SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction.

Journal: BMC bioinformatics
Published Date: Jan 23, 2024

Abstract

BACKGROUND: Drug-drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI.

Authors

  • Jing Zhu
    College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, China.
  • Chao Che
    Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian 116622, China.
  • Hao Jiang
    Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences , 555 Zuchongzhi Road, Shanghai 201203, China.
  • Jian Xu
    Department of Cardiology, Lishui Central Hospital and the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
  • Jiajun Yin
    General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116000, China.
  • Zhaoqian Zhong
    Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, 116622, Dalian, China. zhaoqianzhong@gmail.com.

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