DNAR-05. Discovery and development of a novel CNS-penetrating ATR inhibitor
Bibliographic record
Abstract
Abstract Ataxia telangiectasia and Rad3-related protein serine/threonine kinase (ATR) is a key enzyme in the regulation of DNA damage repair, making it a compelling therapeutic target for the treatment of many cancer indications. A number of ATR inhibitors have been developed to date and have shown promising effects on solid tumors, both as monotherapies and as combination therapies with chemotherapy, radiotherapy, and immunotherapy. ATR inhibitors that are currently in clinical development demonstrate low penetrance through the blood-brain barrier (BBB) into the central nervous system (CNS), making them sub-optimal therapeutic options for targeting tumors and metastases in the brain. Discovery and development of novel potent, CNS-penetrating selective ATR inhibitors could provide a new therapeutic approach for patients with brain tumors, who otherwise have very few treatment options. Here, we describe the use of the Enki™ platform, a generative artificial intelligence approach, to discover novel CNS-penetrating ATR inhibitors and our progress in the development of these inhibitors. The Enki™ platform utilizes generative AI and deep learning to search a broad chemical space to identify hits. It uses a latent diffusion model to optimize many properties simultaneously within the search space, including maximal potency against the primary target, selectivity, ADMET, and physicochemical properties. This platform was used to generate de novo molecules with optimized properties to penetrate the blood-brain barrier and specifically target ATR. The most promising of these molecules were synthesized. In vitro and in vivo data, including potency, selectivity, BBB permeability and metabolic stability for these compounds will be presented. Use of the Enki™ platform allows deeper exploration into chemical space and refinement of drug properties while also accelerating the drug development timeline. This has enabled our discovery of numerous ATR inhibitor candidates with selectivity and efficacy against ATR, as well as CNS-penetrance.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".