Predicting Drug-Drug Interactions Using Meta-path Based Similarities

Tanvir, Farhan; Islam, Muhammad Ifte Khairul; Akbaş, Esra

doi:10.1109/cibcb49929.2021.9562802

Cited by 12 publications

(2 citation statements)

References 19 publications

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“…Meta-paths, which are sequences of object types, can effectively capture the semantic relations between drugs. Recently, numerous studies have employed meta-path-based heterogeneous graph neural networks (HGNNs) for learning node embeddings, encompassing social networks [ 9 , 10 ], recommendation systems [ 11 , 12 ] and biological healthcare [ 13 , 14 , 15 ]. In these studies, meta-paths offer an interpretable way to reveal how entities connect through intermediary entities, for example, how users in recommendation systems connect through items and how drugs connect via target proteins or chemical substructures.…”

Section: Introductionmentioning

confidence: 99%

MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction

Hu,

Yu,

2024

Molecules

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The combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety. In recent years, deep models based on heterogeneous graph representation learning have attracted widespread interest in DDI event prediction and have yielded satisfactory results, but there is still room for improvement in prediction performance. In this study, we proposed a meta-path-based heterogeneous graph contrastive learning model, MPHGCL-DDI, for DDI event prediction. The model constructs two contrastive views based on meta-paths: an average graph view and an augmented graph view. The former represents that there are connections between drugs, while the latter reveals how the drugs connect with each other. We defined three levels of data augmentation schemes in the augmented graph view and adopted a combination of three losses in the model training phase: multi-relation prediction loss, unsupervised contrastive loss and supervised contrastive loss. Furthermore, the model incorporates indirect drug information, protein–protein interactions (PPIs), to reveal latent relations of drugs. We evaluated MPHGCL-DDI on three different tasks of two datasets. Experimental results demonstrate that MPHGCL-DDI surpasses several state-of-the-art methods in performance.

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Section: Introductionmentioning

confidence: 99%

MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction

Hu,

Yu,

2024

Molecules

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“…Tanvir et al. [ 21 ] employed meta-paths to extract rich semantic relationships between entities, and feeded a comprehensive feature to classifiers for drug-drug interaction prediction. These meta-paths are always designed empirically, which rely heavily on domain knowledge and are hardly transferred to other heterogeneous networks [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

AMGDTI: drug–target interaction prediction based on adaptive meta-graph learning in heterogeneous network

Su,

Hu,

Wang

et al. 2023

Briefings in Bioinformatics

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Prediction of drug–target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug–target relationship.

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Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network

Zhang,

Yao,

Yue

et al. 2023

Nat Comput Sci

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Predicting Drug-Drug Interactions Using Meta-path Based Similarities

Cited by 12 publications

References 19 publications

MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction

MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction

AMGDTI: drug–target interaction prediction based on adaptive meta-graph learning in heterogeneous network

Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network

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