Title: scPPDM: A Diffusion Model for Single-Cell Drug–Response Prediction
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: AI for Biomedicine, Single Cell, Diffusion Models, VAE, Drug Perturbation

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.

TL;DR: scPPDM, a diffusion-based framework for single-cell drug-response prediction, enables interpretable control of dose and response strength and achieves SoTA performance on Tahoe-100M.