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

Open Access

Issue		Natl Sci Open Volume 4, Number 4, 2025


Article Number		20240015
Number of page(s)		16
Section		Life Sciences and Medicine
DOI		https://doi.org/10.1360/nso/20240015
Published online		19 June 2025

Sun Y, Ma X, Hu H. Application of nano-drug delivery system based on cascade technology in cancer treatment. Int J Mol Sci 2021; 22: 5698. [Article] [Google Scholar]
Amber KT, Maglie R, Solimani F, et al. Targeted therapies for autoimmune bullous diseases: Current status. Drugs 2018; 78: 1527-1548. [Article] [Google Scholar]
Yu C, Liu X, Yang J, et al. Combination of immunotherapy with targeted therapy: Theory and practice in metastatic melanoma. Front Immunol 2019; 10: 990. [Article] [CrossRef] [PubMed] [Google Scholar]
DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ 2016; 47: 20-33. [Article] [CrossRef] [PubMed] [Google Scholar]
Aittokallio T. What are the current challenges for machine learning in drug discovery and repurposing?. Expert Opin Drug Discov 2022; 17: 423-425. [Article] [Google Scholar]
Pandey M, Fernandez M, Gentile F, et al. The transformational role of GPU computing and deep learning in drug discovery. Nat Mach Intell 2022; 4: 211-221. [Article] [Google Scholar]
Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell 2020; 180: 688-702.e13. [Article] [CrossRef] [PubMed] [Google Scholar]
Huang J, Xu Y, Xue Y, et al. Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences. Nat Biomed Eng 2023; 7: 797-810. [Article] [Google Scholar]
Gorgulla C, Boeszoermenyi A, Wang ZF, et al. An open-source drug discovery platform enables ultra-large virtual screens. Nature 2020; 580: 663-668. [Article] [Google Scholar]
Stecula A, Hussain MS, Viola RE. Discovery of novel inhibitors of a critical brain enzyme using a homology model and a deep convolutional neural network. J Med Chem 2020; 63: 8867-8875. [Article] [Google Scholar]
Li Y, Zhang L, Wang Y, et al. Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nat Commun 2022; 13: 6891. [Article] [Google Scholar]
Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 2019; 37: 1038-1040. [Article] [Google Scholar]
Deng D, Yang Y, Zou Y, et al. Discovery and evaluation of 3-quinoxalin urea derivatives as potent, selective, and orally available ATM inhibitors combined with chemotherapy for the treatment of cancer via goal-oriented molecule generation and virtual screening. J Med Chem 2023; 66: 9495-9518. [Article] [Google Scholar]
Segler MHS, Kogej T, Tyrchan C, et al. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 2018; 4: 120-131. [Article] [Google Scholar]
Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616: 673-685. [Article] [CrossRef] [PubMed] [Google Scholar]
Tan X, Li C, Yang R, et al. Discovery of pyrazolo[3,4-d]pyridazinone derivatives as selective DDR1 inhibitors via deep learning based design, synthesis, and biological evaluation. J Med Chem 2021; 65: 103-119. [Article] [Google Scholar]
Cai H, Zhang H, Zhao D, et al. FP-GNN: A versatile deep learning architecture for enhanced molecular property prediction. Brief Bioinform 2022; 23: bbac408. [Article] [Google Scholar]
Zhang Z, Liu Q, Wang H, et al. Motif-based graph self-supervised learning for molecular property prediction. Adv Neural Inf Process Syst 2021; 34: 15870–15882 [Google Scholar]
Rong Y, Bian Y, Xu T, et al. Self-supervised graph transformer on large-scale molecular data. Adv Neural Inf Process Syst 2020; 33: 12559–12571 [Google Scholar]
Shao J, Gong Q, Yin Z, et al. S2DV: Converting SMILES to a drug vector for predicting the activity of anti-HBV small molecules. Brief Bioinform 2022; 23: bbab593. [Article] [Google Scholar]
