WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest.

Journal: Bioinformatics (Oxford, England)
Published Date: Jul 1, 2018

Abstract

MOTIVATION: Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally determined activities. In particular, many G protein-coupled receptors (GPCRs), which are the largest integral membrane protein family and represent targets of nearly 40% drugs on the market, lack published experimental data about ligand interactions. Computational methods with the ability to accurately predict the bioactivity of ligands can help efficiently address this problem.

Authors

  • Jiansheng Wu
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) 30 South Puzhu Road Nanjing 211816 P. R. China.
  • Qiuming Zhang
    School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China.
  • Weijian Wu
    College of Computer and Information, Hohai University, Nanjing, China.
  • Tao Pang
    Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing, China.
  • Haifeng Hu
    School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China.
  • Wallace K B Chan
    Department of Biological Chemistry, University of Michigan, Ann Arbor, USA.
  • Xiaoyan Ke
    Child Mental Health Research Center, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

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