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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".