Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Accurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein-ligand complex is ongoing.

Authors

  • Sangmin Seo
    Department of Computer Science and Engineering, Incheon National University, Incheon, Republic of Korea.
  • Jonghwan Choi
    Department of Computer Science and Engineering, Incheon National University, Incheon, Republic of Korea.
  • Sanghyun Park
    Department of Medical Statistics, College of Medicine, Catholic University of Korea, Seoul, Korea.
  • Jaegyoon Ahn
    Department of Integrative Biology and Physiology, University of California, Los Angeles, USA. Electronic address: jgahn@ucla.edu.