Micromolar Concentration Affinity Study on a Benchtop NMR Spectrometer with Secondary <sup>13</sup> C Labeled Hyperpolarized Ligands
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Benchtop NMR is becoming an increasingly important tool, sometimes providing a simple and low-cost alternative to high-field NMR. The Achilles heel of NMR and even more critically of benchtop NMR is its limited sensitivity. However, when combined with hyperpolarization techniques, the sensitivity boost can provide excellent sensitivity that can even make benchtop NMR compatible with affinity studies for drug discovery. Hyperpolarization by dissolution dynamic nuclear polarization (dDNP) provides a route to enhancing 13 C nuclear magnetic resonance (NMR) sensitivity by more than 5 orders of magnitude for a wide range of small molecules on a benchtop NMR system. We show here how ligands can be secondarily labeled with 13 C tags and hyperpolarized with conventional dDNP methods. These hyperpolarized ligands display long nuclear spin–lattice relaxation time constants and can therefore be used to probe interactions with target proteins in conventional dDNP settings. The boost in sensitivity combined with the simplicity of the 13 C spectra (one peak per ligand) enables detection on an 80 MHz benchtop NMR spectrometer at micromolar concentrations, which may ultimately provide a way of improving and accelerating the discovery of new drug candidates.
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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".