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    "headline": "The Reaction Model Put Both Sides on One Graph",
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    "dek": "RxnCLF encodes reactants and products together so pretraining can learn the transformation rather than two disconnected molecular snapshots.",
    "summary": "RxnCLF encodes reactants and products together so pretraining can learn the transformation rather than two disconnected molecular snapshots.",
    "body_text": "RxnCLF uses a condensed reaction graph and contrastive pretraining on 1.7 million Pistachio reactions. The representation captures reaction-center and side-chain context, then outperformed reported graph and sequence baselines after fine-tuning on public and proprietary yield-prediction sets. The author-reported results concern benchmark and high-throughput-experiment datasets; they do not establish laboratory yield for arbitrary new reactions.",
    "why_it_matters": "RxnCLF encodes reactants and products together so pretraining can learn the transformation rather than two disconnected molecular snapshots.",
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      "The author-reported results concern benchmark and high-throughput-experiment datasets; they do not establish laboratory yield for arbitrary new reactions."
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        "text": "RxnCLF encodes reactants and products together so pretraining can learn the transformation rather than two disconnected molecular snapshots.",
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        "qualification": "The author-reported results concern benchmark and high-throughput-experiment datasets; they do not establish laboratory yield for arbitrary new reactions."
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    "tags": [
      "chemistry",
      "reaction prediction",
      "foundation models"
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      "source_id": "source-2026-08-07-013",
      "title": "arXiv preprint 2608.06259",
      "publisher": "arXiv",
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    "title": "The Reaction Model Put Both Sides on One Graph",
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