research
A Third of the Screen Found the Same Drug Hits
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.
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.
Limits and context
- The work supports experiment selection; it does not prescribe treatment or establish patient outcomes.
Key claims
A foundation model used a few measurements from a new sample to prioritize combination-drug experiments without molecular profiling or fine-tuning.
Qualification: The work supports experiment selection; it does not prescribe treatment or establish patient outcomes.
Evidence: source-2026-08-13-009
Sources
- arXiv preprint 2608.12219arXiv · primary research
Corrections
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