Examining the PM6 semiempirical method for pKa prediction across a wide range of oxyacids
Bibliographic record
Abstract
Abstract The pK~a~ estimation ability of the semiempirical PM6 method was evaluated across a broad range of oxyacids and compared to results obtained using the SPARC software program. Compound classes under consideration included acetic acids, alicyclic and aromatic heterocyclic acids, benzoic acids, boronic acids, hydroxamic acids, oximes, peroxides, peroxyacids, phenols, α-saturated acids, α-saturated alcohols, sulfinic acids, α-unsaturated acids, and α-unsaturated alcohols. PM6 accurately predicts the acidity of acetic and benzoic acids and their derivatives, but is less reliable for alicyclic and aromatic heterocyclic acids and phenols. α-Saturated acids are reliably modeled by PM6 except for polyacid derivatives with α-alcohol moieties. α-Saturated alcohols only appear to yield reliable PM6 results where an α-hydroxy or α-alkoxy moiety is absent. Carboxylic acids with simple α-alkene unsaturation are well approximated by PM6 except where alkyne α-unsaturation or α-carboxylation are also present. The PM6 and SPARC methods exhibit approximately equal pKa prediction performance for the acetic, alicyclic, and benzoic acids. SPARC outperforms PM6 on the peroxides, peroxyacids, phenols, and α-saturated acids and α-saturated alcohols. pKa values for boron, nitrogen, and sulfur oxyacids do not appear to be reliably estimated by either the PM6 or SPARC methods. The findings will help guide the potential appropriateness of results from the PM6 pK~a~ estimation method for waste treatment and environmental fate investigations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".