Multiple Instance Learning Improves Ames Mutagenicity Prediction for Problematic Molecular Species-Reference-Cited by-同舟云学术

Multiple Instance Learning Improves Ames Mutagenicity Prediction for Problematic Molecular Species

Published:2023-07-21 Issue:8 Volume:36 Page:1227-1237
ISSN:0893-228X
Container-title:Chemical Research in Toxicology
language:en
Short-container-title:Chem. Res. Toxicol.

Author:

Feeney Samuel V.¹,Lui Raymond¹^ORCID,Guan Davy¹^ORCID,Matthews Slade¹^ORCID

Affiliation:

1. Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 2006, Australia

Publisher

American Chemical Society (ACS)

Subject

Toxicology,General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.2c00372

Reference37 articles.

1. EMA. ICH M7 Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk. European Medicines Agency, 2021. https://www.ema.europa.eu/en/ich-m7-assessment-control-dna-reactive-mutagenic-impurities-pharmaceuticals-limit-potential (accessed 2021).

2. An Improved Bacterial Test System for the Detection and Classification of Mutagens and Carcinogens

3. Revised methods for the Salmonella mutagenicity test

4. Carcinogens are Mutagens: A Simple Test System Combining Liver Homogenates for Activation and Bacteria for Detection

5. Improvement of quantitative structure–activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project

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