Back-pocket optimization of 2-aminopyrimidine-based macrocycles leads to potent dual EPHA2/GAK kinase inhibitors with antiviral activity
Bibliographic record
Abstract
Macrocyclization of acyclic compounds is a powerful strategy for improving inhibitor potency and selectivity. Here, we developed a 2-aminopyrimidine-based macrocyclic dual EPHA2/GAK kinase inhibitor as a chemical tool to study the role of these two kinases in viral entry and assembly. Starting with a promiscuous macrocyclic inhibitor, 6, we performed a structure-guided activity relationship and selectivity study using a panel of over 100 kinases. The crystal structure of EPHA2 in complex with the developed macrocycle 23 provided a basis for further optimization by specifically targeting the back pocket, resulting in compound 55 as a potent dual EPHA2/GAK inhibitor. Subsequent front-pocket derivatization resulted in an interesting in cellulo selectivity profile, favoring EPHA4 over the other ephrin receptor kinase family members. The dual EPHA2/GAK inhibitor 55 prevented dengue virus infection of Huh7 liver cells, mainly via its EPHA2 activity, and is therefore a promising candidate for further optimization of its activity against dengue virus.
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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) | 0.000 | 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) | 0.001 | 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 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".