Drug-Design QSARs based on QTAIM Electron Localization/Delocalization Indices
Bibliographic record
Abstract
Following the lead of chemical graph-theoretical connectivity matrices, electron localization/delocalization matrices (LDMs) and their matrix-invariants derived forms will be introduced and shown to provide a faithfully encoding of the properties of the molecules by comparing with experiment. The matrix elements of an LDM are obtained from Bader's quantum theory of atoms in molecules (QTAIM) whereby the diagonal elements are the localization indices and the off diagonal elements are 1/2 of the delocalization indices. The sum of any row or column is the total electron population of a given atom while the sum of all sums is, of course, the total number of electrons in the molecule (N). The matrix is, thus, rich with electronic (and structural) information, implicitly and explicitly, and is conceivably useful in generating quantitative molecular descriptors for structure-to-activity relationship studies (QSAR). This talk will briefly review the uses and concepts of molecular electron density descriptors with emphasis on this new class of descriptors as a novel, possibly promising, possibility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".