Abstract A012: AI-assisted Design of Novel NRF2Mut Inhibitors
Bibliographic record
Abstract
Abstract Nuclear factor erythroid 2-related factor 2 (NRF2) is a master transcription factor that plays a central role in orchestrating cellular responses to oxidative and electrophilic stress. NRF2 is regulated by Kelch-like ECH-associated protein 1 (KEAP1), which promotes NRF2 ubiquitination and proteasomal degradation. However, in approximately 2% of human cancers, NRF2 becomes hyperactive due to mutations, leading to uncontrolled NRF2 signaling. This aberrant activation drives chemo- and radioresistance, metabolic reprogramming, and tumor progression, and is associated with poor clinical outcomes. In our previous work, we discovered pyrimethamine (PYR), an FDA-approved antimalarial drug, as a potent NRF2 inhibitor. We validated PYR’s efficacy in NRF2Mut esophageal squamous cell carcinoma (ESCC) cells in vitro and a Nrf2E79Q-driven esophageal phenotype mouse model in vivo. Mechanistically, PYR acts as a dual-function compound: it serves as a molecular glue that facilitates the interaction between NRF2Mut and KEAP1, resulting in enhanced proteasomal degradation, and also functions as a dihydrofolate reductase (DHFR) inhibitor. Encouraged by these findings, PYR has advanced into a Phase I “window of opportunity” clinical trial (NCT05678348). In the current study, we aimed to further understand the molecular glue mechanism of PYR and explore additional NRF2-targeting compounds. We identified triamterene (TRM), a potassium-sparing diuretic, as a second NRF2 inhibitor that similarly promotes the degradation of NRF2W24C by enhancing KEAP1 binding and increasing ubiquitination. Both PYR and TRM significantly reduced the half-life of NRF2W24C. Computational modeling showed that both compounds dock within KEAP1’s Kelch domain, increasing protein-protein interactions and lowering the binding energy between KEAP1 and NRF2Mut. A structure-activity relationship analysis using seven TRM analogs further supported the importance of the Kelch pocket in TRM-induced NRF2 degradation. To develop novel NRF2 inhibitors, we utilized Chemistry42, an AI-driven drug discovery platform, to design compounds targeting the Kelch pocket. Of the four compounds synthesized, one—designated X30—showed strong activity in NRF2Mut ESCC cells but not in NRF2WT cells. Western blot assays confirmed X30-induced proteasomal degradation of NRF2W24C via KEAP1 interaction. Importantly, this effect was lost in KEAP1KO ESCC cells, validating the KEAP1 dependency of X30 and TRM. Notably, unlike PYR, X30 does not act through DHFR inhibition, suggesting it operates via a distinct and KEAP1-dependent mode of action. In summary, X30 represents an entirely new chemical scaffold with no prior reports in the literature and very limited structural similarity to known bioactive compounds. It was exclusively generated using the AI-driven platform Chemistry42, showcasing the creative potential of AI in developing selective NRF2Mut inhibitors. Further evaluation of additional AI-designed compounds is underway to advance therapeutic strategies against NRF2Mut cancers. Citation Format: Zhaohui Xiong, Zachary Ladd, Candice Bui-Linh, Francis Spitz, Haining Wang, Xiaoxin Luke. Chen. AI-assisted Design of Novel NRF2Mut Inhibitors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A012.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".