H2GnnDTI: hierarchical heterogeneous graph neural networks for drug-target interaction prediction.

Journal: Bioinformatics (Oxford, England)

PMID: 40097269

Abstract

MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying DTIs necessitate computational tools for automated prediction and comprehension of DTIs. Despite recent advancements, current methods fail to fully leverage the hierarchical information in DTIs.

Authors

Yueying Jing

School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
Dongxue Zhang

Jilin University, College of Computer Science and Technology, Changchun, Jilin Province, China; Key Laboratory of Symbol Computation and Knowledge Engineering, Jilin University, Changchun 130012, China.
Limin Li

Keywords

Algorithms Computational Biology Drug Discovery Drug Repositioning Graph Neural Networks Neural Networks, Computer Pharmaceutical Preparations Proteins Software

External Resources

View on PubMed Access via DOI PubMed (40097269)

H2GnnDTI: hierarchical heterogeneous graph neural networks for drug-target interaction prediction.

Abstract

Authors

Keywords

External Resources

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