Determination of Dermal Absorption Qsar/Qsprs by Brute Force Regression: Multiparameter Model Development with Molsuite 2000
Bibliographic record
Abstract
Accurate dermal quantitative structure-activity/property relationship (QSAR/QSPR) models are needed to predict percutaneous absorption of environmental contaminants. The Molsuite 2000 chemistry modeling software (ChemSW, Fairfield, CA) was used to model the Flynn component of the Kirchner in vitro human skin permeability coefficient (K(p)) data. This Kirchner- derived Flynn (K/F) database was updated to include recent literature data quality recommendations. A K/F data subset consisting of nondrug compounds was used to further optimize the developed QSPR models. The statistical fit of the models was excellent with r(2) values up to.89 for a three-descriptor parameter K/F database model and up to.96 for a four- parameter model of the data subset. A one-parameter transform model using only logarithm octanol-water partition coefficient (log K(o/w)) was also developed for the data subset (r(2) =.87). Molecular volume (MV) descriptors were not shown to be superior to molecular weight in conventional two-parameter models with log K(o/w) but may be superior in multiparameter models. A previously nonreported descriptor, surface tension in water (STW), was found to provide optimal multiparameter models. The developed models passed PRESS cross-validation and could be useful for predicting environmental systemic dermal exposure.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".