Predicting Drug-Target Interactions Via Dual-Stream Graph Neural Network

Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force search over a compound database is financially infeasible. We have witnessed the increasing measured drug-target interactions records in recent years, and the rich drug/protein-related information allows th...

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Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 21; no. 4; pp. 948 - 958
Main Authors: Li, Yuhui, Liang, Wei, Peng, Li, Zhang, Dafang, Yang, Cheng, Li, Kuan-Ching
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2024
Subjects:
Deep learning
Diffusion tensor imaging
Diseases
drug-target interactions
Drugs
graph neural network
Machine learning
matrix completion
Predictive models
Proteins
Task analysis
ISSN:1545-5963, 1557-9964, 1557-9964
Online Access:Get full text
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Summary:Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force search over a compound database is financially infeasible. We have witnessed the increasing measured drug-target interactions records in recent years, and the rich drug/protein-related information allows the usage of graph machine learning. Despite the advances in deep learning-enabled drug-target interaction, there are still open challenges: (1) rich and complex relationship between drugs and proteins can be explored; (2) the intermediate node is not calibrated in the heterogeneous graph. To tackle with above issues, this paper proposed a framework named DSG-DTI. Specifically, DSG-DTI has the heterogeneous graph autoencoder and heterogeneous attention network-based Matrix Completion. Our framework ensures that the known types of nodes (e.g., drug, target, side effects, diseases) are precisely embedded into high-dimensional space with our pretraining skills. Also, the attention-based heterogeneous graph-based matrix completion achieves highly competitive results via effective long-range dependencies extraction. We verify our model on two public benchmarks. The result of two publicly available benchmark application programs show that the proposed scheme effectively predicts drug-target interactions and can generalize to newly registered drugs and targets with slight performance degradation, outperforming the best accuracy compared with other baselines.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2022.3204188