ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening
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TL;DR - ScreenShot is a hierarchical transformer that predicts combination-drug responses from a few observations on a new patient, without molecular profiling or fine-tuning. It could reduce screening costs while improving personalized treatment selection.
- Pretrained on 40 datasets spanning 3,700 drugs and 6,000 biological samples.
- Uses in-context learning over functional measurements to make few-shot predictions.
- Outperformed baselines on four held-out datasets in accuracy and selective-treatment identification.
- Its active-learning strategy matched uniform screening’s hit detection using one-third of the budget.
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ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening
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TL;DR - ScreenShot is a hierarchical transformer that predicts combination-drug responses from a few observations on a new patient, without molecular profiling or fine-tuning. It could reduce screening costs while improving personalized treatment selection.
- Pretrained on 40 datasets spanning 3,700 drugs and 6,000 biological samples.
- Uses in-context learning over functional measurements to make few-shot predictions.
- Outperformed baselines on four held-out datasets in accuracy and selective-treatment identification.
- Its active-learning strategy matched uniform screening’s hit detection using one-third of the budget.