Protein Lysine Methyltransferase G9a Inhibitors: Design, Synthesis, and Structure Activity Relationships of 2,4-Diamino-7-aminoalkoxy-quinazolines.
Bibliographic record
Abstract
Protein lysine methyltransferase G9a, which catalyzes methylation of lysine 9 of histone H3 (H3K9) and lysine 373 (K373) of p53, is overexpressed in human cancers. Genetic knockdown of G9a inhibits cancer cell growth, and the dimethylation of p53 K373 results in the inactivation of p53. Initial SAR exploration of the 2,4-diamino-6,7-dimethoxyquinazoline template represented by 3a (BIX01294), a selective small molecule inhibitor of G9a and GLP, led to the discovery of 10 (UNC0224) as a potent G9a inhibitor with excellent selectivity. A high resolution X-ray crystal structure of the G9a-10 complex, the first cocrystal structure of G9a with a small molecule inhibitor, was obtained. On the basis of the structural insights revealed by this cocrystal structure, optimization of the 7-dimethylaminopropoxy side chain of 10 resulted in the discovery of 29 (UNC0321) (Morrison K(i) = 63 pM), which is the first G9a inhibitor with picomolar potency and the most potent G9a inhibitor to date.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".