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          "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\")",
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          "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)",
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          "kind": "paragraph",
          "text": "CORPORATE PRESENTATION WINTER 2023",
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          "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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          "text": "DRUG CREATION",
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