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GraphDPA: Predicting drug-pathway associations by graph convolutional networks.

Zhong-Rui Zhang1, Zhen-Ran Jiang1

  • 1School of Computer Science and Technology, East China Normal University, Shanghai 200062, China.

Computational Biology and Chemistry
|July 5, 2022
PubMed
Summary

This study introduces GraphDPA, a novel graph convolutional network (GCN) model for predicting drug-pathway associations. GraphDPA accurately identifies drug-target pathways, advancing pathway-based drug discovery and reducing potential toxicity.

Keywords:
Drug-pathway associationFeature fusionGraph convolutional networks

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Area of Science:

  • Computational biology
  • Pharmacology
  • Bioinformatics

Background:

  • Pathway-based drug discovery aims to develop safer drugs with fewer side effects.
  • Accurately identifying drug-target pathway associations is a significant challenge in this field.
  • Advancements in biomolecular interaction databases and neural networks offer new opportunities for drug-pathway association prediction.

Purpose of the Study:

  • To propose a novel computational model, GraphDPA, for predicting drug-pathway associations.
  • To leverage graph convolutional networks (GCNs) for learning drug and pathway features.
  • To enhance the accuracy and efficiency of identifying potential drug-target pathways.

Main Methods:

  • GraphDPA represents drug and pathway-gene associations as a graph structure.
  • Graph convolutional networks (GCNs) are employed to learn latent representations of drugs and pathways.
  • The model predicts associations between drugs and biological pathways based on learned features.

Main Results:

  • GraphDPA demonstrated high accuracy in predicting drug-pathway associations.
  • The model's performance validates the effectiveness of GCNs in this predictive task.
  • The findings suggest GraphDPA's potential utility in accelerating drug discovery pipelines.

Conclusions:

  • GraphDPA offers a promising approach for accurate drug-pathway association prediction.
  • The study highlights the significant potential of GCNs in computational drug discovery.
  • This method can aid in identifying drugs with desired pathway targeting profiles and reduced toxicity.