A beginner's guide to <sup>19</sup>F NMR and its role in drug screening
Bibliographic record
Abstract
The structural biology renaissance has created new opportunities for both understanding mechanisms of action of many dynamic protein complexes and advancing drug discovery. 19 F NMR can play a key role in both protein and ligand nuclear magnetic resonance (NMR). In particular, by judiciously labeling the protein target with CF 3 reporters, functional states can be monitored as a function of ligand or drug candidate so as to understand their mechanism of action or response. At the same time, fragment-based drug discovery (FBDD) using fluorinated libraries enables the rapid detection of binders and their elaboration toward lead compounds. Future studies will likely employ fluorinated tags with improved chemical shift sensitivity and reporters that can be biosynthetically incorporated via AMBER stop codon technologies. At the same time, FBDD will be greatly improved by promising new fluorinated libraries in combination with improved computational methods for predicting lead compounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".