Computational Modeling of Substituent Effects on Phenol Toxicity
Bibliographic record
Abstract
Standard computational models of cytotoxicity of substituted phenols relate the toxicity to a set of quatitative structure-activity relationship (QSAR) descriptors such as log P, p K a, OH bond dissociation enthalpy (BDE), etc. Implicit in this approach is the idea that the phenoxyl radical is disruptive to the cell and factors increasing its production rate will enhance the toxicity. To improve the QSAR correlations, substituents are usually divided into electron-donating groups (EDG) and electron-withdrawing groups (EWG), which are treated separately and thought to follow different mechanisms of toxicity. In this paper, we focus on one important aspect of toxicity, the rate constant for production of phenoxyl radical. Activation energies are obtained for the reaction of X-phenol with peroxyl radical by using the Evans-Polanyi principle, giving rate constants as a function of DeltaBDE values for both EDG and EWG sets. We show that (i) a plot of log k for phenoxyl formation vs DeltaBDE shows a double set of straight lines with different slopes, justifying the usual EDG and EWG separation but without requiring any change in mechanism; (ii) the same method can be effectively used for different target radicals (e.g., tert-butoxyl) or different sets of parent compounds (e.g., substituted catechols), thus giving a useful general approach to analysis of toxicity data; (iii) regions of constant toxicity in all cases are predicted; and (iv) we argue that competing parallel mechanisms of toxicity are likely to be dominant for EWG-substituted phenols.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".