Zhang XC, Wu CK, Yang ZJ, et al. MG-BERT: Leveraging unsupervised atomic representation learning for molecular property prediction. Brief Bioinform 2021; 22: bbab152. [Article] [Google Scholar]
Xue D, Zhang H, Chen X, et al. X-MOL: Large-scale pre-training for molecular understanding and diverse molecular analysis. Sci Bull 2022; 67: 899-902. [Article] [Google Scholar]
Wang X, Zhao H, Tu W, et al. Automated 3d pre-training for molecular property prediction. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, 2023, 2419–2430 [Google Scholar]
Stärk H, Beaini D, Corso G, et al. 3D infomax improves GNNs for molecular property prediction. In: Proceedings of the 39th International Conference on Machine Learning. PMLR, 2022, 20479–20502 [Google Scholar]
Gallegos LC, Luchini G, St John PC, et al. Importance of engineered and learned molecular representations in predicting organic reactivity, selectivity, and chemical properties. Acc Chem Res 2021; 54: 827-836. [Article] [Google Scholar]
Zeng X, Xiang H, Yu L, et al. Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework. Nat Mach Intell 2022; 4: 1004-1016. [Article] [Google Scholar]
Li J, Telychko M, Yin J, et al. Machine vision automated chiral molecule detection and classification in molecular imaging. J Am Chem Soc 2021; 143: 10177-10188. [Article] [Google Scholar]
Pei Y, Wang G, Cao H, et al. A deep-learning pipeline to diagnose pediatric intussusception and assess severity during ultrasound scanning: A multicenter retrospective-prospective study. npj Digit Med 2023; 6: 182. [Article] [Google Scholar]
Zhang Z, Xie Y, Xing F, et al. Mdnet: A semantically and visually interpretable medical image diagnosis network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, 2017, 6428–6436 [Google Scholar]
Sousa T, Correia J, Pereira V, et al. Generative deep learning for targeted compound design. J Chem Inf Model 2021; 61: 5343-5361. [Article] [Google Scholar]
Born J, Manica M. Regression Transformer enables concurrent sequence regression and generation for molecular language modelling. Nat Mach Intell 2023; 5: 432-444. [Article] [Google Scholar]
Xie C, Zhuang XX, Niu Z, et al. Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow. Nat Biomed Eng 2022; 6: 76-93. [Article] [Google Scholar]
Diao Y, Liu D, Ge H, et al. Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery. Nat Commun 2023; 14: 4552. [Article] [Google Scholar]
Renz P, Van Rompaey D, Wegner JK, et al. On failure modes in molecule generation and optimization. Drug Discov Today Technol 2019; 32-33: 55-63. [Article] [Google Scholar]
Meyers J, Fabian B, Brown N. De novo molecular design and generative models. Drug Discov Today 2021; 26: 2707-2715. [Article] [Google Scholar]
Woodward DF, Jones RL, Narumiya S. International union of basic and clinical pharmacology. LXXXIII: Classification of prostanoid receptors, updating 15 years of progress. Pharmacol Rev 2011; 63: 471-538. [Article] [Google Scholar]
Lu W, Yu W, He J, et al. Reprogramming immunosuppressive myeloid cells facilitates immunotherapy for colorectal cancer. EMBO Mol Med 2021; 13: e12798. [Article] [Google Scholar]
Peng S, Hu P, Xiao YT, et al. Single-cell analysis reveals EP4 as a target for restoring T-cell infiltration and sensitizing prostate cancer to immunotherapy. Clin Cancer Res 2022; 28: 552-567. [Article] [Google Scholar]
Bonavita E, Bromley CP, Jonsson G, et al. Antagonistic inflammatory phenotypes dictate tumor fate and response to immune checkpoint blockade. Immunity 2020; 53: 1215-1229.e8. [Article] [Google Scholar]
