DiffDR: A Diffusion-based Deep Learning Framework for Accurate Drug Response Imputation and Feature Selection

Molecular features play critical roles in shaping cellular responses to therapeutic agents, and understanding their influence on drug sensitivity and resistance is essential for explaining heterogeneous treatment outcomes. Integrating multi-omics molecular information can uncover complex cross-modal dependencies, identify potential biomarkers, and enhance drug response prediction. However, the high dimensionality and strong interdependencies of multi-omics data pose substantial modeling challenges, underscoring the need for robust, interpretable computational approaches.

This study presents DiffDR, a diffusion-based framework that models multi-omics features and drug representations through an energy-constrained diffusion module. This module encodes batched samples and efficiently propagates information while preventing over-smoothing, enabling the capture of both global and local dependencies without relying on explicit graph structures. To enhance model transparency, DiffDR incorporates an integrated gradient-based interpretability module that quantitatively attributes prediction outcomes to specific omics features.

DiffDR demonstrates superior predictive performance compared with several state-of-theart drug response prediction methods. Ablation analysis indicates that the energy-constrained diffusion mechanism substantially improves predictive accuracy, confirming its effectiveness in handling high-dimensional multi-omics data.

The findings highlight the value of DiffDR in capturing cross-modal molecular dependencies and providing interpretable insights into drug response mechanisms.

Overall, DiffDR represents a robust and interpretable approach for drug response prediction, enabling biologically meaningful mechanistic insights into molecular drivers of drug response.