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    "headline": "A Third of the Screen Found the Same Drug Hits",
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    "dek": "A foundation model used a few measurements from a new sample to prioritize combination-drug experiments without molecular profiling or fine-tuning.",
    "summary": "A foundation model used a few measurements from a new sample to prioritize combination-drug experiments without molecular profiling or fine-tuning.",
    "body_text": "ScreenShot was pretrained across 40 drug-screening datasets covering 3,700 drugs and 6,000 biological samples. Given a small context from a held-out patient sample, it predicts responses to drug combinations directly from functional measurements. On four held-out datasets it outperformed the reported baselines, and an active-learning strategy using its representations matched uniform screening's hit detection with one-third of the experimental budget. The work supports experiment selection; it does not prescribe treatment or establish patient outcomes.",
    "why_it_matters": "A foundation model used a few measurements from a new sample to prioritize combination-drug experiments without molecular profiling or fine-tuning.",
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      "The work supports experiment selection; it does not prescribe treatment or establish patient outcomes."
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    "tags": [
      "drug screening",
      "foundation models",
      "active learning"
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    {
      "source_id": "source-2026-08-13-009",
      "title": "arXiv preprint 2608.12219",
      "publisher": "arXiv",
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      "published_at": "2026-08-12T12:13:50.000-04:00",
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    "title": "A Third of the Screen Found the Same Drug Hits",
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