Controlling the Covalent Reactivity of a Kinase Inhibitor with Light
Bibliographic record
Abstract
Covalent kinase inhibitors account for some of the most successful drugs that have recently entered the clinic and many others are in preclinical development. A common strategy is to target cysteines in the vicinity of the ATP binding site using an acrylamide electrophile. To increase the tissue selectivity of kinase inhibitors, it could be advantageous to control the reactivity of these electrophiles with light. Here, we introduce covalent inhibitors of the kinase JNK3 that function as photoswitchable affinity labels (PALs). Our lead compounds contain a diazocine photoswitch, are poor non-covalent inhibitors in the dark, and become effective covalent inhibitors after irradiation with visible light. Our proposed mode of action is supported by X-ray structures that explain why these compounds are unreactive in the dark and undergo proximity-based covalent attachment following exposure to light.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".