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A Graph Deep Learning-Based Framework for Drug–Disease Association Identification with Chemical Structure Similarities.

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Title: A Graph Deep Learning-Based Framework for Drug–Disease Association Identification with Chemical Structure Similarities.
Authors: Zhao, Bo-Wei1,2,3 (AUTHOR), Su, Xiao-Rui1,2,3 (AUTHOR), Li, Dong-Xu1,2,3 (AUTHOR), Li, Guo-Dong1,2,3 (AUTHOR), Hu, Peng-Wei1,2,3 (AUTHOR), Zhao, Yong-Gang4 (AUTHOR) 1940503593@qq.com, Hu, Lun1,2,3 (AUTHOR) hulun@ms.xjb.ac.cn
Source: Journal of Computational Biophysics & Chemistry. Apr2025, Vol. 24 Issue 3, p331-343. 13p.
Subjects: Machine learning, Drug repositioning, Drug discovery, K-means clustering, Knowledge graphs, Deep learning
Abstract: Traditional drug development requires a lot of time and effort, while computational drug repositioning enables to discover the underlying mechanisms of drugs, thereby reducing the cost of drug discovery and development. However, existing computational models utilize only low-order biological information at the level of individual drugs, diseases and their associations, making it difficult to dig deeper and fuse higher-order structural information. In this paper, we develop a novel prediction model, namely DRGDL, based on graph deep learning to infer potential drug-disease associations (DDAs) by coming chemical structural similarity information, which can more comprehensively make use of the biological features of drugs and diseases. First, the lower-order and higher-order representations of drugs and diseases are captured by two different graph deep learning strategies. Then, suitable negative samples are select by use of k-means algorithm. At last, the Random Forest classifier is applied to complete the prediction task of DDAs by integrating two representations of drugs and diseases. Experiment results indicate that DRGDL achieves excellent performance under ten-fold cross-validation on two benchmark datasets. A novel graph deep learning-based model, namely DRGDL, is proposed to improve the accuracy of drug-disease association (DDA) prediction by incorporating chemical structural similarity information. DRGDL utilizes two graph deep learning techniques—GAT for lower-order and node2vec for higher-order representations of drugs and diseases—thereby enhancing the identification of potential DDAs. Experimental results demonstrate that DRGDL outperforms existing methods, highlighting its effectiveness in drug repositioning by integrating biological knowledge and advanced graph learning algorithms. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computational Biophysics & Chemistry is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: A Graph Deep Learning-Based Framework for Drug–Disease Association Identification with Chemical Structure Similarities.
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  Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Bo-Wei%22">Zhao, Bo-Wei</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Su%2C+Xiao-Rui%22">Su, Xiao-Rui</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Dong-Xu%22">Li, Dong-Xu</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Guo-Dong%22">Li, Guo-Dong</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Peng-Wei%22">Hu, Peng-Wei</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yong-Gang%22">Zhao, Yong-Gang</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> 1940503593@qq.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Lun%22">Hu, Lun</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> hulun@ms.xjb.ac.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+Biophysics+%26+Chemistry%22">Journal of Computational Biophysics & Chemistry</searchLink>. Apr2025, Vol. 24 Issue 3, p331-343. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+repositioning%22">Drug repositioning</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+discovery%22">Drug discovery</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
– Name: Abstract
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  Data: Traditional drug development requires a lot of time and effort, while computational drug repositioning enables to discover the underlying mechanisms of drugs, thereby reducing the cost of drug discovery and development. However, existing computational models utilize only low-order biological information at the level of individual drugs, diseases and their associations, making it difficult to dig deeper and fuse higher-order structural information. In this paper, we develop a novel prediction model, namely DRGDL, based on graph deep learning to infer potential drug-disease associations (DDAs) by coming chemical structural similarity information, which can more comprehensively make use of the biological features of drugs and diseases. First, the lower-order and higher-order representations of drugs and diseases are captured by two different graph deep learning strategies. Then, suitable negative samples are select by use of k-means algorithm. At last, the Random Forest classifier is applied to complete the prediction task of DDAs by integrating two representations of drugs and diseases. Experiment results indicate that DRGDL achieves excellent performance under ten-fold cross-validation on two benchmark datasets. A novel graph deep learning-based model, namely DRGDL, is proposed to improve the accuracy of drug-disease association (DDA) prediction by incorporating chemical structural similarity information. DRGDL utilizes two graph deep learning techniques—GAT for lower-order and node2vec for higher-order representations of drugs and diseases—thereby enhancing the identification of potential DDAs. Experimental results demonstrate that DRGDL outperforms existing methods, highlighting its effectiveness in drug repositioning by integrating biological knowledge and advanced graph learning algorithms. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Computational Biophysics & Chemistry is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1142/S2737416523410053
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        Text: English
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        PageCount: 13
        StartPage: 331
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Drug repositioning
        Type: general
      – SubjectFull: Drug discovery
        Type: general
      – SubjectFull: K-means clustering
        Type: general
      – SubjectFull: Knowledge graphs
        Type: general
      – SubjectFull: Deep learning
        Type: general
    Titles:
      – TitleFull: A Graph Deep Learning-Based Framework for Drug–Disease Association Identification with Chemical Structure Similarities.
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            NameFull: Zhao, Bo-Wei
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            NameFull: Su, Xiao-Rui
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            NameFull: Li, Dong-Xu
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            NameFull: Hu, Peng-Wei
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            – D: 01
              M: 04
              Text: Apr2025
              Type: published
              Y: 2025
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