AIMC Topic: Toxicity Tests

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Multimodal deep learning for chemical toxicity prediction and management.

Scientific reports Jun 3, 2025
The accurate prediction of chemical toxicity is a crucial research focus in chemistry, biotechnology, and national defense. The development of comprehensive datasets for chemical toxicity prediction remains limited due to security constraints and the...
Deep Learning Toxicity Tests
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Model-driven high-throughput zebrafish embryo assay for evaluating whole effluent toxicity variation across 100 full-scale wastewater treatment plants.

Water research Apr 17, 2025
The zebrafish embryo is a valuable model for evaluating whole effluent toxicity (WET). However, the widely recognized acute toxicity indicator, based on International Organization of Standardization (ISO) methods, requires large numbers of embryos an...
Wastewater Animals Toxicity Tests Embryo, Nonmammalian Water Pollutants, Chemical Zebrafish
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Classification and regression machine learning models for predicting mixed toxicity of carbamazepine and its transformation products.

Environmental research Feb 8, 2025
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...
Machine Learning Water Pollutants, Chemical Anticonvulsants Acridines Models, Statistical Regression Analysis Toxicity Tests Vibrio Carbamazepine Quantitative Structure-Activity Relationship Proflavine
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Chemical Space Networks Enhance Toxicity Recognition via Graph Embedding.

Journal of chemical information and modeling Feb 6, 2025
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 ...
Humans Neural Networks, Computer Toxicity Tests
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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 Jan 10, 2025
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...
Toxicity Tests Hazardous Substances No-Observed-Adverse-Effect Level Humans Machine Learning Animals Risk Assessment
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Machine learning-based analysis of microfluidic device immobilized C. elegans for automated developmental toxicity testing.

Scientific reports Jan 2, 2025
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...
Lab-On-A-Chip Devices Caenorhabditis elegans Machine Learning Animals Image Processing, Computer-Assisted Toxicity Tests
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NeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion.

Environment international Dec 28, 2024
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 ...
Neurotoxicity Syndromes Neural Networks, Computer Blood-Brain Barrier Deep Learning Toxicity Tests Animals
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ToxSTK: A multi-target toxicity assessment utilizing molecular structure and stacking ensemble learning.

Computers in biology and medicine Dec 6, 2024
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...
Toxicity Tests Ensemble Learning Humans Machine Learning Risk Assessment Animals Quantitative Structure-Activity Relationship
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The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging.

Journal of hazardous materials Nov 30, 2024
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...
Humans Plastics Toxicity Tests Machine Learning Hazardous Substances Support Vector Machine Product Packaging
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FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Archives of toxicology Sep 18, 2024
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...
Reproduction Neural Networks, Computer Quantitative Structure-Activity Relationship Humans Computer Simulation Toxicity Tests
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