MFR-DTA: a multi-functional and robust model for predicting drug-target binding affinity and region.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Recently, deep learning has become the mainstream methodology for drug-target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug-target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers.

Authors

  • Yang Hua
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Xiaoning Song
    Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.
  • Zhenhua Feng
    School of Computer Science and Electronic Engineering, University of Surrey, Guildford GU2 7XH, UK.
  • Xiaojun Wu
    Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.