AIMC Topic: Mutagenicity Tests

Clear Filters Showing 1 to 10 of 34 articles

A Simple Framework for Collaborative Development of Predictive Models Trained on Proprietary Data.

Journal of chemical information and modeling
We present a simple methodology that allows the building and sharing of predictive models without compromising the confidentiality of the structures of the training series. Multiple shared models can be used to obtain ensemble models, providing bette...

Enhancing Toxicity Prediction of Synthetic Chemicals via Novel SMILES Fragmentation and Interpretable Deep Learning.

Journal of chemical information and modeling
Toxicity prediction and identification of structural alerts (SAs) for synthetic chemicals are critical for assessing risks to environmental and human health. Traditional methods, which rely heavily on molecular descriptors, often suffer from poor int...

Regulatory practices on the genotoxicity testing of nanomaterials and outlook for the future.

Regulatory toxicology and pharmacology : RTP
The toxicity of nanomaterials(NMs) is closely tied to their physicochemical properties, such as size, shape, surface chemistry, stability in biological medium, and state of agglomeration as well to their uptake by cells. Key deficiencies in standardi...

Safety Evaluation of Serendipity Berry Sweet Protein From Komagataella phaffii.

Journal of applied toxicology : JAT
Serendipity Berry Sweet Protein (sweelin) is a novel hyper-sweet thermophilic protein designed using Artificial Intelligence Computational Protein Design (AI-CPD) to improve the stability and sensory profile of the protein found in serendipity berry ...

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...

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 ...

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...

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...

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...

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...