Multiscale-Information-Embedded Universal Toxicity Prediction Framework
Bibliographic record
Abstract
Hazard identification and labeling of industrial chemicals and their released environmental pollutants are crucial for mitigating ecological and health risks. Comprehensive evaluation of multiple toxicity end points is essential to fully characterize chemical hazards. Existing deep-learning-based toxicity prediction models often exhibit poor generalizability, especially for rare toxicities with sparse data. Most studies fail to capture the three-dimensional (3D) spatial arrangement and stereochemical properties of chemicals, as well as the interrelated nature among end points, hindering accurate toxicity profiling. Here, we propose ToxScan, an SE(3)-equivariant multiscale model, as a universal toxicity prediction framework to address these issues. It incorporates 3D geometry information through a two-level molecular and atomic representation learning protocol. A parallel multiscale modeling and a multitask learning scheme are applied to learn universal toxicological characteristics. Results show that ToxScan achieves 7.8-37.6% improvements over state-of-the-art models for medium-/small-scale end points, demonstrates differentiation of structural analogues with contrasting toxicities, and maintains generalizability to environmental pollutants. Interpretability analysis at the atomic and molecular levels reveals identifiable atomic interaction patterns and potential structural alerts. Case studies reveal its capacity to detect subtle structural determinants while elucidating the mechanisms of pollutants. To facilitate user accessibility, we provide an intuitive web platform (https://funmg.dp.tech/Toxscan) for the rapid prediction of multiple toxicity end points of new compounds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".