Fragment Screening and Fast Micromolar Detection on a Benchtop NMR Spectrometer Boosted by Photoinduced Hyperpolarization
Bibliographic record
Abstract
Fragment-based drug design is a well-established strategy for rational drug design, with nuclear magnetic resonance (NMR) on high-field spectrometers as the method of reference for screening and hit validation. However, high-field NMR spectrometers are not only expensive, but require specialized maintenance, dedicated space, and depend on liquid helium cooling which became critical over the recurring global helium shortages. We propose an alternative to high-field NMR screening by applying the recently developed approach of fragment screening by photoinduced hyperpolarized NMR on a cryogen-free 80 MHz benchtop NMR spectrometer yielding signal enhancements of up to three orders in magnitude. It is demonstrated that it is possible to discover new hits and kick-off drug design using a benchtop NMR spectrometer at low micromolar concentrations of both protein and ligand. The approach presented performs at higher speed than state-of-the-art high-field NMR approaches while exhibiting a limit of detection in the nanomolar range. Photoinduced hyperpolarization is known to be inexpensive and simple to be implemented, which aligns greatly with the philosophy of benchtop NMR spectrometers. These findings open the way for the use of benchtop NMR in near-physiological conditions for drug design and further life science applications.
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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".