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Mutagenicity Tests

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Multitask Deep Neural Networks for Ames Mutagenicity Prediction.

Journal of chemical information and modeling
The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of , the vast majority of the published in silico models ...

Mechanistic Task Groupings Enhance Multitask Deep Learning of Strain-Specific Ames Mutagenicity.

Chemical research in toxicology
The Ames test is a gold standard mutagenicity assay that utilizes various strains with and without S9 fraction to provide insights into the mechanisms by which a chemical can mutate DNA. Multitask deep learning is an ideal framework for developing Q...

DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application.

Regulatory toxicology and pharmacology : RTP
The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the In...

Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

Environmental and molecular mutagenesis
This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seve...

AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks.

Computers in biology and medicine
Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained signific...

DeepRA: A novel deep learning-read-across framework and its application in non-sugar sweeteners mutagenicity prediction.

Computers in biology and medicine
Non-sugar sweeteners (NSSs) or artificial sweeteners have long been used as food chemicals since World War II. NSSs, however, also raise a concern about their mutagenicity. Evaluating the mutagenic ability of NSSs is crucial for food safety; this ste...

Deep active learning with high structural discriminability for molecular mutagenicity prediction.

Communications biology
The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecul...

Machine learning enhances genotoxicity assessment using MultiFlow® DNA damage assay.

Environmental and molecular mutagenesis
Genotoxicity is a critical determinant for assessing the safety of pharmaceutical drugs, their metabolites, and impurities. Among genotoxicity tests, mechanistic assays such as the MultiFlow® DNA damage assay (MFA) allows the investigations on mode o...

Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches.

Journal of chemical information and modeling
Nitroaromatic compounds (NAs) are widely used in industrial applications but pose significant genotoxic risks, necessitating accurate mutagenicity prediction for chemical safety assessments. This study integrates conceptual density functional theory ...

Explainable no-code OECD-compliant machine learning models to predict the mutagenic activity of polycyclic aromatic hydrocarbons and their radical cation metabolites.

The Science of the total environment
Polycyclic aromatic hydrocarbons (PAHs) are persistent pollutants with well-known genotoxic and mutagenic effects, posing risks to ecosystems and human health. Their hydrophobic nature promotes accumulation in soils and aquatic environments, increasi...