Discovery of a Potent and Selective Coactivator Associated Arginine Methyltransferase 1 (CARM1) Inhibitor by Virtual Screening
Bibliographic record
Abstract
Protein arginine methyltransferases (PRMTs) represent an emerging target class in oncology and other disease areas. So far, the most successful strategy to identify PRMT inhibitors has been to screen large to medium-size chemical libraries. Attempts to develop PRMT inhibitors using receptor-based computational methods have met limited success. Here, using virtual screening approaches, we identify 11 CARM1 (PRMT4) inhibitors with ligand efficiencies ranging from 0.28 to 0.84. CARM1 selective hits were further validated by orthogonal methods. Two structure-based rounds of optimization produced 27 (SGC2085), a CARM1 inhibitor with an IC50 of 50 nM and more than hundred-fold selectivity over other PRMTs. These results indicate that virtual screening strategies can be successfully applied to Rossmann-fold protein methyltransferases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".