QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions†
Bibliographic record
Abstract
The principle of using a singe model to predict the toxicity of mixtures of chemicals based on the characterisation of the degrees of similarity and dissimilarity of the constituent chemicals using descriptors has been demonstrated in a previous work. The current study introduces a feature extraction technique, independent component analysis, to the method to remove the correlations and dependencies between descriptors and reduce the dimension prior to similarity and dissimilarity calculations. In addition, a goal attainment multi-objective optimisation technique is used for the determination of the fuzzy membership function parameters. For three mixtures, which include a new mixture and two previously studied mixtures that all inhibit reproduction (via different mechanisms of action) in green freshwater algae scenedesmus vacuolatus, the approach showed better or equivalent prediction performance than either concentration addition or independent action models. Unlike QSARs for pure compounds that require large collections of data, the new approach for mixtures only requires one mixture at a particular composition to determine the necessary fuzzy membership function parameter values. These values can then be used to predict the toxicity of the mixture at any other compositions. This could potentially lead to a reduction in the frequency of bioassay tests. Use of the fuzzy membership functions and parameter values obtained for one mixture when used to predict the toxicity of a completely different mixture is also tested and it is found that the approach also gives prediction results with good accuracy.
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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 | 0.000 |
| Open science | 0.000 | 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".