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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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Press
2025-06-01
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| Series: | National Science Open |
| Subjects: | |
| Online Access: | https://www.sciengine.com/doi/10.1360/nso/20240015 |
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| Summary: | 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. |
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| ISSN: | 2097-1168 |