Second-generation MS-based high-throughput screening system for enantioselective catalysts and biocatalysts
Bibliographic record
Abstract
A high-throughput method is described, where the enantioselectivity of approximately 10 000 catalysts or biocatalysts can be determined per day. The method is based on electrospray mass spectrometric techniques using an eight-channel multiplexed (MUX) sprayer system connected to a time-of-flight mass spectrometer. The inlet of the ion source is controlled by a stepping rotor that is continuously moving from one sprayer to the next with a recording time of 100 ms for each channel and a delay time of 50 ms, thus allowing a spectrum to be obtained from each channel every 1.2 s. One cycle, where eight samples are being sprayed in parallel, requires around 70 s, which allows a 96-well microtiter plate to be screened in 14 min. Integration of two pseudo-enantiomers (S)-glycidyl phenyl ether and (R)-D5-glycidyl phenyl ether is necessary to quantify the enantiomeric excess (ee-value), where one enantiomer is isotopically labeled to allow easy identification of the mass spectrometric signals. Errors of ~2% for the ee-values indicate that in addition to the significant improvement in sample throughput this is also a precise method for high-throughput screening. This second-generation assay is useful for combinatorial enantioselective transition-metal catalysis and in the directed evolution of enantioselective enzymes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".