<p>Systematic mapping of chemical perturbation responses is revolutionizing polypharmacological drug discovery, yet remains constrained by experimental scalability. Here we introduce XPert, a biologically informed dual-branch transformer model designed to model gene-specific perturbation effects and dose–time dynamics. The dual-branch architecture separately encodes pre-perturbation and post-perturbation cellular states, allowing the model to disentangle intrinsic transcriptional patterns from regulatory shifts triggered by perturbations. By leveraging context-aware gene network modelling, XPert overcomes the over-denoising issues inherent in dominant variational-autoencoder-based approaches, achieving 36.7% higher Pearson’s correlation coefficient and 78.2% lower mean square error in cold-cell generalization under single-dose–single-time scenarios. Through extension to multidose–multitime prediction, XPert precisely resolves pharmacodynamic trajectories and uncovers key molecular events underlying the drug effects. To address real-world data scarcity, we apply knowledge transfer from large-scale preclinical screens to clinical contexts, achieving up to 15.04% improvement in patient-specific response predictions. Furthermore, XPert provides mechanistic interpretability, as evidenced by the identification of clinically validated resistance biomarkers.</p>