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  "documentTitle": "Absci | Investor Presentation Deck | 28 slides",
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  "authorName": "Absci",
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  "presentationDate": "2022-05-01 00:00:00",
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  "notes": "Uses a comparison framework to justify the need for Absci's technology.",
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      "kind": "callout",
      "text": "Training AI models requires high-quality data on multiple parameters to optimize desired drug function, manufacturability, developability, and immunogenicity - Absci's assays are capable of characterizing complex biologics & generating data to train AI",
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      "text": "Aspirin ~21 atoms vs Monoclonal antibody ~25,000 atoms",
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      "text": "Small Molecule Drugs: Consistent chemical manufacturing methods, Well-defined homogenous structures, Discovery & manufacturing mostly process independent, Completely characterizable, Generally non-immunogenic. Protein Therapeutics: Produced by living cells subject to variability, Complex, heterogeneous structures, Discovery & manufacturing strongly process dependent, Not entirely characterizable, May be immunogenic",
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      "text": "Proteins are complex, making it difficult to generate the quality of data needed for training AI models",
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