摘要
In target-based drug design,the manual creation of a poor initial compound library,the time-consuming wetlaboratory 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 PGE2 receptor subtype 4(EP4),a promising target for tumor im-munotherapy.Remarkably,the most potent EP4 antagonist,ZY001,demonstrated an IC50 value of(0.51±0.02)nM,along with high selectivity.Furthermore,ZY001 effectively impaired the PGE2-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.
基金
supported by the National Natural Science Foundation of China (62425204, 62122025, U22A2037, 62450002,62432011, 62250028, 81972828, 82172644, and 81830083)
Hunan Provincial Natural Science Foundation of China(2021JJ10020)
National Key Scientific Infrastructure for Translational Medicine (Shanghai)(TMSK-2021-120)
ECNU Multifunctional Platform for Innovation (011)
