Challenges in constructing statistically based structure‐activity relationship models for developmental toxicity
Bibliographic record
Abstract
Regulatory agencies are increasingly called upon to review large numbers of environmental contaminants that have not been characterized for their potential to pose a health risk. Additionally, there is special interest in protecting potentially sensitive subpopulations and identifying developmental toxicants that may be present in the environment. Thus, there is an urgent need for efficient methods to screen large numbers of chemicals for their potential to pose a developmental hazard. One potential screening method involves the use of statistically based structure-activity relationship (SAR) tools to predict activity of untested chemicals. Such systems rely on statistical analyses to discern relationships between structure and activity for a training set of substances. Predictions can then be made for an untested substance as long as its structural features are encompassed by chemicals of the training set. In theory, such systems could assist regulatory agencies in their screening efforts; however, to date, there has been little independent evaluation of these tools for this use. To contribute to such an evaluation, the International Life Sciences Institute Risk Science Institute (ILSI RSI) convened a Working Group to examine methodology used to construct statistically based SAR systems for developmental toxicity. This document reports on the deliberations of the Working Group, which concluded that an improved process is needed for utilizing developmental toxicity data in the construction of statistically based SAR models. The process must be objective, reproducible, rational and transparent. Moreover, it must be informed by the expertise of developmental toxicologists and biologists and must be subject to peer review.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".