Abstract C116: Novel ATR inhibitors with CNS penetrance developed by artificial intelligence
Bibliographic record
Abstract
Abstract DNA damage repair (DDR) enzymes, including Ataxia telangiectasia and Rad3-related protein serine/threonine kinase (ATR), are crucial for maintaining genomic stability in cells. Inhibition of DDR enzymes has proven to be a compelling therapeutic option for numerous cancer indications that can lead to cell death through replication stress or mitotic catastrophe. ATR inhibitors have shown promising effects on solid tumors, both as monotherapies and in combination with chemotherapy, radiotherapy, or immunotherapy. However, they lack the ability to effectively penetrate through the blood-brain barrier (BBB) into the central nervous system (CNS), making them suboptimal for treatment of brain tumors and metastases. Using a generative artificial intelligence approach called the Enki™ platform for drug discovery, we are developing a novel, potent, CNS-penetrating, and selective ATR inhibitor. The Enki™ platform generates candidate compounds using a generative AI and deep learning approach to search and optimize compound properties in a broad chemical space, including potency, target specificity, physicochemical properties, and ADMET characteristics. This platform was used to create a shortlist of de novo compounds with properties to selectively target ATR and penetrate into the CNS. The most promising molecules from this list were synthesized and characterized in vitro and in vivo. Data on the potency, selectivity, CNS penetrance and metabolic stability of select inhibitors will be presented. In summary, the Enki™ platform has enabled accelerated drug discovery timelines and deep exploration into expanded chemical spaces, allowing identification of ATR inhibitor candidates with BBB penetrance into the CNS. Citation Format: Sarah Truong, Beibei Zhai, Louise Ramos, Mona Marzban, Fariba Ghaidi, Marshall Drew-Brook, Pete Guzzo, Ahmad Issa, Mehran Khodabandeh, Sara Omar, Jason Rolfe, Seyed Ali Saberali, Kally Singh, Xiaoqi Chen, Dennis Brown, John Langlands, Jeffrey Bacha, Mads Daugaard. Novel ATR inhibitors with CNS penetrance developed by artificial intelligence [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C116.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".