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

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.

Results

Here, we introduce H2GnnDTI, a novel two-level hierarchical heterogeneous graph learning model to predict DTIs, by integrating the structures of drugs and proteins via a low-level view GNN and a high-level view GNN. The hierarchical graph consists of high-level heterogeneous nodes representing drugs and proteins, connected by edges representing known DTIs. Each drug or protei

SUBMITTER: Jing Y 

PROVIDER: S-EPMC11954568 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature