A comprehensive model for chemical bioavailability and toxicity of organic chemicals based on first principles
Bibliographic record
Abstract
Here, we present a novel model to predict the toxicity and bioavailability of polychlorinated biphenyls (PCBs) as model compounds based on a first principles approach targeting basic electronic characteristics. The predictive model is based on an initio density functional theory. The model suggests HOMO-LUMO energy gap as the overarching indicator of PCBs toxicity, which was shown to be the primary factor predicting toxicity, but not the only factor. The model clearly explains why chlorination of both para positions is required for maximum toxic potency. To rank toxic potency, the "dipole moment" in relation to the most chemically active Cl-sites was critical. This finding was consistent with the accepted toxic equivalency factor (TEF) model for these molecules, and was also able to improve on ranking toxic potency of PCBs with similar TEFs. Predictions of HOMO-LUMO gap made with the model were consistent with measured values determined by synchrotron based X-ray spectroscopy for a subset of PCBs. HOMO-LUMO gap can also be used to predict bioaccumulation of PCBs. Overall, the new model provides an in silico method to screen a wide range of chemicals to predict their toxicity and bioavailability to act as an AhR agonist.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".