TY - JOUR
T1 - Identifying drug-target interactions based on graph convolutional network and deep neural network
AU - Zhao, Tianyi
AU - Hu, Yang
AU - Valsdottir, Linda R.
AU - Zang, Tianyi
AU - Peng, Jiajie
N1 - Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - 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.
AB - 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.
KW - biological networks
KW - deep neural network
KW - drug-target interaction prediction
KW - graph convolutional network
UR - http://www.scopus.com/inward/record.url?scp=85103474313&partnerID=8YFLogxK
U2 - 10.1093/bib/bbaa044
DO - 10.1093/bib/bbaa044
M3 - 文章
C2 - 32367110
AN - SCOPUS:85103474313
SN - 1467-5463
VL - 22
SP - 2141
EP - 2150
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 2
ER -