想找有效的药物组合？ScreenShot不用分子图谱就能预测，少量样本就比现有方法准，还能省三分之二实验预算。
ScreenShot是一种分层Transformer模型，预训练于40个药物筛选数据集，覆盖3700种药物和6000个生物样本。该模型通过上下文学习直接预测新患者对组合疗法的反应，无需微调和分子谱分析。在四个保留数据集上，ScreenShot的预测准确率和选择性有效治疗识别均优于所有基线。其内部表示还可驱动加权k-means++主动学习，用三分之一的筛选预算实现相同的命中检测。
ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening
Treating patients with combinations of drugs reduces the risk of resistance to any individual drug. Finding effective combinations is difficult because the large search space makes combinatorial screens prohibitively expensive, time consuming, and often technically infeasible. Predictive models can fill this gap, yet existing methods typically require molecular profiling of each sample and per-cohort training, limiting their applicability when time and tissue are scarce. To address this challenge, we introduce ScreenShot, a hierarchical transformer pretrained on 40 drug screening datasets covering 3,700 drugs and 6,000 biological samples, whose architecture mirrors the nested structure of screening data. Given a few-shot context of observations from a new patient, ScreenShot predicts the response of the sample to combination therapies through in-context learning, operating directly on functional measurements with no fine-tuning and no molecular profiling. On four held-out datasets, ScreenShot outperforms all baselines in both prediction accuracy and identification of selectively effective treatments. ScreenShot's internal representations are directly useful for experimental design: we use them to drive a weighted k-means++ active learning strategy that selects which experiments to run, achieving the same hit detection as uniform screening with a third of the budget. Source code and interactive dashboard: https://github.com/tansey-lab/screenshot.