Thermodynamic Modeling of Chiral Compounds Solubility Using Correlative and Predictive Models
Bibliographic record
Abstract
Many intermolecular forces and parameters affect the solubility of a compound in a solvent. Various thermodynamic models are presented to predict these parameters and determine solid liquid equilibrium data. By selecting suitable thermodynamic model for solubility modeling, calculation error is reduced and the results will be closer to the experimental data. Herein, the ability of two predictive and two correlative models in solubility modeling of chiral compounds is investigated. Thus, solubility of pure and racemic forms of chiral Ketamine, Mandelic acid and 3-Chloromandelic acid is evaluated using UNIQUAC and NRTL models. The solubility modeling of pure and racemic forms of Ketamine in Ethanol is also determined by UNIFAC and NRTL-SAC models. There are good agreement between experimental data and results of NRTL and UNIQUAC models. Predictive NRTL-SAC model shows smaller deviation than UNIFAC in solubility determination of pure and racemic form of Ketamine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".