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  "docId": "019de076-7746-74ba-85f9-63c229edc49f",
  "docSlug": "987f138dade45ff64effcbdaa91cb6f6",
  "documentTitle": "Absci | Investor Presentation Deck | 42 slides",
  "authorId": "absci",
  "authorName": "Absci",
  "documentKindSlug": "pitchdeck",
  "documentKindLabel": "Pitch deck",
  "sourceTypeSlug": "investor_relations",
  "sourceTypeLabel": "Investor relations",
  "presentationDate": "2023-12-01 00:00:00",
  "orientation": "landscape",
  "aspectRatio": 1.7777778,
  "pageNumber": 1,
  "pageCount": 42,
  "prevPage": null,
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  "density": "dense",
  "nDataPoints": 0,
  "notes": "The slide uses Python-like code snippets as a design element to emphasize the computational nature of their drug discovery process.",
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  "slideHref": "/slides/019de076-7746-74ba-85f9-63c229edc49f/1",
  "deckHref": "/decks/019de076-7746-74ba-85f9-63c229edc49f",
  "deckJsonHref": "/decks/019de076-7746-74ba-85f9-63c229edc49f.json",
  "deckAnchorHref": "/decks/019de076-7746-74ba-85f9-63c229edc49f#slide-1",
  "components": [
    {
      "bbox": {
        "h": 0.05,
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        "x": 0.05,
        "y": 0.06
      },
      "kind": "image",
      "text": "absci",
      "attrs": null,
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    {
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        "h": 0.15,
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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,
      "subkind": "paragraph",
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    {
      "bbox": {
        "h": 0.15,
        "w": 0.2,
        "x": 0.72,
        "y": 0.05
      },
      "kind": "paragraph",
      "text": "from absci import lead_opt_model\nlead_optimizer = lead_opt_model.load_latest()\nlibrary.naturalness = lead_optimizer.naturalness(library)\nlead_optimizer.optimize(library).to_wet_lab(assay=\"SPR\")",
      "attrs": null,
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        "h": 0.15,
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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": "CORPORATE PRESENTATION WINTER 2023",
      "attrs": null,
      "subkind": "paragraph",
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        "h": 0.05,
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        "x": 0.28,
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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\"])",
      "attrs": null,
      "subkind": "paragraph",
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    {
      "bbox": {
        "h": 0.7,
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        "y": 0.145
      },
      "kind": "title",
      "text": "DRUG CREATION",
      "attrs": null,
      "subkind": "headline",
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