KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning
Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel de...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1525029/full |
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| author | Huimin Luo Huimin Luo Hui Yang Hui Yang Ge Zhang Ge Zhang Jianlin Wang Jianlin Wang Junwei Luo Chaokun Yan Chaokun Yan Chaokun Yan |
| author_facet | Huimin Luo Huimin Luo Hui Yang Hui Yang Ge Zhang Ge Zhang Jianlin Wang Jianlin Wang Junwei Luo Chaokun Yan Chaokun Yan Chaokun Yan |
| author_sort | Huimin Luo |
| collection | DOAJ |
| description | Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, a graph regularized approach is applied to integrate multiple drug and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases. Then, topological feature representations of drugs and diseases are learned from constructed biomedical knowledge graphs (KGs) which encompasses known drug-related and disease-related interactions. Next, the similarity features and topological features are fused by utilizing an attention-based feature fusion method. Finally, drug-disease associations are predicted using the graph convolutional network. Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. Moreover, case study results further validate the effectiveness of KGRDR in predicting novel drug-disease interactions. |
| format | Article |
| id | doaj-art-32b1399a1a4848b59304db32ceaf4f9f |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-32b1399a1a4848b59304db32ceaf4f9f2025-02-11T06:59:52ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-02-011610.3389/fphar.2025.15250291525029KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioningHuimin Luo0Huimin Luo1Hui Yang2Hui Yang3Ge Zhang4Ge Zhang5Jianlin Wang6Jianlin Wang7Junwei Luo8Chaokun Yan9Chaokun Yan10Chaokun Yan11School of Computer and Information Engineering, Henan University, Kaifeng, ChinaHenan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaHenan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaHenan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaHenan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, ChinaCollege of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaHenan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, ChinaAcademy for Advanced Interdisciplinary Studies, Henan University, Zhengzhou, ChinaComputational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, a graph regularized approach is applied to integrate multiple drug and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases. Then, topological feature representations of drugs and diseases are learned from constructed biomedical knowledge graphs (KGs) which encompasses known drug-related and disease-related interactions. Next, the similarity features and topological features are fused by utilizing an attention-based feature fusion method. Finally, drug-disease associations are predicted using the graph convolutional network. Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. Moreover, case study results further validate the effectiveness of KGRDR in predicting novel drug-disease interactions.https://www.frontiersin.org/articles/10.3389/fphar.2025.1525029/fulldrug repositioningdrug-disease interaction predictionmulti-similarity fusionbiomedical knowledge graphfeature fusion |
| spellingShingle | Huimin Luo Huimin Luo Hui Yang Hui Yang Ge Zhang Ge Zhang Jianlin Wang Jianlin Wang Junwei Luo Chaokun Yan Chaokun Yan Chaokun Yan KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning Frontiers in Pharmacology drug repositioning drug-disease interaction prediction multi-similarity fusion biomedical knowledge graph feature fusion |
| title | KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning |
| title_full | KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning |
| title_fullStr | KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning |
| title_full_unstemmed | KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning |
| title_short | KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning |
| title_sort | kgrdr a deep learning model based on knowledge graph and graph regularized integration for drug repositioning |
| topic | drug repositioning drug-disease interaction prediction multi-similarity fusion biomedical knowledge graph feature fusion |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1525029/full |
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