MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

Predicting drug-target affinity (DTA) is beneficial for accelerating drug discovery. Graph neural networks (GNNs) have been widely used in DTA prediction. However, existing shallow GNNs are insufficient to capture the global structure of compounds. Besides, the interpretability of the graph-based DTA models highly relies on the graph attention mechanism, which can not reveal the global relationship between each atom of a molecule. In this study, we proposed a deep multiscale graph neural network based on chemical intuition for DTA prediction (MGraphDTA). We introduced a dense connection into the GNN and built a super-deep GNN with 27 graph convolutional layers to capture the local and global structure of the…

• NN
  National Natural Science Foundation of China

  Awards: 62176272, Grant No. 62176272
• SY
  Sun Yat-sen University

  Award: 62176272
• CM
  China Medical University Hospital

  Awards: DMR-111-123, DMR-111-102, DMR-111-143, DMR-111-123, DMR-111-102, DMR-111-143
• CM
  China Medical University
• ST
  Science, Technology and Innovation Commission of Shenzhen Municipality

  Award: JCYL 20170818165305521
• JD
  Junta de Castilla y León
• GM
  Guangzhou Municipal Science and Technology Project

  Award: 201803010072
• DO
  Division of Materials Research