Elucidating Fluorine Steering Effects in Diels‐Alder Reactions Interfaced with Charge‐Enhanced Reactivity
Bibliographic record
Abstract
Abstract Fluorinated molecules are core to contemporary drug discovery programs and critical for advancing innovation in numerous fields. In merging these important chemical themes, fluorinated Diels‐Alder cycloaddition products are a particularly attractive subset of compounds with significant utility. Herein, an in‐depth computational and experimental study of fluorine substitution effects on dienophile partners in Diels‐Alder reactions is reported. Of particular focus to this study is understanding the origin of reaction rate deceleration as a consequence of employing fluorinated dienophiles and the factors controlling endo‐ vs. exo‐selectivity. To unlock insight into this unique reactivity, density function theory calculations, distortion/interaction‐activation strain models, energy decomposition analysis and natural bond orbital analysis, among other computational methods, were applied. In addition, the influence of oriented external‐electric‐field‐effects (OEEFs) and local electric field effects were explored. To further probe this effect, experimental studies of charge‐enhanced Diels‐Alder reactivity with fluorinated dienophiles were conducted. Collectively, this work offers novel mechanistic understanding pertinent to Diels‐Alder reactions of fluorinated dienophiles providing valuable fluorinated scaffolds.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".