What is it about?
The Ames test is an in vitro assay widely used to predict mutagenicity of chemicals. Quantitative structure−activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable chemical compounds, impurities and metabolites that are difficult to examine using the Ames test. To check and improve the (Q)SAR models the Division of Genetics and Mutagenesis of the National Institute of Health Sciences (Japan) has conducted the Second Ames/QSAR International Challenge Project (2020–2022) . It distributed a training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, to the participating 21 teams from 11 countries, asking them to predict the Ames mutagenicity of each trial chemical using various Ames/QSAR models. Results are reported and discussed.
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Why is it important?
Many QSAR models are built with a small number of data. Here the dataset is much bigger, and it is really unbalanced, making it difficult to obtain both high sensitivity and specificity.
Perspectives
Many models show that the prediction accuracy of models is very near to the reproducibility of the in vitro Ames test, making the models appealing for real use in prioritization and classification.
Prof Giuseppina Carla Gini
Politecnico di Milano
Read the Original
This page is a summary of: Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project, SAR and QSAR in Environmental Research, December 2023, Taylor & Francis,
DOI: 10.1080/1062936x.2023.2284902.
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