authorName": "Absci",
  "documentKindSlug": "conference-presentation",
  "documentKindLabel": "Conference presentation",
  "sourceTypeSlug": "investor_relations",
  "sourceTypeLabel": "Investor relations",
  "presentationDate": "2024-01-01 00:00:00",
  "orientation": "landscape",
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  "pageNumber": 1,
  "pageCount": 22,
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  "notes": "The slide uses Python-like code snippets as a design element to represent the company's computational drug discovery platform.",
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  "slideHref": "/slides/019de076-8eed-77e6-97e7-f406514b962a/1",
  "deckHref": "/decks/019de076-8eed-77e6-97e7-f406514b962a",
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  "deckAnchorHref": "/decks/019de076-8eed-77e6-97e7-f406514b962a#slide-1",
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      "kind": "image",
      "text": "absci",
      "attrs": null,
      "subkind": "logo",
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    {
      "bbox": {
        "h": 0.05,
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      "kind": "paragraph",
      "text": "42ND JP MORGAN HEALTHCARE CONFERENCE",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
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      "confidence": null,
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    {
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        "h": 0.1,
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        "x": 0.74,
        "y": 0.045
      },
      "kind": "paragraph",
      "text": "from absci import lead_opt_model\nlead_optimizer = lead_opt_model.load_latest()\nlibrary.naturalness =\nlead_optimizer.naturalness(library)\nlead_optimizer.optimize(library).to_wet_lab(assay=\"SPR\")",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
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    {
      "bbox": {
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        "x": 0.05,
        "y": 0.94
      },
      "kind": "paragraph",
      "text": "ABSCI CORPORATION 2024 ALL RIGHTS RESERVED",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
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    },
    {
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      },
      "kind": "paragraph",
      "text": "from absci import de_novo_model\nmodel = de_novo_model.load_latest()\nantigen = model.load_pdb(\"7olz.pdb\", chain=\"A\")\nantibodies = model.predict(antigen, N=300000)",
      "attrs": null,
      "subkind": "paragraph",
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      },
      "kind": "paragraph",
      "text": "from absci_library import codon_optimizer\nlibrary = codon_optimizer.reverse_translate(library)\nlibrary.to_csv(\"covid-antibody-designs.csv\")\nlibrary.to_wet_lab(assay=\"ACE\")",
      "attrs": null,
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      "kind": "paragraph",
      "text": "from absci import genetic_algorithm; parameters=[\"maximize|binding_affinity:pH=7.5\", \"minimize|binding_affinity:pH=6.0\", \"maximize|human_naturalness\"]; library = genetic_algorithm.multiparametric_optimization(library, parameters, evolutions=100);\nlibrary.to_wet_lab(assays=[\"ACE\", \"SPR\", \"Bioassays\"])",
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      "kind": "title",
      "text": "DRUG CREATION",
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