A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery
Abstract Discovering therapeutic molecules requires the integration of both phenotype‐based drug discovery (PDD) and target‐based drug discovery (TDD). However, this integration remains challenging due to the inherent heterogeneity, noise, and bias present in biomedical data. In this study, Knowledg...
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Wiley
2025-04-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202412402 |
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| author | Qing Ye Yundian Zeng Linlong Jiang Yu Kang Peichen Pan Jiming Chen Yafeng Deng Haitao Zhao Shibo He Tingjun Hou Chang‐Yu Hsieh |
| author_facet | Qing Ye Yundian Zeng Linlong Jiang Yu Kang Peichen Pan Jiming Chen Yafeng Deng Haitao Zhao Shibo He Tingjun Hou Chang‐Yu Hsieh |
| author_sort | Qing Ye |
| collection | DOAJ |
| description | Abstract Discovering therapeutic molecules requires the integration of both phenotype‐based drug discovery (PDD) and target‐based drug discovery (TDD). However, this integration remains challenging due to the inherent heterogeneity, noise, and bias present in biomedical data. In this study, Knowledge‐Guided Drug Relational Predictor (KGDRP), a graph representation learning approach is developed that effectively integrates multimodal biomedical data, including network data containing biological system information, gene expression data, and sequence data that incorporates chemical molecular structures, all within a heterogeneous graph (HG) structure. By incorporating biomedical HG (BioHG) into a heterogeneous graph neural network (HGNN)‐based architecture, KGDRP exhibits a remarkable 12% improvement compared to previous methods in real‐world screening scenarios. Notably, the biology‐informed representation, derived from KGDRP, significantly enhance target prioritization by 26% in drug target discovery. Furthermore, zero‐shot evaluation on COVID‐19 exhibited a notably higher success rate in identifying diverse potential drugs. The utilization of BioHG facilitates a unique KGDRP‐based analysis of cell‐target‐drug interactions, thereby enabling the elucidation of drug mechanisms. Overall, KGDRP provides a robust infrastructure for the seamlessly integration of multimodal data and biomedical networks, effectively accelerating PDD, guiding therapeutic target discovery, and ultimately expediting therapeutic molecule discovery. |
| format | Article |
| id | doaj-art-be9a8130eee44faeba61b025066cece1 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-be9a8130eee44faeba61b025066cece12025-08-20T02:24:50ZengWileyAdvanced Science2198-38442025-04-011216n/an/a10.1002/advs.202412402A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug DiscoveryQing Ye0Yundian Zeng1Linlong Jiang2Yu Kang3Peichen Pan4Jiming Chen5Yafeng Deng6Haitao Zhao7Shibo He8Tingjun Hou9Chang‐Yu Hsieh10College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang 310027 ChinaCollege of Control Science and Engineering Zhejiang University Hangzhou Zhejiang 310027 ChinaCollege of Pharmaceutical Sciences Zhejiang University Hangzhou Zhejiang 310058 ChinaCollege of Pharmaceutical Sciences Zhejiang University Hangzhou Zhejiang 310058 ChinaCollege of Pharmaceutical Sciences Zhejiang University Hangzhou Zhejiang 310058 ChinaCollege of Control Science and Engineering Zhejiang University Hangzhou Zhejiang 310027 ChinaCarbonSilicon AI Technology Co., Ltd Hangzhou Zhejiang 310018 ChinaCenter for Intelligent and Biomimetic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong 440305 ChinaCollege of Control Science and Engineering Zhejiang University Hangzhou Zhejiang 310027 ChinaCollege of Pharmaceutical Sciences Zhejiang University Hangzhou Zhejiang 310058 ChinaCollege of Pharmaceutical Sciences Zhejiang University Hangzhou Zhejiang 310058 ChinaAbstract Discovering therapeutic molecules requires the integration of both phenotype‐based drug discovery (PDD) and target‐based drug discovery (TDD). However, this integration remains challenging due to the inherent heterogeneity, noise, and bias present in biomedical data. In this study, Knowledge‐Guided Drug Relational Predictor (KGDRP), a graph representation learning approach is developed that effectively integrates multimodal biomedical data, including network data containing biological system information, gene expression data, and sequence data that incorporates chemical molecular structures, all within a heterogeneous graph (HG) structure. By incorporating biomedical HG (BioHG) into a heterogeneous graph neural network (HGNN)‐based architecture, KGDRP exhibits a remarkable 12% improvement compared to previous methods in real‐world screening scenarios. Notably, the biology‐informed representation, derived from KGDRP, significantly enhance target prioritization by 26% in drug target discovery. Furthermore, zero‐shot evaluation on COVID‐19 exhibited a notably higher success rate in identifying diverse potential drugs. The utilization of BioHG facilitates a unique KGDRP‐based analysis of cell‐target‐drug interactions, thereby enabling the elucidation of drug mechanisms. Overall, KGDRP provides a robust infrastructure for the seamlessly integration of multimodal data and biomedical networks, effectively accelerating PDD, guiding therapeutic target discovery, and ultimately expediting therapeutic molecule discovery.https://doi.org/10.1002/advs.202412402biological networksdrug target discoverygraph representation learningphenotypic screeningtranscriptomics |
| spellingShingle | Qing Ye Yundian Zeng Linlong Jiang Yu Kang Peichen Pan Jiming Chen Yafeng Deng Haitao Zhao Shibo He Tingjun Hou Chang‐Yu Hsieh A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery Advanced Science biological networks drug target discovery graph representation learning phenotypic screening transcriptomics |
| title | A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery |
| title_full | A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery |
| title_fullStr | A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery |
| title_full_unstemmed | A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery |
| title_short | A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery |
| title_sort | knowledge guided graph learning approach bridging phenotype and target based drug discovery |
| topic | biological networks drug target discovery graph representation learning phenotypic screening transcriptomics |
| url | https://doi.org/10.1002/advs.202412402 |
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