Nakao K, Murase A, Ohshiro H, et al. CJ-023,423, a novel, potent and selective prostaglandin EP4 receptor antagonist with antihyperalgesic properties. J Pharmacol Exp Ther 2007; 322: 686-694. [Article] [Google Scholar]
Yang JJ, Yu WW, Hu LL, et al. Discovery and characterization of 1H-1,2,3-triazole derivatives as novel prostanoid EP4 receptor antagonists for cancer immunotherapy. J Med Chem 2019; 63: 569-590. [Article] [Google Scholar]
Blouin M, Han Y, Burch J, et al. The discovery of 4-{1-[({2,5-dimethyl-4-[4-(trifluoromethyl)benzyl]-3-thienyl}carbonyl)amino]cyclopropyl}benzoic Acid (MK-2894), a potent and selective prostaglandin E₂ subtype 4 receptor antagonist. J Med Chem 2010; 53: 2227-2238. [Article] [Google Scholar]
Cheng Z, Wang Y, Zhang Y, et al. Discovery of 2 H-indazole-3-carboxamide derivatives as novel potent prostanoid EP4 receptor antagonists for colorectal cancer immunotherapy. J Med Chem 2023; 66: 6218-6238. [Article] [Google Scholar]
Hong DS, Parikh A, Shapiro GI, et al. First-in-human phase I study of immunomodulatory E7046, an antagonist of PGE₂-receptor E-type 4 (EP4), in patients with advanced cancers. J Immunother Cancer 2020; 8: e000222. [Article] [Google Scholar]
Das D, Hong J. Prostaglandin E2 receptor 4 (EP4): A promising therapeutic target for the treatment of cancer and inflammatory diseases. Curr Chem Biol 2021; 15: 50-68. [Article] [Google Scholar]
Akbari B, Soltantoyeh T, Shahosseini Z, et al. PGE2-EP2/EP4 signaling elicits mesoCAR T cell immunosuppression in pancreatic cancer. Front Immunol 2023; 14: 1209572. [Article] [Google Scholar]
Liu X, Xiao T, Si S, et al. How does noise help robustness? Explanation and exploration under the neural SDE framework. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, 2020, 282–290 [Google Scholar]
Bishop CM. Training with noise is equivalent to Tikhonov regularization. Neural Computation 1995; 7: 108-116. [Article] [CrossRef] [Google Scholar]
Graves A, Graves A. Long short-term memory. In: Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence. Berlin, Heidelberg: Springer, 2012. 37–45 [Google Scholar]
Cho K, van Merrienboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. ArXiv:1406.1078 [Google Scholar]
ArunKumar KE, Kalaga DV, Mohan Sai Kumar C, et al. Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alex Eng J 2022; 61: 7585-7603. [Article] [Google Scholar]
Sherstinsky A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys Nonlinear Phenom 2020; 404: 132306. [Article] [Google Scholar]
ArunKumar KE, Kalaga DV, Kumar CMS, et al. Forecasting of COVID-19 using deep layer recurrent neural networks (RNNs) with gated recurrent units (GRUs) and long short-term memory (LSTM) cells. Chaos Solitons Fractals 2021; 146: 110861. [Article] [Google Scholar]
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, 2018, 7132–7141 [Google Scholar]
Li P, Wang J, Qiao Y, et al. An effective self-supervised framework for learning expressive molecular global representations to drug discovery. Brief Bioinform 2021; 22: bbab109. [Article] [Google Scholar]
Liu S, Wang H, Liu W, et al. Pre-training molecular graph representation with 3D geometry. ArXiv:2110.07728 [Google Scholar]
Wang Y, Wang J, Cao Z, et al. Molecular contrastive learning of representations via graph neural networks. Nat Mach Intell 2022; 4: 279-287. [Article] [Google Scholar]
Kim S, Chen J, Cheng T, et al. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res 2019; 47: D1102-D1109. [Article] [Google Scholar]
Probst D, Reymond JL. Visualization of very large high-dimensional data sets as minimum spanning trees. J Cheminform 2020; 12: 12. [Article] [Google Scholar]
