CuI-Catalyzed Ullmann-Type Coupling of Phenols and Thiophenols with 5-Substituted 1,2,3-Triiodobenzenes: Facile Synthesis of Mammary Carcinoma Inhibitor BTO-956 in One Step
Bibliographic record
Abstract
Abstract A facile and unprecedented synthesis of 2,3-diiodinated or 2,6-diiodinated diaryl ether/thioether derivatives through regioselective Ullmann-type cross couplings of 5-substituted 1,2,3-triiodobenzenes and phenols/thiophenols is described. Remarkably, the coupling reactions are simply controlled by the type of nucleophiles and the nature of C5 substituent at 1,2,3-triiodoarenes providing the internal or terminal coupling products in high regioselectivity and good isolated yields. Noticeable steric and electronic effects were clearly observed on both 1,2,3-triiodoarenes and nucleophiles. The highest yields were isolated from a combination between either electron-poor 1,2,3-triiodoarenes and phenols or electron-rich 1,2,3-triiodoarenes and thiophenols. The optimized conditions were found to be suitable for several functional groups. Using this methodology, mammary carcinoma inhibitor BTO-956 is prepared in only one step with excellent regioselectivity and good isolated yield. This report discloses the first method to prepare 2,3-diiodinated and 2,6-diiodinated diaryl ethers/thioethers in one step that is efficient, regioselective, and general in scope. The products are truly remarkable precursors for other transformations.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".