Forecasting CYP2D6 and CYP3A4 Risk with a Global/Local Fusion Model of CYP450 Inhibition
Why this work is in the frame
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Bibliographic record
Abstract
This work presents a method to utilize the ever-expanding corporate collections of CYP450 inhibition data to forecast the future risk of compounds not yet synthesized. The global/local fusion method differs from existing QSAR methods, in that each prediction is derived from a custom-built QSAR model, constructed on-the-fly, using a customized training set assembled for each prediction. It uses a consensus of global and local descriptor-based models along with pharmacophore-based fingerprint similarity to form a prediction and to assess the uncertainty of the prediction on a case-by-case basis. We also present a new forward prediction testing and validation scheme in which the corporate dataset is split chronologically, and predictions for a molecule are based on the pool of existing data available before the molecule is registered and tested. The validation accuracy of the CYP2D6 and CYP3A4 models approaches the underlying accuracy of the data, about 0.4 log IC50 units standard error (or nearly 70% r(2) correlation) for the most confident predictions, and extends to about 0.6 log IC50 units standard error (or under 30% r(2) correlation) for the least confident predictions. As a classification model for CYP2D6 and CYP3A4 activity, the validation accuracy is about 79% for predicted actives and 85% for predicted inactives, which is consistent with existing published models.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it