HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning
Abstract Background Drug–drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functiona...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06052-0 |
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| _version_ | 1832585339015790592 |
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| author | Jinchen Sun Haoran Zheng |
| author_facet | Jinchen Sun Haoran Zheng |
| author_sort | Jinchen Sun |
| collection | DOAJ |
| description | Abstract Background Drug–drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures. Results This study introduces a novel framework for DDI prediction termed HDN-DDI. HDN-DDI integrates an explainable substructure extraction module to decompose drug molecules and represents them using innovative hierarchical molecular graphs, which effectively incorporates information from real chemical substructures and improves molecules encoding efficiency. Furthermore, the enhanced dual-view learning method inspired by the underlying mechanisms of DDIs enables HDN-DDI to comprehensively capture both hierarchical structure and interaction information. Experimental results demonstrate that HDN-DDI has achieved state-of-the-art performance with accuracies of 97.90% and 99.38% on the two widely-used datasets in the warm-start setting. Moreover, HDN-DDI exhibits substantial improvements in the cold-start setting with boosts of 4.96% in accuracy and 7.08% in F1 score on previously unseen drugs. Real-world applications further highlight HDN-DDI’s robust generalization capabilities towards newly approved drugs. Conclusion With its accurate predictions and robust generalization across different settings, HDN-DDI shows promise for enhancing drug safety and efficacy. Future research will focus on refining decomposition rules as well as integrating external knowledge while preserving the model’s generalization capabilities. |
| format | Article |
| id | doaj-art-2089084edaba4000879041a87666c25d |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-2089084edaba4000879041a87666c25d2025-01-26T12:54:56ZengBMCBMC Bioinformatics1471-21052025-01-0126112010.1186/s12859-025-06052-0HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learningJinchen Sun0Haoran Zheng1School of Computer Science and Technology, University of Science and Technology of ChinaSchool of Computer Science and Technology, University of Science and Technology of ChinaAbstract Background Drug–drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures. Results This study introduces a novel framework for DDI prediction termed HDN-DDI. HDN-DDI integrates an explainable substructure extraction module to decompose drug molecules and represents them using innovative hierarchical molecular graphs, which effectively incorporates information from real chemical substructures and improves molecules encoding efficiency. Furthermore, the enhanced dual-view learning method inspired by the underlying mechanisms of DDIs enables HDN-DDI to comprehensively capture both hierarchical structure and interaction information. Experimental results demonstrate that HDN-DDI has achieved state-of-the-art performance with accuracies of 97.90% and 99.38% on the two widely-used datasets in the warm-start setting. Moreover, HDN-DDI exhibits substantial improvements in the cold-start setting with boosts of 4.96% in accuracy and 7.08% in F1 score on previously unseen drugs. Real-world applications further highlight HDN-DDI’s robust generalization capabilities towards newly approved drugs. Conclusion With its accurate predictions and robust generalization across different settings, HDN-DDI shows promise for enhancing drug safety and efficacy. Future research will focus on refining decomposition rules as well as integrating external knowledge while preserving the model’s generalization capabilities.https://doi.org/10.1186/s12859-025-06052-0Drug-drug interactionsChemical substructuresHierarchical molecular graphsGraph attention networkDual-view representation learning |
| spellingShingle | Jinchen Sun Haoran Zheng HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning BMC Bioinformatics Drug-drug interactions Chemical substructures Hierarchical molecular graphs Graph attention network Dual-view representation learning |
| title | HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning |
| title_full | HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning |
| title_fullStr | HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning |
| title_full_unstemmed | HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning |
| title_short | HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning |
| title_sort | hdn ddi a novel framework for predicting drug drug interactions using hierarchical molecular graphs and enhanced dual view representation learning |
| topic | Drug-drug interactions Chemical substructures Hierarchical molecular graphs Graph attention network Dual-view representation learning |
| url | https://doi.org/10.1186/s12859-025-06052-0 |
| work_keys_str_mv | AT jinchensun hdnddianovelframeworkforpredictingdrugdruginteractionsusinghierarchicalmoleculargraphsandenhanceddualviewrepresentationlearning AT haoranzheng hdnddianovelframeworkforpredictingdrugdruginteractionsusinghierarchicalmoleculargraphsandenhanceddualviewrepresentationlearning |