MGPLI: exploring multigranular representations for protein-ligand interaction prediction.

Journal: Bioinformatics (Oxford, England)
Published Date: Oct 31, 2022

Abstract

MOTIVATION: The capability to predict the potential drug binding affinity against a protein target has always been a fundamental challenge in silico drug discovery. The traditional experiments in vitro and in vivo are costly and time-consuming which need to search over large compound space. Recent years have witnessed significant success on deep learning-based models for drug-target binding affinity prediction task.

Authors

  • Junjie Wang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Jie Hu
    Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States.
  • Huiting Sun
    Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
  • MengDie Xu
    Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Yun Yu
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China. yuyun@njmu.edu.cn.
  • Yun Liu
    Google Health, Palo Alto, CA USA.
  • Liang Cheng
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150001, China. liangcheng@hrbmu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals