首个基于扩散模型的单细胞药物反应预测框架，可精准模拟药物剂量效应。
scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction
- 用非拼接注意力融合用药前状态与药物剂量信息，统一建模在潜在空间。
- 在未见药物和组合下，基因表达变化预测准确率提升超34%。
- 支持可解释的剂量调节与假设分析，减少实验成本，适合药物研发场景。
本文提出首个基于扩散模型的单细胞药物反应预测框架scPPDM，首次实现从单细胞转录组数据中预测药物响应。scPPDM通过非拼接的梯度注意力机制，将用药前状态与药物剂量信息联合编码至统一潜在空间。推理时，解耦的无分类器引导机制提供状态保持与药物响应强度两个可解释控制，并将剂量映射为引导强度以实现可调响应强度。在Tahoe-100M基准测试中，针对未见协变量组合（UC）和未见药物（UD）两种严苛场景，scPPDM在对数倍数变化恢复、差异相关性、解释方差和差异基因重叠等指标上均达到新最优表现。代表性提升包括在未见药物场景下，差异基因对数倍数变化的Spearman/Pearson相关性分别提高36.11%/34.21%（优于第二佳模型）。该控制接口支持透明的“假设分析”与剂量调优，降低实验负担，同时保留生物学特异性。
原文摘要 · Abstract (English)
This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn. During inference, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude for tunable intensity. Evaluated on the Tahoe-100M benchmark under two stringent regimes, unseen covariate combinations (UC) and unseen drugs (UD), scPPDM sets new state-of-the-art results across log fold-change recovery, delta correlations, explained variance, and DE-overlap. Representative gains include +36.11%/+34.21% on DEG logFC-Spearman/Pearson in UD over the second-best model. This control interface enables transparent what-if analyses and dose tuning, reducing experimental burden while preserving biological specificity.
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