Amidicity Change as a Significant Driving Force and Thermodynamic Selection Rule of Transamidation Reactions. A Synergy between Experiment and Theory
Bibliographic record
Abstract
Although essential in medicinal and industrial chemistry, transamidation reactions are still poorly understood mechanistically and in particular in terms of the extreme nature for their proceeding either very smoothly or not occurring at all. As yet, there exists no qualitative rule to predict the outcome of an amide interacting with an amine, with quantitative evaluations far from being established. In this paper we aim to clarify the thermodynamic selection rule and driving force of transamidation reactions based on amidicity value, measuring numerically the amide bond strength, toward providing a relatively simple protocol for practicing organic chemists to predict the outcome of an experiment. The change of amidicity over the course of a reaction made it possible to see that the process is favorable or unfavorable. This recently evaluated driving force of amidicity behaves analogously to the driving force of aromaticity in other organic reactions. This paper presents a successful comparison between empirical synthetic results and relevant computational characterizations, for a variety of transamidation reactions, all toward a synergy between experiments and theory. In this paper, we are re-examining experimentally and theoretically earlier experimental findings in relation to transamidation reactions and interpreting them from the aspect of amidicity change and stabilization enthalpies.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".