Predicting and interpreting cell-type-specific drug responses in the small-data regime using inductive priors

Predicting how small molecules affect diverse cell types phenotypically is central to drug discovery, yet it remains a challenging task. Modelling cell-type-specific transcriptional responses provides a scalable alternative for early candidate identification, enabling broader exploration and lower costs than exhaustive experimental exploration of the chemical space. Here we present PrePR-CT, a graph-based deep learning approach that utilizes cell-type-specific co-expression networks as an inductive bias to predict transcriptional responses to chemical perturbations. Graph attention networks learn biologically meaningful representations that capture cell-type-specific gene interactions, enabling gene-level attributions. Across five single-cell RNA sequencing datasets, including human blood and multiple cancer lines, one bulk transcriptomics dataset and a large-scale small-molecule screen, the method generalizes to unseen perturbations and previously unseen cell types under data-limited settings, achieving higher accuracy for expression variability compared to generative baselines. Attribution analyses identify high-attention genes that complement traditional differential expression analyses, highlighting pathway-specific mechanisms of small-molecule response. By combining scalability, robustness to distribution shifts and interpretability, PrePR-CT enables cell-type-resolved prediction of drug responses, providing a foundation for more precise modelling of cellular perturbations in early drug discovery. Alsulami et al. present PrePR-CT, a computational approach that predicts how different cell types respond to drug-like compounds using limited data. By integrating biological networks with machine learning, it improves accuracy, interpretability and efficiency in early drug discovery.