A heterogeneous graph method integrating multi-layer semantics and topological information for improving drug-target interaction prediction

doi:10.12122/j.issn.1673-4254.2025.11.12

Abstract

Abstract:

Objective To develop a heterogeneous graph prediction method based on the fusion of multi-layer semantics and topological information for addressing the challenges in drug-target interaction prediction, including insufficient modeling of high-order semantic dependencies, lack of adaptive fusion of semantic paths, and over-smoothing of node features. Methods A heterogeneous graph network with multiple types of entities such as drugs, proteins, side effects, and diseases was constructed, and graph embedding techniques were used to obtain low-dimensional feature representations. An adaptive metapath search module was introduced to automatically discover semantic path combinations for guiding the propagation of high-order semantic information. A semantic aggregation mechanism integrating multi-head attention was designed to automatically learn the importance of each semantic path based on contextual information and achieve differentiated aggregation and dynamic fusion among paths. A structure-aware gated graph convolutional module was then incorporated to regulate the feature propagation intensity for suppressing redundant information and redcuing over-smoothing. Finally, the potential interactions between drugs and targets were predicted through an inner product operation. Results Compared with existing drug-target interaction prediction methods, the proposed method achieved an average improvement of 3.4% and 2.4%, 3.0% and 3.8% in terms of the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC) on public datasets, respectively. Conclusion The drug-target interaction prediction method developed in this study can effectively extract complex high-order semantic and topological information from heterogeneous biological networks, thereby improving the accuracy and stability of drug-target interaction prediction. This method provides technical support and theoretical foundation for precise drug target discovery and targeted treatment of complex diseases.

Key words: drug-target interaction, heterogeneous networks, gated mechanism, multi-head attention mechanism, graph convolutional networks

Zihao CHEN, Yanbu GUO, Shengli SONG, Quanming GUO, Dongming ZHOU. A heterogeneous graph method integrating multi-layer semantics and topological information for improving drug-target interaction prediction[J]. Journal of Southern Medical University, 2025, 45(11): 2394-2404.

Figures/Tables 13

References 34

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Node type	Num	Edge type	Num
Drug	708	Drug-drug (interaction)	10 036
Protein	1512	Drug-drug (interaction)	501 264
Disease	5603	Drug-protein	1923
Side effect	4192	Drug-disease	199 214
		Drug-side effect	80 164
		Protein-disease	1 596 745
		Protein-protein (interaction)	7363
		Protein-protein (similarity)	2 286 144

Node type	Num	Edge type	Num
Drug	708	Drug-drug (interaction)	10 036
Protein	1512	Drug-drug (interaction)	501 264
Disease	5603	Drug-protein	1923
Side effect	4192	Drug-disease	199 214
		Drug-side effect	80 164
		Protein-disease	1 596 745
		Protein-protein (interaction)	7363
		Protein-protein (similarity)	2 286 144

Node type	Num	Edge type	Num
Drug	1094	Drug-drug	1 196 836
Protein	1556	Drug-drug	11 819
Chemical structure	881	Drug-chemical substructure	133 880
Side effect	4063	Drug-side effect	122 792
Substituent	738	Drug-side effect	20 798
GO term	4098	Protein-GO term	35 980
		Protein-protein	2 421 136

Node type	Num	Edge type	Num
Drug	1094	Drug-drug	1 196 836
Protein	1556	Drug-drug	11 819
Chemical structure	881	Drug-chemical substructure	133 880
Side effect	4063	Drug-side effect	122 792
Substituent	738	Drug-side effect	20 798
GO term	4098	Protein-GO term	35 980
		Protein-protein	2 421 136

Methods	Luo dataset
Methods	AUC	AUPRC
DTINet	0.879±0.004	0.906±0.003
NeoDTI	0.955±0.003	0.889±0.004
GCN-DTI	0.918±0.005	0.897±0.005
IMCHGAN	0.956±0.004	0.959±0.003
HampDTI	0.928±0.003	0.927±0.005
SGCL-DTI	0.977±0.002	0.976±0.002
GSRF-DTI	0.977±0.002	0.980±0.003
EEG-DTI	0.954±0.003	0.964±0.004
MIDTI	0.978±0.003	0.970±0.002
CE-DTI	0.976±0.002	0.976±0.002
SHGCL-DTI	0.957±0.004	0.958±0.003
AMGDTI	0.977±0.002	0.977±0.002
GMADTI	0.987±0.002	0.987±0.002