Selective Inhibitors of Human Neuraminidase 3
Bibliographic record
Abstract
Human neuraminidases (NEU) are associated with human diseases including cancer, atherosclerosis, and diabetes. To obtain small molecule inhibitors as research tools for the study of their biological functions, we designed a library of 2-deoxy-2,3-didehydro- N -acetylneuraminic acid (DANA) analogues with modifications at C4 and C9 positions. This library allowed us to discover selective inhibitors targeting the human NEU3 isoenzyme. Our most selective inhibitor for NEU3 has a K i of 320 ± 40 nM and a 15-fold selectivity over other human neuraminidase isoenzymes. This inhibitor blocks glycolipid processing by NEU3 in vitro. To improve their pharmacokinetic properties, various esters of the best inhibitors were synthesized and evaluated. Finally, we confirmed that our best compounds exhibited selective inhibition of NEU orthologues from murine brain.
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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) | 0.000 | 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 | 0.001 |
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".