Modeling the Reaction Mechanisms of the Amide Hydrolysis in an <i>N</i>-(<i>o</i>-Carboxybenzoyl)-<scp>l</scp>-amino Acid
Bibliographic record
Abstract
Reaction mechanisms of the amide hydrolysis from the protonated, neutral, and deprotonated forms of N-(o-carboxybenzoyl)-l-amino acid have been investigated by use of the B3LYP density functional method. Our calculations reveal that in the amide hydrolysis the reaction barrier is significantly lower in solution than that in the gas phase, in contrast with the mechanism for imide formation in which the solvent has little influence on the reaction barrier. In the model reactions, the water molecules function both as a catalyst and as a reactant. The reaction mechanism starting from the neutral form of N-(o-carboxybenzoyl)-l-amino acid, which corresponds to pH 0-3, is concluded to be the most favored, and a concerted mechanism is more favorable than a stepwise mechanism. This conclusion is in agreement with experimental observations that the optimal pH range for amide hydrolysis of N-(o-carboxybenzoyl)-l-leucine is pH 0-3 where N-(o-carboxybenzoyl)-l-leucine is predominantly in its neutral form. We suggest that besides the acid-catalyzed mechanism the addition-elimination mechanism is likely to be an alternative choice for cleaving an amide bond. For the reaction mechanism initiated by protonation at the amidic oxygen (hydrogen ion concentration H(0) < -1), the reaction of the model compound with two water molecules lowers the transition barrier significantly compared with that involving a single water molecule.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".