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    "headline": "Molecule Editing Learns from the Previous Attempt",
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    "dek": "A reinforcement-learning method feeds property improvements and structural similarity back into the next optimization turn.",
    "summary": "A reinforcement-learning method feeds property improvements and structural similarity back into the next optimization turn.",
    "body_text": "Molecular optimization must often improve several properties while keeping the candidate recognizably close to its starting structure. MFG trains a language model to use a feedback vector describing both property changes and similarity after each turn, with a reward that favors progressive improvement. Across ten multi-objective tasks, the authors report an 81.50 percent average similarity-constrained success rate, versus 70.81 percent for the base model. These are computational optimization results, not laboratory validation or evidence of a viable drug. The work formalizes the iterative loop medicinal chemists expect: a proposed change should respond to what happened on the previous one.",
    "why_it_matters": "A reinforcement-learning method feeds property improvements and structural similarity back into the next optimization turn.",
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      "These are computational optimization results, not laboratory validation or evidence of a viable drug."
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    "tags": [
      "MFG",
      "molecular optimization",
      "drug discovery"
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      "title": "arXiv preprint 2610.18851, original v1",
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