A Bulky Imidodiphosphorimidate Brønsted Acid Enables Highly Enantioselective Prins-semipinacol Rearrangements
Bibliographic record
Abstract
The development of BINOL-derived Brønsted acid catalysts has been profoundly guided by rational design, with carefully implemented structural changes leading to unique generations of catalysts with enhanced reaction capabilities. This approach to catalyst optimization has promoted the integration of knowledge gathered in optimizing prior eras of Brønsted acids and ultimately, the molecular features that have contributed to the success of previous designs are preserved. Of these, the large substituents at the 3 and 3’ positions of the BINOL backbone are the most critical with almost every newly developed structure possessing this feature. However, imidodiphosphorimidate (IDPi) catalysts are not synthetically well-suited to contain the same sterically bulky groups associated with the high selectivity imparted by previously implemented catalyst structures. Herein, we have leveraged the moderate size (as compared to TRIP and 9-anthryl) but high applicability of the 9-phenanthryl substituent to synthesize a sterically demanding IDPi. Using computed descriptors, we survey the catalyst properties of known structures to demonstrate this catalyst to be both unique and one of the bulkiest IDPi yet synthesized. The applicability of the catalyst was evaluated in the construction of stereochemically dense spirocycles generated via an asymmetric Prins-semipinacol reaction sequence. Transition state calculations were deployed to interrogate the origins of the superior enantioselectivity and these demonstrate the mechanistic hallmarks of the 9-phenanthryl substituent can be generalized to a genuinely different class of Brønsted acid catalyst. Ultimately, providing the basis for the development of general catalyst design principles and the translation of “privileged” substituents across unique eras of Brønsted acid catalyst structures.
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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 | 0.000 | 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) | 0.001 | 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 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".