Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions

The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by constructing shallow prediction models. These methods faile...

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Veröffentlicht in:IEEE/ACM transactions on computational biology and bioinformatics Jg. 19; H. 1; S. 455 - 464
Hauptverfasser: Sun, Chang, Xuan, Ping, Zhang, Tiangang, Ye, Yilin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adversarial regularization
Classifiers
Computational modeling
Computer applications
Computer networks
Diffusion tensor imaging
Drug interaction
Drug Interactions
Drug Repositioning
drug-target interaction
Drugs
generative adversarial network
Generative adversarial networks
graph convolutional autoencoder
Heterogeneous networks
LightGBM
Neural Networks, Computer
Nodes
Normal distribution
Pharmaceutical Preparations
Prediction models
Predictions
Predictive models
Proteins
Therapeutic targets
ISSN:1545-5963, 1557-9964, 1557-9964
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Zusammenfassung:The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by constructing shallow prediction models. These methods failed to deeply learn the low-dimension feature vectors for drugs and targets and ignored the distribution of these feature vectors. We proposed a graph convolutional autoencoder and generative adversarial network (GAN)-based method, GANDTI, to predict DTIs. We constructed a drug-target heterogeneous network to integrate various connections related to drugs and targets, i.e., the similarities and interactions between drugs or between targets and the interactions between drugs and targets. A graph convolutional autoencoder was established to learn the network embeddings of the drug and target nodes in a low-dimensional feature space, and the autoencoder deeply integrated different kinds of connections within the network. A GAN was introduced to regularize the feature vectors of nodes into a Gaussian distribution. Severe class imbalance exists between known and unknown DTIs. Thus, we constructed a classifier based on an ensemble learning model, LightGBM, to estimate the interaction propensities of drugs and targets. This classifier completely exploited all unknown DTIs and counteracted the negative effect of class imbalance. The experimental results indicated that GANDTI outperforms several state-of-the-art methods for DTI prediction. Additionally, case studies of five drugs demonstrated the ability of GANDTI to discover the potential targets for drugs.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2020.2999084