Practical Calculation of Molecular Acidity with the Aid of a Reference Molecule
Bibliographic record
Abstract
A set of linear free energy models are presented for determining the pK(a) values of amines, alcohols, and carboxylic acids. Models are determined from a series of pK(a) predictors, taken both from traditional natural atomic orbital analysis (NAO) and from a novel approach introduced here of using a reference molecule: an ammonium ion for amines and a hydrogen sulfide molecule for alcohols and carboxylic acids. Using these reference molecules, we calculate the barrier to proton transfer and show that a number of properties associated with the transition state are correlated with the pK(a). By considering 38 predictors, we obtain a four-variable model for amines and a three-variable model for oxygen-containing compounds. The model for amines is based on 145 compounds and has a root mean squared error (RMSE) of 0.45 and R(2) = 0.98. The oxygen set has 48 molecules: RMSE = 0.26, and R(2) = 0.993. Similar, linear, and multilinear models are constructed after separating the sets into chemically similar categories: alcohols, carboxylic acids, and primary, secondary, tertiary, and aromatic amines. This separation gives simpler models with relatively low RMSE values, where the most important predictor of the pK(a) is the difference in energy between transferring the proton from the reference molecular base to the conjugate acid from the data set.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".