Toxicity profile of aromatic compounds towards <i>Scenedesmus obliquus</i>: a QSAR study
Bibliographic record
Abstract
The parameterization of molecular hydrophobicity and electrophilicity, contributing to the overall toxicity of aromatic compounds, has been the subject of many quantitative structure−activity relationship (QSAR) studies. So far, hydrophobicity has been largely described in terms of the logarithm of the octanol−water partition coefficient (log P) and the molecular electrophilicity in terms of the energy of the lowest unoccupied molecular orbital (E LUMO ), the maximum acceptor superdeocalizability (A max ), and the electrophilicity index (ω). Here, we report for the first time the parameterization of these properties in terms of cumulative interplay of multiple descriptors. The toxicity data of 68 compounds were compiled in terms of 50% population growth inhibition (pIGC 50 ) of Scenedesmus obliquus. The comparison of the two QSARs (pIGC 50 = 0.175E LUMO + 0.057log P + 0.363ω + 0.019V – 3.292, R 2 adj = 0.761 and pIGC 50 = 0.368E LUMO + 0.146α + 0.258ω + 0.021V − 1.170, R 2 adj = 0.776) reveals that polarizability (α) is a superior descriptor to log P for parameterization of hydrophobicity, when used in conjunction with E LUMO , ω, and V, for profiling of the toxicity of the test compounds. The overall results indicate that ω and α are better descriptors of electrophilicity and hydrophobicity, respectively, for mapping the toxicity profile of aromatic derivatives towards the target organism.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".