Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 2, Pages 2141-2150Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa044
Keywords
drug-target interaction prediction; graph convolutional network; deep neural network; biological networks
Funding
- National Natural Science Foundation of China [61702421, U1811262, 61772426]
- China Postdoctoral Science Foundation [2017M610651]
- National Key Research and Development Program of China [2016YFC0901605]
- National Science and Technology Major Project [2016YFC 1202302]
- [20180029]
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In order to improve the identification of DTIs, a DPP network was established and a novel learning framework GCN-DTI was proposed. The method utilizes graph convolutional networks to learn DPP features and deep neural networks to predict final DTI labels, outperforming existing approaches significantly.
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.
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