RESEARCH ARTICLE

Discovery of EP4 antagonists with image-guided explainable deep learning workflow

¹ College of Information Science and Engineering, Hunan University, Changsha 410082, China
² Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, Shanghai 200241, China
³ Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
⁴ Department of AIDD, Shanghai Yuyao Biotechnology Co., Ltd., Shanghai 201109, China
⁵ Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
⁶ School of Computer Science and Technology, Xidian University, Xi’an 710071, China
⁷ School of Software, Shandong University, Jinan 250000, China
⁸ Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
⁹ Hainan Academy of Medical Sciences, Hainan Medical University, Haikou 570311, China

^* Corresponding authors (emails: xzeng@hnu.edu.cn (Xiangxiang Zeng); wqlu@bio.ecnu.edu.cn (Weiqiang Lu))

Received: 30 August 2024
Revised: 25 March 2025
Accepted: 25 May 2025

Abstract

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 PGE₂ receptor subtype 4 (EP4), a promising target for tumor immunotherapy. 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.