Nano-QSAR models for predicting cytotoxicity of metal oxide nanoparticles (MONPs) to <i>E. coli</i>
Bibliographic record
Abstract
Nanotechnology has been applied to many aspects of human life. Meanwhile, concerns regarding the toxicity of engineered nanomaterials to the environment have also been growing. Herein, an economic and convenient approach based on quantitative structure–activity relationship for nanomaterials (nano-QSAR) was proposed to evaluate the cytotoxicity of metal oxide nanoparticles (MONPs) to E. coli. Six molecular descriptors of 17 MONPs were selected and calculated using Gaussian98 software and DFT-B3LYP method on the LANL2DZ basis set. Two multivariable models, linear and nonlinear, were built based on the calculated molecular descriptors using multiple linear regression (MLR) and support vector machine (SVM) methods, respectively. Results demonstrated that both models presented high reliability, good predictive performance, and fine generalization ability, with all R 2 values greater than 0.84. It was also revealed that the lowest unoccupied molecular orbital (LUMO) and molar heat capacity (C p ) were the two key descriptors influencing the cytotoxicity of MONPs.
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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 | 0.001 |
| 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 | 0.001 |
| 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".