scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction

• URL: http://arxiv.org/abs/2510.11726v1
• Date: Wed, 08 Oct 2025 16:17:39 GMT
• Title: scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction
• Authors: Zhaokang Liang, Shuyang Zhuang, Xiaoran Jiao, Weian Mao, Hao Chen, Chunhua Shen,
• Abstract summary: This paper introduces scPPDM, the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data.<n>scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn.
• Score: 44.96130504547205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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