Influence of ligand’s directional configuration, chrysenes as model compounds, on the binding activity with aryl hydrocarbon receptor
Bibliographic record
Abstract
Understanding what and how physico-chemical factors of a ligand configure conditions for ligand-receptor binding is a key to accurate assessment of toxic potencies of environmental pollutants. We investigated influences of the dipole-driven orientation and resulting directional configuration of ligands on receptor binding activities. Using physico-chemical properties calculated by ab initio density functional theory, directional reactivity factors (DRF) were devised as main indicators of toxic potencies, linking molecular ligand-receptor binding to in vitro responses. The directional reactive model was applied to predict variation of aryl hydrocarbon receptor-mediated toxic potencies among homologues of chrysene with structural modifications such as the numbers of constituent benzene rings, methylation and hydroxylation. Results of predictive models were consistent with empirical potencies determined by use of the H4IIE-luc transactivation bioassay. The experiment-free approach based on first principles provides an analytical framework for estimating molecular bioactivity in silico and complements conventional empirical approaches to studying molecular initiating events in adverse outcome pathways.
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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) | 0.000 | 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 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".