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...
Development and application of cell-based bioassay in aquatic toxicity assessment.
Aquatic toxicology (Amsterdam, Netherlands)
May 26, 2025
Cell-based bioassays provide a dynamic and physiologically relevant platform for investigating cellular responses to chemical, biological, and environmental stimuli. Unlike traditional biochemical assays, cell-based bioassays utilize living cells to ...
Artificial intelligence (AI)-driven morphological assessment of zebrafish larvae for developmental toxicity chemical screening.
Aquatic toxicology (Amsterdam, Netherlands)
May 20, 2025
Screening chemicals using the zebrafish embryo developmental toxicity assay requires visual assessment of larval morphological changes based on images by experienced screeners. The process is time-consuming and prone to subjectivity. However, deep le...
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...
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 ...
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...
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...
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 ...
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...
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...
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