Prediction of cytotoxicity of polycyclic aromatic hydrocarbons from first principles
Bibliographic record
Abstract
Ligand-specific binding interactions of xenobiotics with receptor proteins form the basis of cytotoxicity-based hazard assessment. Computational approaches enable predictive hazard assessment for a large number of chemicals in a high-throughput manner, minimizing the use of animal testing. However, in silico models for predicting mechanisms of toxic actions and potencies are difficult to develop because toxicity datasets or comprehensive understanding of the complicated kinetic process of ligand-receptor interactions are needed for model development. In this study, a directional reactive binding factor (DRBF) model based on first principles was used to predict cytotoxicity potencies of agonists of the aryl hydrocarbon receptor (AhR) for 16 different polycyclic aromatic hydrocarbons (PAHs). Molecular dynamics were simulated by accounting for the directional configuration factor toward receptor protein and the factor of binding to the Per-Arnt-Sim (PAS) domain. When comparing the experimental results of toxic potencies from in vitro bioassays with the predictions among two different in silico models, including quantitative structure-activity relationship (QSAR) and molecular docking models, the DRBF model exhibited the highest model performance (R 2 = 0.90 and p < 0.01). Our results showed that the DRBF model based on first principles and molecular and computational structural biology could serve as a novel framework to advance next generation hazard assessment for high-throughput screening of chemical substances. • We presented simple, accurate models to predict binding of ligands to AhR. • Development of a directional reactivity factor (DRF) as a prediction indicator. • A directional reactive binding factor by calculating a binding rate between ligands and AhR. • Greater accuracy than conventional toxicity prediction model (QSAR, docking model). • The model highlighted a more profound mechanism of chemical toxicity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".