Accurate prediction of synergistic drug combination using a multi-source information fusion framework.

Journal: BMC biology
Published Date: Jul 3, 2025

Abstract

BACKGROUND: Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alone. Nowadays, deep learning is widely applied in this field. However, most methods overlook the important role of protein-protein interaction networks formed by gene expression products and the pharmacophore information of drugs in predicting drug synergy.

Authors

  • Shuting Jin
    Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen 361005, China. stjin.xmu@gmail.com.
  • Huaze Long
    School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China.
  • Anqi Huang
    Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education); Wuhan University School of Pharmaceutical Sciences, Wuhan, 430071, China.
  • Jianming Wang
    School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, China. sjwjm@zufe.edu.cn.
  • Xuan Yu
    School of Computer Engineering and Science, Shanghai University, Shanghai, China.
  • Zhiwei Xu
    Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Shanghai, China. xuzhiwei10800@163.com.
  • Junlin Xu
    School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei 430065, China.

Keywords

External Resources

Popular Topics
Recent Journals