DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction.

ABSTRACT:

Motivation

Drug-target interaction (DTI) prediction accelerates drug discovery by identifying interactions between chemical compounds and proteins. Existing methods often rely on drug-drug and protein-protein similarity graphs but process them independently, limiting their ability to model interdependencies between modalities. Moving beyond isolated embedding generation from protein and drug graphs, we propose DCGAT-DTI, a novel deep learning framework with a dynamic cross-graph attention (DCGAT) module that dynamically models intra- and cross-graph interactions. Initial embeddings are generated using pretrained language models. Similarity graphs constructed from these embeddings are passed to DCGAT, which uses a Graph Convolutional Network-based Cross-Neighborhood Selection networ

SUBMITTER: Abir AR 

PROVIDER: S-EPMC12776360 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature