A quantitative structure-activity relationship for predicting metabolic biotransformation rates for organic chemicals in fish
Bibliographic record
Abstract
An evaluated database of whole body in vivo biotransformation rate estimates in fish was used to develop a model for predicting the primary biotransformation half-lives of organic chemicals. The estimated biotransformation rates were converted to half-lives and divided into a model development set (n=421) and an external validation set (n=211) to test the model. The model uses molecular substructures similar to those of other biodegradation models. The biotransformation half-life predictions were calculated based on multiple linear regressions of development set data against counts of 57 molecular substructures, the octanol-water partition coefficient, and molar mass. The coefficient of determination (r2) for the development set was 0.82, the cross-validation (leave-one-out coefficient of determination, q2) was 0.75, and the mean absolute error (MAE) was 0.38 log units (factor of 2.4). Results for the external validation of the model using an independent test set were r2 = 0.73 and MAE = 0.45 log units (factor of 2.8). For the development set, 68 and 95% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. For the test (or validation) set, 63 and 90% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. Reasons for discrepancies between model predictions and expected values are discussed and recommendations are made for improving the model. This model can predict biotransformation rate constants from chemical structure for screening level bioaccumulation hazard assessments, exposure and risk assessments, comparisons with other in vivo and in vitro estimates, and as a contribution to testing strategies that reduce animal usage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".