AIMC Topic: Mutagens

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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 Mar 18, 2025
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...
Environmental Monitoring Models, Chemical Environmental Pollutants Machine Learning Risk Assessment Mutagenicity Tests Cations Organisation for Economic Co-Operation and Development Polycyclic Aromatic Hydrocarbons Mutagens
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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 Feb 27, 2025
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 ...
Density Functional Theory Mutagens Nitro Compounds Mutagenicity Tests Quantitative Structure-Activity Relationship Machine Learning
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Machine learning enhances genotoxicity assessment using MultiFlow® DNA damage assay.

Environmental and molecular mutagenesis Dec 30, 2024
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...
Machine Learning Algorithms Computer Simulation Humans Mutagenicity Tests DNA Damage Mutagens
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Effect-directed analysis of genotoxicants in food packaging based on HPTLC fractionation, bioassays, and toxicity prediction with machine learning.

Analytical and bioanalytical chemistry Nov 23, 2024
Many chemicals in food packaging can leach as complex mixtures to food, potentially including substances hazardous to consumer health. Detecting and identifying all of the leachable chemicals are impractical with current analytical instrumentation an...
Food Packaging Biological Assay Mutagens Chromatography, Thin Layer Tandem Mass Spectrometry Machine Learning Animals
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Synergizing Machine Learning, Conceptual Density Functional Theory, and Biochemistry: No-Code Explainable Predictive Models for Mutagenicity in Aromatic Amines.

Journal of chemical information and modeling Nov 11, 2024
This study synergizes machine learning (ML) with conceptual density functional theory (CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic amines (AAs) with a fully No-Code methodology using a comprehensive data s...
Mutagens Quantitative Structure-Activity Relationship Machine Learning Density Functional Theory Amines
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Deep active learning with high structural discriminability for molecular mutagenicity prediction.

Communications biology Aug 31, 2024
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...
Mutagens Mutagenicity Tests Humans Algorithms Drug Discovery Computer Simulation Deep Learning
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DeepRA: A novel deep learning-read-across framework and its application in non-sugar sweeteners mutagenicity prediction.

Computers in biology and medicine Jun 12, 2024
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...
Mutagens Neural Networks, Computer Algorithms Mutagenicity Tests Sweetening Agents Deep Learning
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A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

Archives of toxicology May 18, 2024
The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocar...
Carcinogens Carcinogenicity Tests Gene Expression Profiling Mutagens Male Rats Oligonucleotide Array Sequence Analysis Support Vector Machine Reproducibility of Results Liver Risk Assessment Administration, Oral Animals
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Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

Environmental and molecular mutagenesis May 17, 2024
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...
Data Visualization Mutagens Mutagenicity Tests Algorithms Cell Line Biomarkers Humans Principal Component Analysis Cluster Analysis Machine Learning
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AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks.

Computers in biology and medicine May 8, 2024
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...
Neural Networks, Computer Mutagenicity Tests Mutagens
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