AIMC Topic: Mutagens

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Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split.

The Science of the total environment Jan 28, 2024
Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY dat...
Risk Assessment Mutagens Biometry Neural Networks, Computer Databases, Factual
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A deep-learning approach for identifying prospective chemical hazards.

Toxicology Dec 15, 2023
With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identifi...
Humans Receptors, Estrogen Prospective Studies Animals Hazardous Substances Deep Learning Risk Assessment Mutagens
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DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application.

Regulatory toxicology and pharmacology : RTP Aug 25, 2023
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...
Mutagenesis Mutagenicity Tests Deep Learning Mutagens
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Mechanistic Task Groupings Enhance Multitask Deep Learning of Strain-Specific Ames Mutagenicity.

Chemical research in toxicology Jul 21, 2023
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...
Neural Networks, Computer Mutagens Mutagenesis Mutagenicity Tests Deep Learning DNA
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Multitask Deep Neural Networks for Ames Mutagenicity Prediction.

Journal of chemical information and modeling Sep 6, 2022
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 ...
Mutagenicity Tests Mutagens Mutagenesis Neural Networks, Computer Computer Simulation Reproducibility of Results
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Machine learning - Predicting Ames mutagenicity of small molecules.

Journal of molecular graphics & modelling Sep 5, 2021
In modern drug discovery, detection of a compound's potential mutagenicity is crucial. However, the traditional method of mutagenicity detection using the Ames test is costly and time consuming as the compounds need to be synthesised and then tested ...
Mutagenesis Support Vector Machine Mutagens Machine Learning Computer Simulation
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Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

Archives of toxicology Jul 10, 2021
The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it re...
Humans Automation, Laboratory Deep Learning Dose-Response Relationship, Drug Carbamates Cell Line Flow Cytometry Micronucleus Tests DNA Damage Cytokinesis Mutagens Methyl Methanesulfonate Benzimidazoles
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QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.

Molecular diversity Jun 19, 2021
Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models can be constructed from 2D molecular graphs without computing chemical descriptors. Two graph...
Neural Networks, Computer Algorithms Quantitative Structure-Activity Relationship Models, Theoretical Drug Discovery Mutagenesis Molecular Structure Bayes Theorem Deep Learning Mutagens
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A deep neural network-based approach for prediction of mutagenicity of compounds.

Environmental science and pollution research international Apr 24, 2021
We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of m...
Support Vector Machine Mutagens Neural Networks, Computer Machine Learning Reproducibility of Results Animals
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MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints.

Interdisciplinary sciences, computational life sciences Jan 27, 2021
An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In ...
Mutagens Neural Networks, Computer Mutagenesis Drug Discovery
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