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Related Experiment Video

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Chaokun Yan1, Quanao Li1, Junwei Luo2

  • 1School of Computer and Information Engineering, Henan University, Kaifeng, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces KGGAN-DDI, a new method for drug-drug interaction (DDI) prediction. It effectively captures asymmetric drug relationships using knowledge graphs and generative adversarial networks for improved accuracy.

Keywords:
asymmetrydirected generative adversarial networkdrug–drug interactionknowledge graphleast squares loss

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Area of Science:

  • Pharmacology
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Drug-drug interaction (DDI) prediction is crucial but challenging due to biological complexity.
  • Deep learning methods have advanced DDI prediction, but often neglect the asymmetrical nature of interactions.
  • This asymmetry can lead to information loss in feature learning.

Purpose of the Study:

  • To propose a novel method, KGGAN-DDI, for predicting potential drug-drug interactions.
  • To address the challenge of neglected asymmetry in DDI prediction.
  • To enhance the accuracy and efficiency of DDI prediction models.

Main Methods:

  • Utilized a knowledge graph embedding module to encode asymmetric drug pair associations.
  • Employed a dual-generator Generative Adversarial Network (GAN) for realistic sample generation.
  • Incorporated a least squares loss function to stabilize the optimization process and mitigate vanishing gradients.

    • Main Results:

    • KGGAN-DDI demonstrated superior performance compared to existing state-of-the-art methods in extensive experiments.
    • The knowledge graph embedding effectively captured and encoded asymmetric drug interactions.
    • The dual-generator GAN and least squares loss improved prediction accuracy and model stability.

    Conclusions:

    • KGGAN-DDI effectively predicts drug-drug interactions by addressing the asymmetry challenge.
    • The proposed method offers enhanced feature representation and contextual relevance for drug interactions.
    • Experimental results and case studies validate the effectiveness and superiority of KGGAN-DDI.