Identifying drug-target interactions based on graph convolutional network and deep neural network

Tianyi Zhao, Yang Hu, Linda R. Valsdottir, Tianyi Zang, Jiajie Peng

Research output: Contribution to journalArticlepeer-review

237 Scopus citations

Abstract

Identification of new drug-target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug-protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, 'graph convolutional network (GCN)-DTI', for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.

Original languageEnglish
Pages (from-to)2141-2150
Number of pages10
JournalBriefings in Bioinformatics
Volume22
Issue number2
DOIs
StatePublished - 1 Mar 2021

Keywords

  • biological networks
  • deep neural network
  • drug-target interaction prediction
  • graph convolutional network

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