In this talk, we discuss the paper “scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction” by Z. Liang et al., arxiv, 2025.<\/p>\n
Abstract<\/strong><\/p>\n 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.<\/p>\n","protected":false},"excerpt":{"rendered":" In this talk, we discuss the paper “scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction” by Z. Liang et al., arxiv, 2025. Abstract This paper introduces the Single-Cell Perturbation Prediction … <\/p>\n