Inductive Electronegativity Scale. Iterative Calculation of Inductive Partial Charges
Bibliographic record
Abstract
A number of novel QSAR descriptors have been introduced on the basis of the previously elaborated models for steric and inductive effects. The developed "inductive" parameters include absolute and effective electronegativity, atomic partial charges, and local and global chemical hardness and softness. Being based on traditional inductive and steric substituent constants these 3D descriptors provide a valuable insight into intramolecular steric and electronic interactions and can find broad application in structure-activity studies. Possible interpretation of physical meaning of the inductive descriptors has been suggested by considering a neutral molecule as an electrical capacitor formed by charged atomic spheres. This approximation relates inductive chemical softness and hardness of bound atom(s) with the total area of the facings of electrical capacitor formed by the atom(s) and the rest of the molecule. The derived full electronegativity equalization scheme allows iterative calculation of inductive partial charges on the basis of atomic electronegativities, covalent radii, and intramolecular distances. A range of inductive descriptors has been computed for a variety of organic compounds. The calculated inductive charges in the studied molecules have been validated by experimental C-1s Electron Core Binding Energies and molecular dipole moments. Several semiempirical chemical rules, such as equalized electronegativity's arithmetic mean, principle of maximum hardness, and principle of hardness borrowing could be explicitly illustrated in the framework of the developed approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".