An Atoms-In-Molecules study of the genetically-encoded amino acids: I. Effects of conformation and of tautomerization on geometric, atomic, and bond properties
Bibliographic record
Abstract
The theory of Atoms-In-Molecules (AIM) is a partitioning of the real space of a molecule into disjoint atomic constituents as determined by the topology of the electron density, rho(r). This theory identifies an atom in a molecule with a quantum mechanical open system and, consequently, all of the atom's properties are unambiguously defined. AIM recovers the basic empirical cornerstone of chemistry: that atoms and functional groups possess characteristic and additive properties that in many cases exhibit a remarkable transferability between different molecules. As a result, the theory enables the theoretical synthesis of a large molecule and the prediction of its properties by joining fragments that are predetermined as open systems. The present article is the first of a series (in preparation) that explore this possibility for polypeptides by determining the transferability of the building blocks: the amino acid residues. Transferability of group properties requires transferability of the electron density rho(r), which in turn requires the transferability of the geometric parameters. This article demonstrates that these parameters are conformation-insensitive for a representative amino acid, leucine, and that the atomic and bond properties exhibit a corresponding transferability. The effects of hydrogen bonding are determined and a set of geometrical conditions for the occurrence of such bonding is identified. The effects of transforming neutral leucine into its zwitter-ionic form on its atomic and bond properties are shown to be localized primarily to the sites of ionization.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".