DOAJ Open Access 2018

Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions

Christoph Helma David Vorgrimmler Denis Gebele Martin Gütlein Barbara Engeli +3 lainnya

Abstrak

This study compares the accuracy of (Q)SAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar) algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain) are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.

Topik & Kata Kunci

Therapeutics. Pharmacology

Penulis (8)

Christoph Helma

David Vorgrimmler

Denis Gebele

Martin Gütlein

Barbara Engeli

Jürg Zarn

Benoit Schilter

Elena Lo Piparo

Format Sitasi

APA MLA BibTeX

Helma, C., Vorgrimmler, D., Gebele, D., Gütlein, M., Engeli, B., Zarn, J. et al. (2018). Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions. https://doi.org/10.3389/fphar.2018.00413

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Informasi Jurnal

Tahun Terbit: 2018
Sumber Database: DOAJ
DOI: 10.3389/fphar.2018.00413
Akses: Open Access ✓