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  1. Home
  3. Diffdr: A Diffusion-based Deep Learning Framework For Accurate Drug Response Imputation And Feature Selection.
  1. Home
  3. Diffdr: A Diffusion-based Deep Learning Framework For Accurate Drug Response Imputation And Feature Selection.

Related Experiment Videos

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

Qi Zheng1, Sihong Zheng1, Yanping Jiang1

  • 1School of Mathematics, Foshan University, Foshan, China.

Current Drug Targets
|June 12, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces DiffDR, a diffusion-based framework for predicting drug response using multi-omics data. DiffDR enhances accuracy and provides interpretable insights into molecular drivers of drug sensitivity.

Keywords:
Bioinformaticsdeep learningdiffusion-based frameworkdrug response mechanisms.drug response predictionmultimodal integration

Related Experiment Videos

Area of Science:

  • Computational biology
  • Genomics
  • Pharmacology

Background:

  • Molecular features significantly impact cellular responses to therapies, influencing drug sensitivity and resistance.
  • Understanding these molecular influences is key to explaining varied treatment outcomes.
  • Integrating multi-omics data aids in biomarker discovery and drug response prediction but faces modeling challenges due to data complexity.

Purpose of the Study:

  • To develop a robust and interpretable computational framework for predicting drug response.
  • To model complex dependencies within multi-omics data for enhanced prediction accuracy.
  • To provide mechanistic insights into how molecular features affect drug response.

Main Methods:

  • Introduced DiffDR, a diffusion-based framework utilizing an energy-constrained diffusion module.
  • Modeled multi-omics features and drug representations, efficiently propagating information without explicit graph structures.
  • Incorporated a gradient-based interpretability module for attributing predictions to specific omics features.

    • Main Results:

    • DiffDR achieved superior predictive performance over existing state-of-the-art methods.
    • Ablation studies confirmed the significant contribution of the energy-constrained diffusion mechanism to predictive accuracy.
    • The framework effectively handles high-dimensional multi-omics data.

    Conclusions:

    • DiffDR successfully captures cross-modal molecular dependencies.
    • The framework offers interpretable insights into drug response mechanisms.
    • DiffDR provides a robust approach for understanding molecular drivers of drug response.