Catalytic Homogeneous Asymmetric Hydrogenation: Successes and Opportunities
Bibliographic record
Abstract
This is an overview of successes in the realm of catalytic homogeneous asymmetric hydrogenation of substrates primarily of interest in the synthesis of pharmaceuticals in order to identify important problems still unsolved. First, tables are provided that list the successful reductions to over 90% enantiomeric excess of prochiral ketones to alcohols, imines to amines, and olefins to saturated carbon centers. Noted in the tables are the metal (including “green” metals Mn, Fe, and Co) or enzyme, the class of ligand, the conditions of the medium, and the scale of reduction, if over 1 kg of product, as well as the nature of the process, whether direct hydrogenation using H 2 gas (DH), transfer hydrogenation (TH), or hydrogenation with dynamic kinetic resolution (DKR). Tables of representative pharmaceutical or fine chemicals products are provided for each class of substrate. With this overview, the opportunities for further research and development become clearer.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".