A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.

Journal: Bioinformatics (Oxford, England)
Published Date: Jun 2, 2025

Abstract

MOTIVATION: Drug side effects refer to harmful or adverse reactions that occur during drug use, unrelated to the therapeutic purpose. A core issue in drug side effect prediction is determining the frequency of these drug side effects in the population, which can guide patient medication use and drug development. Many computational methods have been developed to predict the frequency of drug side effects as an alternative to clinical trials. However, existing methods typically build regression models on five frequency classes of drug side effects and tend to overfit the training set, leading to boundary handling issues and the risk of overfitting.

Authors

  • Haochen Zhao
    Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Dingxi Li
    Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Jian Zhong
  • Xiao Liang
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Veterinary Medicine, China Agricultural University, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Beijing, 100193, People's Republic of China; College of Veterinary Medicine, Qingdao Agricultural University, Qingdao, 266109, People's Republic of China.
  • Guihua Duan
    School of Computer Science and Engineering, Central South University, Changsha, China.
  • Jianxin Wang

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