Direct Heterocycle C–H Alkenylation via Dual Catalysis Using a Palladacycle Precatalyst: Multifactor Optimization and Scope Exploration Enabled by High-Throughput Experimentation
Bibliographic record
Abstract
One of the major challenges in developing catalytic methods for C–C bond formation is the identification of generally applicable reaction conditions, particularly if multiple substrate structural classes are involved. Pd-catalyzed direct arylation reactions are powerful transformations that enable direct functionalization of C–H bonds; however, the corresponding direct alkenylation reactions, using vinyl (pseudo) halide electrophiles, are less well developed. Inspired by process development efforts toward GSK3368715, an investigational active pharmaceutical ingredient, we report that a Pd(II) palladacycle derived from tri- tert -butylphosphine and Pd(OAc) 2 is an effective single-component precatalyst for a variety of direct alkenylation reactions. High-throughput experimentation identified optimal solvent/base combinations for a variety of HetAr–H substrate classes undergoing C–H activation without the need for cocatalysts or stoichiometric silver bases (e.g., Ag 2 CO 3 ). We propose this reaction proceeds via a dual cooperative catalytic mechanism, where in situ-generated Pd(0) supports a canonical Pd(0)/(II) cross-coupling cycle and the palladacycle effects C–H activation via CMD in a redox-neutral cycle. In all, 192 substrate combinations were tested with a hit rate of approximately 40% and 24 isolated examples. Importantly, this method was applied to prepare a key intermediate in the synthesis of GSK3368715 on multigram scale.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".