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

ABSTRACT: 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 compound simultaneously. We also developed a novel visual explanation method, grad

SUBMITTER: Yang Z 

PROVIDER: S-EPMC8768884 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature