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Toxicity Tests

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FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Archives of toxicology
Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive tox...

Leveraging new approach methodologies: ecotoxicological modelling of endocrine disrupting chemicals to Danio rerio through machine learning and toxicity studies.

Toxicology mechanisms and methods
New approach methodologies (NAMs) offer information tailored to the intended application while reducing the use of animals. NAMs aim to develop quantitative structure-activity relationship (QSAR) and quantitive-Read-Across structure-activity relation...

The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging.

Journal of hazardous materials
Assessing chemical toxicity in materials like plastic packaging is critical to safeguarding public health. This study presents the development of classification-based machine learning models to predict the toxicity of chemicals associated with plasti...

NeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion.

Environment international
Chemically induced neurotoxicity is a critical aspect of chemical safety assessment. Traditional and costly experimental methods call for the development of high-throughput virtual screening. However, the small datasets of neurotoxicity have limited ...

ToxSTK: A multi-target toxicity assessment utilizing molecular structure and stacking ensemble learning.

Computers in biology and medicine
Drug registration requires risk assessment of new active pharmaceutical ingredients or excipients to ensure they are safe for human health and the environment. However, traditional risk assessment is expensive and relies heavily on animal testing. Ma...

Quantitative prediction of toxicological points of departure using two-stage machine learning models: A new approach methodology (NAM) for chemical risk assessment.

Journal of hazardous materials
Point of departure (POD) is a concept used in risk assessment to calculate the reference dose of exposure that is likely to have no appreciable risk on health. POD can be directly utilized from no observed adverse effect levels (NOAEL) which is the d...

Machine learning-based analysis of microfluidic device immobilized C. elegans for automated developmental toxicity testing.

Scientific reports
Developmental toxicity (DevTox) tests evaluate the adverse effects of chemical exposures on an organism's development. Although current testing primarily relies on large mammalian models, the emergence of new approach methodologies (NAMs) is encourag...

TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.

Methods in molecular biology (Clifton, N.J.)
Developmental toxicity is key human health endpoint, especially relevant for safeguarding maternal and child well-being. It is an object of increasing attention from international regulatory bodies such as the US EPA (US Environmental Protection Agen...

Classification and regression machine learning models for predicting mixed toxicity of carbamazepine and its transformation products.

Environmental research
Carbamazepine (CBZ) and its transformation products (TPs) often occur in aquatic environments in the form of mixtures, posing potential risks to ecosystems. However, establishing standardized protocols for synthesizing, isolating, and acquiring these...

Chemical Space Networks Enhance Toxicity Recognition via Graph Embedding.

Journal of chemical information and modeling
Chemical space networks (CSNs) are a new effective strategy for detecting latent chemical patterns irrespective of defined coordinate systems based on molecular descriptors and fingerprints. CSNs can be a new powerful option as a new approach method ...