GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Computational approaches for identifying the protein-ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein-ligand binding affinity and achieve significant performance improvement. However, protein-ligand binding affinity prediction still has fundamental challenges. One challenge is that the mutual information between proteins and ligands is hard to capture. Another challenge is how to find and highlight the important atoms of the ligands and residues of the proteins.

Authors

  • Kaili Wang
    Central China Normal University, China.
  • Renyi Zhou
    School of Computer Science and Engineering, Central South University, China.
  • Jing Tang
    Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.