Latest Nature Communications Study: AI-Enabled Development of Next-Generation ENPP1 Inhibitors for Innate Immune Modulation by Insilico Medicine
Latest Nature Communications Study: AI-Enabled Development of Next-Generation ENPP1 Inhibitors for Innate Immune Modulation by Insilico Medicine
Insilico Medicine (“Insilico”), a clinical-stage generative artificial intelligence (AI)-driven biotechnology company, recently unveiled a groundbreaking study targeting ENPP1 to develop small molecule inhibitors that effectively modulate the STING pathway and enhance tumor immunity. Published in Nature Communications, the study showcases the leveraging of Insilico's advanced generative AI platform and integrated workflow which identifies and validates ENPP1 as a critical immune checkpoint among multiple solid tumors and assists in developing a highly specific oral ENPP1 inhibitor, ISM5939, for immunotherapy.
R&D roadmap of ENPP1 program
In the field of cancer immunotherapy, the activation of the STING (Stimulator of Interferon Genes) pathway is considered an effective strategy to enhance anti-tumor immune responses. However, current clinical validations of direct STING agonists face two major challenges: First, STING agonists require intertumoral injection, resulting in low bioavailability and making them difficult to use for widely metastatic tumors. Second, STING agonists induce systemic inflammatory responses and T cell apoptosis, which limits their clinical efficacy. To overcome these challenges, Insilico Medicine chose to focus on targeting ENPP1 (ectonucleotide pyrophosphatase/phosphodiesterase 1) as a breakthrough approach.
ENPP1 plays a key role in various essential physiological processes, such as cardiovascular, neurological, and immune regulation, and is highly expressed in multiple types of tumors. Research has shown that ENPP1 is closely associated with tumor metastasis, immune evasion, and poor prognosis in malignant tumors. Mechanistically, ENPP1 degrades cellular cyclic GMP-AMP (cGAMP), thereby blocking activation of the STING pathway and suppressing anti-tumor immune activity in the tumor microenvironment. Therefore, targeting ENPP1 is expected to precisely modulate the STING signaling pathway within tumor tissue, restore local immune activation, and enhance anti-tumor immune responses. This provides a novel and promising strategy for cancer immunotherapy.
Mechanism of ENPP1 Regulates the STING Pathway
In this published study, the research team utilized Insilico's AI-driven target discovery, PandaOmics, alongside patient data from TCGA, to screen and rank indications impacted by ENPP1 inhibitors and identified specific key cancer types impacted by these inhibitors, including triple-negative breast cancer (TNBC), hepatocellular carcinoma (liver cancer), acute myeloid leukemia, ovarian cancer, colorectal adenocarcinoma, breast cancer, head and neck cancer, and ER-negative breast cancer.
Using single-cell sequencing data and spatial transcriptomics, they further confirmed that elevated ENPP1 expression is associated with an immunosuppressive tumor microenvironment. Additionally, bioinformatics analysis of clinical cohorts suggested promising strategies combining ENPP1 inhibitors with existing immune checkpoint inhibitors and DNA-damaging chemotherapy.
Researchers then leveraged Chemistry42, Insilico's generative chemistry AI-based drug design engine to facilitate the design of novel ENPP1 inhibitors. Starting with known ENPP1 inhibitors, the researchers employed the structure-based drug design approach of Chemistry42 to generate new compounds from scratch and efficiently obtain hit molecules within 3 months.
The Hit Series were subsequently optimized through Chemistry42’s integrated features, including Alchemistry, to prioritize the compounds with lower calculated binding energy, and ADMET prediction modules, hERG liability, and ENPP1 inhibition. Through iterative refinement, ISM5939 emerged as a promising compound with drug-like properties, demonstrating high selectivity and potency for ENPP1 inhibition.
Generate and optimize ISM5939 using Chemistry42
Preclinical data indicates that ISM5939 functions effectively in combination with multiple therapies, potentiating the effects of existing cancer treatments by modulating immune responses and enhancing therapeutic efficacy.
When combined with anti-PD-1 therapy, ISM5939 synergistically amplifies T-cell activity and boosts antitumor immunity. In combination with chemotherapy, ISM5939 increases cGAMP accumulation in the tumor microenvironment, thereby activating the STING pathway in antigen-presenting cells (APCs) and improving chemotherapy efficacy. Similarly, when used alongside PARP inhibitors, ISM5939 further enhances STING activation, driving stronger antitumor immune responses. Moreover, ISM5939 exhibits a higher safety margin compared with direct STING agonists, with no significant induction of pro-inflammatory cytokines in the peripheral blood and does not trigger the death of effector T cells within the tumor microenvironment.
“This is our third Nature Portfolio journal paper published this year,” said Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine. “I am pleased to see our team’s research once again recognized by a leading academic journal. The discovery and design process of ISM5939 demonstrates the potential of AI-powered drug discovery technology and workflows to overcome the challenges of traditional drug development. By targeting ENPP1, we are paving the way for safer and more effective cancer treatments.”
“In this study, Insilico Medicine fully showcased the deep integration of biology, computational science, and AI-driven drug discovery and design, providing entirely new possibilities for cancer immunotherapy,” said Feng Ren, Co-CEO and Chief Scientific Officer of Insilico Medicine. “We hope the publication of the ISM5939 discovery process in Nature Communications will inspire the industry, accelerate the discovery of next-generation innovative drugs, unleash the potential of STING-targeted therapies, and bring more new options to immunotherapy.”
Since 2024, Insilico has published five AI drug pipeline-related papers in Nature Portfolio journals. Among them, two studies published in Nature Biotechnology in March and December 2024 reported small-molecule inhibitors: Rentosertib targeting TNIK for idiopathic pulmonary fibrosis, and ISM5411 targeting PHD1/2 for inflammatory bowel disease, respectively. In addition, in January 2025, Insilico, in collaboration with the University of Toronto, published a study in Nature Biotechnology on the design of novel KRAS inhibitors using a quantum-classical hybrid model.
By integrating advanced AI and automation technologies, Insilico Medicine has demonstrated significant efficiency improvements in practical applications, setting a benchmark for AI-driven drug research and development. Compared to the typical 2.5–4 years required in traditional drug discovery, Insilico’s 22 nominated candidate drugs from 2021 to 2024 took only 12–18 months on average to progress from project initiation to nomination of preclinical candidates (PCCs), with each project requiring synthesis and testing of only about 60–200 molecules. The success rate from PCC to IND-enabling stage reached 100%.
[1] Pu, C., Cui, H., Yu, H. et al. Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors. Nat Commun 16, 4793 (2025). https://doi.org/10.1038/s41467-025-59874-0
About Insilico Medicine
Insilico Medicine, a global clinical stage biotechnology company powered by generative AI, is connecting biology, chemistry, medicine and science research using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques for novel target discovery and the generation of novel molecular structures with desired properties. Insilico Medicine is developing breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, central nervous system diseases, infectious diseases, autoimmune diseases, and aging-related diseases. www.insilico.com