IMF-DDI: Information Mapping and Fusion Framework for Drug-drug Interaction Prediction.

Journal: Interdisciplinary sciences, computational life sciences
Published Date: Nov 20, 2025

Abstract

Drug-drug interactions (DDIs) are crucial throughout various stages of drug development. Using computer-aided methods for accurate prediction of DDIs can enhance clinical safety and accelerate drug discovery. However, most existing deep learning methods heavily rely on the connectivity information between drugs. The neglect of the large number of potential DDI relationships can hinder the model's ability to extract meaningful information, thereby limiting its generalization capacity. To address these limitations, we propose IMF-DDI, an innovative DDI prediction framework that obtains drug molecule representations for DDI prediction by combining information from multiple external entities. First, our proposed information mapping module enables the model to capture the associations between drug molecules in terms of their interactions with multiple external entities. Meanwhile, the multi-source information fusion module efficiently integrates information from multiple external entities to generate the final representations of drug molecules. We carefully designed three distinct experimental tasks to validate the effectiveness of IMF-DDI. Our method establishes the current state-of-the-art across all tasks on the DrugBank dataset, while achieving the best performance in most tasks on the TWOSIDES dataset.

Authors

  • Xiaoyang Li
    Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu, 610041, China.
  • Yuhao Zhang
    Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
  • Yafei Liu
    College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China.
  • Xinyu Lu
    State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Peirong Ma
    School of Computer and Electronic Information / School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210023, China.
  • Yafei Li
  • Masaru Kitsuregawa
    Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan.
  • Yanhui Gu
    College of Design and Art, Huaiyin Institute of Technology, Huai'an, Jiangsu, China.

Keywords

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