4-arylbutan-2-ones: Starting Materials in the Synthesis of Novel Heme Oxygenase Inhibitors
Bibliographic record
Abstract
Synthetic organic chemistry and medicinal chemistry are the most significant fields of research in chemistry, where 4-arylbutan-2-ones find applications by allowing access to chemical entities otherwise difficult to synthesize, or in the de novo development of drug candidates. Structure-aided design based on previous results from our group has led to advances in shaping the structure of a series of novel imidazolebased heme oxygenase inhibitors. The practical generation of these inhibitors requires the synthesis of a set of 4-arylbutan-2-ones to be employed as starting materials in a reaction sequence that would afford in the end the desired imidazole-containing inhibitor target compounds. The present report illustrates the use of an one-step alkylation–cleavage synthetic approach toward such 4-arylbutan-2-ones featuring, in most cases, a hydrophobic para-substituent in the aromatic ring, starting from low-cost, commercially available organic reagents (pentane-2,4-dione and the suitably substituted benzyl bromides). The work described in this study represents an extension of a synthetic entry to this type of organic compounds, previously exploited in our group for the preparation of several structural analogs. The identity of the obtained 4-arylbutan-2- ones was established using nuclear magnetic resonance spectroscopy and high resolution mass spectrometry.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".