Structure-sensitive transformer and multi-view graph contrastive learning enhanced prediction of drug-related microbes

Background: The human microbiome plays a crucial role in regulating the efficacy and toxicity of drugs as well as in developing the drugs. Therefore, predicting the drug-related microbes is beneficial for analyzing the functional mechanisms of drugs. Recently, the graph learning based methods demons...

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Vydané v:	BMC bioinformatics Ročník 26; číslo 1; s. 231 - 21
Hlavní autori:	Xuan, Ping, Wang, Rui, Gu, Jing, Cui, Hui, Zhang, Tiangang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 26.09.2025 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Ablation Algorithms Associative learning Bioinformatics Bioinformatics and chemoinformatics in drug discovery Biomedical and Life Sciences Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data augmentation Data mining Drug development Drug discovery Drug resistance Drugs Effectiveness Embedding Graph representations Health aspects Humans Identification and classification Learnable data augmentation Life Sciences Machine Learning Methods Microarrays Microbiomes Microbiota (Symbiotic organisms) Microbiota - drug effects Microorganisms Multi-view contrastive learning Pathogens Pharmaceutical research Prediction models Proteins Semantics Staphylococcus infections Structure-sensitive transformer Topology Toxicity View-level attention mechanism China View-level attention mechanism Learnable data augmentation Structure-sensitive transformer Multi-view contrastive learning
ISSN:	1471-2105, 1471-2105
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Popis
Shrnutí:	Background: The human microbiome plays a crucial role in regulating the efficacy and toxicity of drugs as well as in developing the drugs. Therefore, predicting the drug-related microbes is beneficial for analyzing the functional mechanisms of drugs. Recently, the graph learning based methods demonstrated their advantages in extracting the node features from the biological heterogeneous graphs. However, the previous methods failed to completely preserve the intrinsic structures of biological data and did not fully utilize the topological and positional information for predicting the drug-microbe associations. Results: We propose a new prediction model, structure-sensitive transformer and multi-view graph contrastive learning for microbe-drug association prediction (SMMDA), to encode and integrate the topological structures, semantics, and multiple-view embedding features of the drugs and microbes. Considering the sparsity of the original features of drugs and microbes, the learnable data augmentation strategy is designed to learn their global representations. Since similar drugs are more likely to associate with the similar microbes, a structure-sensitive transformer is proposed to integrate the topology structures composed of drugs (microbes) to form the multi-view embedding features. We design two contrastive learning strategies to exploit the complementary semantics across multiple views. As the embedding features from multiple views have various semantics, we design view-level attention to adaptively integrate these features. Conclusions: The extensive experimental results show that SMMDA outperforms several state-of-the-art methods for predicting the drug-related candidate microbes. The ablation studies show the effectiveness of the major innovations which include the learnable data augmentation, structure-sensitive transformer-based node feature learning, and multi-view contrastive learning. The case studies on three drugs also demonstrate SMMDA’s capability in retrieving the potential microbe candidates for the drugs.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-025-06199-w