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

TL;DR

This paper introduces DCGAT-DTI, a new deep learning framework that improves drug-target interaction prediction by dynamically modeling interactions between drug and protein data.

Contribution

The novel DCGAT module dynamically models intra- and cross-graph interactions for drug-target interaction prediction.

Findings

DCGAT-DTI outperforms state-of-the-art methods on benchmark datasets.

It achieves significant improvements in unbalanced cold start scenarios for both drugs and proteins.

Abstract

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 network to dynamically select cross-modal neighbors. This allows drug and protein embeddings to incorporate information from both modalities through intra- and cross-graph attention mechanisms.

Extensive evaluations on four benchmark datasets demonstrate that DCGAT-DTI outperforms state-of-the-art methods across warm and cold start splits for both balanced and unbalanced datasets. In the challenging unbalanced cold start scenarios, it achieves significant improvement in performance for both drugs and proteins over the baselines.

Source code is available at https://github.com/compbiolabucf/DCGAT-DTI.

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