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Record W2080461000 · doi:10.1021/jp8023292

Amidicity Change as a Significant Driving Force and Thermodynamic Selection Rule of Transamidation Reactions. A Synergy between Experiment and Theory

2008· article· en· W2080461000 on OpenAlexaff
Zoltán Mucsi, Gregory A. Chass, Imre G. Csizmadia

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2008
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity College of the North
Fundersnot available
KeywordsAmideSelection (genetic algorithm)Amine gas treatingChemistryVariety (cybernetics)Biochemical engineeringProcess (computing)Computational chemistryCharacterization (materials science)Computer scienceNanotechnologyMaterials scienceOrganic chemistryArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.282
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations52
Published2008
Admission routes1
Has abstractyes

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