ArticleArchives of toxicology2026
Learning the assay, not the hazard: how computational toxicity models inherit the artifacts of their high-throughput training data.
Article in Archives of toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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Abstract
High-throughput screening data have become the de facto ground truth for in silico toxicology. But a high-throughput "active" can reflect target engagement, non-specific cytotoxicity, or assay interference and the programmes that generate these data flag the latter two with dedicated counter-screens. When models are trained on the hit-calls without those flags, they can learn how the assay behaved rather than how the chemical harms. Because the confounders are structurally systematic, they are exactly the kind of signal a structure-based model will capture. This Commentary sets out the problem and four low-cost controls.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.