Learning the assay, not the hazard: how computational toxicity models inherit the artifacts of their high-throughput training data.

Iqbal MJ, Amjad T, Paz C

DOI: 10.1007/s00204-026-04512-x
Published: 2026 Jul 22
Container: Archives of toxicology
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1007/s00204-026-04512-x,
  title = {Learning the assay, not the hazard: how computational toxicity models inherit the artifacts of their high-throughput training data.},
  author = {Iqbal MJ and Amjad T and Paz C},
  year = {2026},
  journal = {Archives of toxicology},
  doi = {10.1007/s00204-026-04512-x},
  url = {https://doi.org/10.1007/s00204-026-04512-x}
}

RIS

TY  - JOUR
TI  - Learning the assay, not the hazard: how computational toxicity models inherit the artifacts of their high-throughput training data.
AU  - Iqbal MJ
AU  - Amjad T
AU  - Paz C
PY  - 2026
JO  - Archives of toxicology
DO  - 10.1007/s00204-026-04512-x
UR  - https://doi.org/10.1007/s00204-026-04512-x
ER  -

APA

MJ, I., T, A., & C, P. (2026). Learning the assay, not the hazard: how computational toxicity models inherit the artifacts of their high-throughput training data.. Archives of toxicology. https://doi.org/10.1007/s00204-026-04512-x

Source records

pubmed · retrieved 2026-09-26T17:09:19.005Z