Deep generative framework for modeling single-cell drug perturbation response.

Abstract

Accurate characterization of cell-specific drug responses is a prerequisite for linking molecular mechanisms to patient-level therapeutic variability. Heterogeneous cellular responses substantially complicate the inference of drug effects from single-cell transcriptomic data. To address this, we introduce scDPR, the single-cell Drug Perturbation Responses framework. This framework predicts drug-perturbed single-cell transcriptomic states and decomposes observed responses into distinct causal effects. The framework consists of two modules: an attribute adaptation module that models drug-induced transcriptional shifts at the single-cell level; and a causal graph learning module that combining optimal transport (OT) infers direct drug effects while accounting for confounding influences. By integrating drug molecular features, dosage information, and cell-specific attributes, the model learns a latent representation that captures cell-specific transcriptional responses to drug perturbations. We conducted systematic evaluations on large-scale single-cell perturbation datasets, including L1000 and sci-Plex3. Experimental results demonstrate that scDPR outperforms state-of-the-art methods such as chemCPA, in forecasting transcriptome responses to unseen compounds and unknown pathways. It also offers insights into the cellular heterogeneity of drug responses, identifying key subpopulations that contribute to variability in treatment outcomes. In addition, compared to mainstream basic models, scDPR achieves better performance with shorter training time and fewer rounds, providing efficient computational support for large-scale drug screening and mechanism research.