Rogers D, Hahn M. Extended-connectivity fingerprints. J Chem Inf Model 2010; 50: 742-754. [Article] [Google Scholar]
Gilson MK, Liu T, Baitaluk M, et al. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 2016; 44: D1045-D1053. [Article] [Google Scholar]
Mendez D, Gaulton A, Bento AP, et al. ChEMBL: Towards direct deposition of bioassay data. Nucleic Acids Res 2019; 47: D930-D940. [Article] [Google Scholar]
Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. ArXiv:1609.02907 [Google Scholar]
Veličković P, Cucurull G, Casanova A, et al. Graph attention networks. ArXiv:1710.10903 [Google Scholar]
Gilmer J, Schoenholz SS, Riley PF, et al. Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning. PMLR, 2017, 1263–1272 [Google Scholar]
Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017; 7: 42717. [Article] [Google Scholar]
Yokoyama U, Iwatsubo K, Umemura M, et al. The prostanoid EP4 receptor and its signaling pathway. Pharmacol Rev 2013; 65: 1010-1052. [Article] [Google Scholar]
Fan F, Binkowski BF, Butler BL, et al. Novel genetically encoded biosensors using firefly luciferase. ACS Chem Biol 2008; 3: 346-351. [Article] [Google Scholar]
Toyoda Y, Morimoto K, Suno R, et al. Ligand binding to human prostaglandin E receptor EP4 at the lipid-bilayer interface. Nat Chem Biol 2019; 15: 18-26. [Article] [Google Scholar]
Cilenti F, Barbiera G, Caronni N, et al. A PGE2-MEF2A axis enables context-dependent control of inflammatory gene expression. Immunity 2021; 54: 1665-1682.e14. [Article] [Google Scholar]
Na YR, Jung D, Stakenborg M, et al. Prostaglandin E₂ receptor PTGER4-expressing macrophages promote intestinal epithelial barrier regeneration upon inflammation. Gut 2021; 70: 2249-2260. [Article] [Google Scholar]
Gunassekaran GR, Poongkavithai Vadevoo SM, Baek MC, et al. M1 macrophage exosomes engineered to foster M1 polarization and target the IL-4 receptor inhibit tumor growth by reprogramming tumor-associated macrophages into M1-like macrophages. Biomaterials 2021; 278: 121137. [Article] [Google Scholar]
Wu Y, Yang S, Ma J, et al. Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov 2022; 12: 134-153. [Article] [Google Scholar]
Gonda TA, Fang J, Salas M, et al. A DNA hypomethylating drug alters the tumor microenvironment and improves the effectiveness of immune checkpoint inhibitors in a mouse model of pancreatic cancer. Cancer Res 2020; 80: 4754-4767. [Article] [Google Scholar]
Molgora M, Esaulova E, Vermi W, et al. TREM2 modulation remodels the tumor myeloid landscape enhancing anti-PD-1 immunotherapy. Cell 2020; 182: 886-900.e17. [Article] [Google Scholar]
Ghorbani M, Gendelev L, Beroza P, et al. Autoregressive fragment-based diffusion for pocket-aware ligand design. ArXiv:2401.05370 [Google Scholar]
Zhang O, Zhang J, Jin J, et al. ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling. Nat Mach Intell 2023; 5: 1020-1030. [Article] [Google Scholar]
Chan L, Kumar R, Verdonk M, et al. A multilevel generative framework with hierarchical self-contrasting for bias control and transparency in structure-based ligand design. Nat Mach Intell 2022; 4: 1130-1142. [Article] [Google Scholar]
Campbell Q, Cox S, Medina J, et al. MDCrow: Automating molecular dynamics workflows with large language models. ArXiv:2502.09565 [Google Scholar]
Wang J, Hsieh CY, Wang M, et al. Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning. Nat Mach Intell 2021; 3: 914-922. [Article] [Google Scholar]
Yu J, Zheng Y, Koh HY, et al. Collaborative expert LLMs guided multi-objective molecular optimization. ArXiv:2503.03503 [Google Scholar]

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