Identification of Pyrimidine-Based Lead Compounds for Understudied Kinases Implicated in Driving Neurodegeneration
Bibliographic record
Abstract
The pyrimidine core has been utilized extensively to construct kinase inhibitors, including eight FDA-approved drugs. Because the pyrimidine hinge-binding motif is accommodated by many human kinases, kinome-wide selectivity of resultant molecules can be poor. This liability was seen as an advantage since it is well tolerated by many understudied kinases. We hypothesized that nonexemplified aminopyrimidines bearing side chains from well-annotated pyrimidine-based inhibitors with off-target activity on understudied kinases would provide us with useful inhibitors of these lesser studied kinases. Our strategy paired mixing and matching the side chains from the 2- and 4-positions of the parent compounds with modifications at the 5-position of the pyrimidine core, which is situated near the gatekeeper residue of the binding pocket. Utilizing this approach, we imparted improved kinome-wide selectivity to most members of the resultant library. Importantly, we also identified potent biochemical and cell-active lead compounds for understudied kinases like DRAK1, BMP2K, and MARK3/4.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".