Global and local reactivity descriptors based on quadratic and linear energy models for <i>α</i> , <i>β</i> ‐unsaturated organic compounds
Bibliographic record
Abstract
Abstract Global and local descriptors of chemical reactivity can be derived from conceptual density functional theory. Their explicit form, however, depends on how the energy is defined as a function of the number of electrons. Within the existing interpolation models, here, the quadratic and the linear energy model were used to derive global descriptors as the electrophilicity and nucleophilicity (defined as the negative of the ionization potential) and local descriptors employing either the corresponding condensed Fukui function in the linear model or the local response of the global descriptor in the quadratic model. The ability of these descriptors to predict the reactivity of molecules with more than one reactive site was first studied on a set of α , β ‐unsaturated ketones, where experimental rate constants for the nucleophilic attack is known. With the validated descriptors the reactivity of α , β ‐unsaturated carboxylic compounds with different heteroatoms as α , β ‐unsaturated thioesters, esters, and amides was addressed as alternative substrates for enzymatic CO 2 fixation. Carbon dioxide fixation involves the reduction of the neutral α , β ‐unsaturated carboxylic compounds by a nucleophilic attack of a hydride anion from NADPH and the following electrophilic attack by carbon dioxide. It was found that condensed values of the linear Fukui function within the fragment of molecular response approximation describe best the reactivity of α , β ‐unsaturated ketones. For the two relevant processes involved in CO 2 fixation the amides present the largest reactivity in vacuum and in aqueous solution compared to the esters and thioesters and may, therefore, serve as alternative substrates of carboxylases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".