Discovery of EP4 antagonists with image-guided explainable deep learning workflow
In target-based drug design, the manual creation of a poor initial compound library, the time-consuming wet-laboratory experimental screening method, and the weak explainability of their activity against compounds significantly limit the efficiency of discovering novel therapeutics. Here we propose...
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| Format: | Article |
| Language: | English |
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Science Press
2025-06-01
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| Series: | National Science Open |
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| Online Access: | https://www.sciengine.com/doi/10.1360/nso/20240015 |
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| author | Ma Pengsen Cheng Zhiyuan Cheng Zhixiang Wang Yijie Chai Xiaolei Feng Bo Xiang Hongxin Zeng Li Liu Xueming Li Pengyong Wei Leyi Zou Quan Liu Mingyao Zeng Xiangxiang Lu Weiqiang |
| author_facet | Ma Pengsen Cheng Zhiyuan Cheng Zhixiang Wang Yijie Chai Xiaolei Feng Bo Xiang Hongxin Zeng Li Liu Xueming Li Pengyong Wei Leyi Zou Quan Liu Mingyao Zeng Xiangxiang Lu Weiqiang |
| author_sort | Ma Pengsen |
| collection | DOAJ |
| description | In target-based drug design, the manual creation of a poor initial compound library, the time-consuming wet-laboratory experimental screening method, and the weak explainability of their activity against compounds significantly limit the efficiency of discovering novel therapeutics. Here we propose an image-guided, interpretability deep learning workflow, named LeadDisFlow, to enable rapid, accurate target drug discovery. Using LeadDisFlow, we identified four potent antagonists with single-nanomolar antagonistic activity against PGE<sub>2</sub> receptor subtype 4 (EP4), a promising target for tumor immunotherapy. Remarkably, the most potent EP4 antagonist, ZY001, demonstrated an IC<sub>50</sub> value of (0.51 ± 0.02) nM, along with high selectivity. Furthermore, ZY001 effectively impaired the PGE<sub>2</sub>-induced gene expression of a panel of immunosuppressive molecules in macrophages. The workflow facilitates the discovery of potent EP4 antagonists that enhance anti-tumor immune response, and provides a convenient and quick approach to discover promising therapeutics for a specific drug target. |
| format | Article |
| id | doaj-art-32d6dfa1452346fbb794378db610c620 |
| institution | DOAJ |
| issn | 2097-1168 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Science Press |
| record_format | Article |
| series | National Science Open |
| spelling | doaj-art-32d6dfa1452346fbb794378db610c6202025-08-20T03:11:58ZengScience PressNational Science Open2097-11682025-06-01410.1360/nso/20240015eb33e642Discovery of EP4 antagonists with image-guided explainable deep learning workflowMa Pengsen0Cheng Zhiyuan1Cheng Zhixiang2Wang Yijie3Chai Xiaolei4Feng Bo5Xiang Hongxin6Zeng Li7Liu Xueming8Li Pengyong9Wei Leyi10Zou Quan11Liu Mingyao12Zeng Xiangxiang13Lu Weiqiang14["College of Information Science and Engineering, Hunan University, Changsha 410082, China"]["Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, Shanghai 200241, China"]["College of Information Science and Engineering, Hunan University, Changsha 410082, China"]["Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, Shanghai 200241, China"]["Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, Shanghai 200241, China"]["Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China"]["College of Information Science and Engineering, Hunan University, Changsha 410082, China"]["Department of AIDD, Shanghai Yuyao Biotechnology Co., Ltd., Shanghai 201109, China"]["Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China"]["School of Computer Science and Technology, Xidian University, Xi’an 710071, China"]["School of Software, Shandong University, Jinan 250000, China"]["Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China"]["Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, Shanghai 200241, China","Department of AIDD, Shanghai Yuyao Biotechnology Co., Ltd., Shanghai 201109, China","Hainan Academy of Medical Sciences, Hainan Medical University, Haikou 570311, China"]["College of Information Science and Engineering, Hunan University, Changsha 410082, China"]["Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, Shanghai 200241, China","Hainan Academy of Medical Sciences, Hainan Medical University, Haikou 570311, China"]In target-based drug design, the manual creation of a poor initial compound library, the time-consuming wet-laboratory experimental screening method, and the weak explainability of their activity against compounds significantly limit the efficiency of discovering novel therapeutics. Here we propose an image-guided, interpretability deep learning workflow, named LeadDisFlow, to enable rapid, accurate target drug discovery. Using LeadDisFlow, we identified four potent antagonists with single-nanomolar antagonistic activity against PGE<sub>2</sub> receptor subtype 4 (EP4), a promising target for tumor immunotherapy. Remarkably, the most potent EP4 antagonist, ZY001, demonstrated an IC<sub>50</sub> value of (0.51 ± 0.02) nM, along with high selectivity. Furthermore, ZY001 effectively impaired the PGE<sub>2</sub>-induced gene expression of a panel of immunosuppressive molecules in macrophages. The workflow facilitates the discovery of potent EP4 antagonists that enhance anti-tumor immune response, and provides a convenient and quick approach to discover promising therapeutics for a specific drug target.https://www.sciengine.com/doi/10.1360/nso/20240015drug discoveryPGE<sub>2</sub> receptor subtype 4antagonistdeep learningcomputer vision |
| spellingShingle | Ma Pengsen Cheng Zhiyuan Cheng Zhixiang Wang Yijie Chai Xiaolei Feng Bo Xiang Hongxin Zeng Li Liu Xueming Li Pengyong Wei Leyi Zou Quan Liu Mingyao Zeng Xiangxiang Lu Weiqiang Discovery of EP4 antagonists with image-guided explainable deep learning workflow National Science Open drug discovery PGE<sub>2</sub> receptor subtype 4 antagonist deep learning computer vision |
| title | Discovery of EP4 antagonists with image-guided explainable deep learning workflow |
| title_full | Discovery of EP4 antagonists with image-guided explainable deep learning workflow |
| title_fullStr | Discovery of EP4 antagonists with image-guided explainable deep learning workflow |
| title_full_unstemmed | Discovery of EP4 antagonists with image-guided explainable deep learning workflow |
| title_short | Discovery of EP4 antagonists with image-guided explainable deep learning workflow |
| title_sort | discovery of ep4 antagonists with image guided explainable deep learning workflow |
| topic | drug discovery PGE<sub>2</sub> receptor subtype 4 antagonist deep learning computer vision |
| url | https://www.sciengine.com/doi/10.1360/nso/20240015 |
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