Computational Determination of Aqueous p<i>K</i><sub>a</sub> Values of Protonated Benzimidazoles (Part 2)
Bibliographic record
Abstract
Our aim is to develop an effective computational procedure for predicting the aqueous acid equilibrium constants of protonated benzimidazoles at 298.15 K. The experimental determination of these values, apart from been laborious, is a challenge because of the low water solubility of these compounds. Using a variety of descriptors, quantitative structure-property relationships (QSPR) are explored between the experimental aqueous pKa values of a group of fifteen benzimidazoles and descriptors calculated at the B3LYP/6-31+G(d,p) level of theory. Solvent effects are taken into account with the PCM solvation model through both single-point energy calculations (PCM(sp)), and in the geometry optimizations and frequency calculations (PCM(opt)). Descriptors considered are the Gibbs free-energy change of the acid equilibrium in water, the charges on the acidic hydrogen, and on the basic nitrogen, several orbital energies of the protonated and neutral species, and the volume of the solvent cavity. Multiple linear regressions are used to correlate descriptors to the experimental pKa values. Several QSPR equations reproduce the experimental data more accurately, and show stronger correlations than previously attempted methodologies. The predictive capabilities of the QSPR methodologies are tested with four compounds that were not included in the set of benzimidazoles initially investigated. In addition, a correlation between experimental pKa values in water and in a 50% ethanol-water solution is used to estimate aqueous pKa values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".