AI Medical Compendium Journal:
Toxicological sciences : an official journal of the Society of Toxicology

Showing 1 to 10 of 17 articles

A Systematic Strategy for Screening and Application of Specific Biomarkers in Hepatotoxicity Using Metabolomics Combined With ROC Curves and SVMs.

Toxicological sciences : an official journal of the Society of Toxicology Jan 18, 2016
Current studies that evaluate toxicity based on metabolomics have primarily focused on the screening of biomarkers while largely neglecting further verification and biomarker applications. For this reason, we used drug-induced hepatotoxicity as an ex...
Metabolomics Liver Chromatography, High Pressure Liquid Carbon Tetrachloride Acetaminophen Atorvastatin ROC Curve Rats, Wistar Support Vector Machine Animals Male Chemical and Drug Induced Liver Injury Biomarkers Sensitivity and Specificity
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Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches.

Toxicological sciences : an official journal of the Society of Toxicology Jul 21, 2015
Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligan...
Expert Systems Computational Biology Catalytic Domain Databases, Protein Molecular Conformation Nerve Tissue Proteins Animals Models, Molecular Ligands Molecular Docking Simulation GPI-Linked Proteins Acetylcholinesterase Imaging, Three-Dimensional Insecticides Humans Cholinesterase Inhibitors Nootropic Agents Quantitative Structure-Activity Relationship Artificial Intelligence Binding Sites Peptide Mapping
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Development of machine learning-based quantitative structure-activity relationship models for predicting plasma half-lives of drugs in six common food animal species.

Toxicological sciences : an official journal of the Society of Toxicology Jan 1, 2025
Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure-activity relationshi...
Quantitative Structure-Activity Relationship Veterinary Drugs Turkeys Goats Half-Life Sheep Machine Learning Neural Networks, Computer Algorithms Swine Animals Cattle Chickens
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An in vitro and machine learning framework for quantifying serum albumin binding of per- and polyfluoroalkyl substances.

Toxicological sciences : an official journal of the Society of Toxicology Jan 1, 2025
Per- and polyfluoroalkyl substances (PFAS) are a diverse class of anthropogenic chemicals; many are persistent, bioaccumulative, and mobile in the environment. Worldwide, PFAS bioaccumulation causes serious adverse health impacts, yet the physiochemi...
Environmental Pollutants Humans Fluorocarbons Machine Learning Serum Albumin, Human Protein Binding
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Interpretable predictive models of genome-wide aryl hydrocarbon receptor-DNA binding reveal tissue-specific binding determinants.

Toxicological sciences : an official journal of the Society of Toxicology Nov 28, 2023
The aryl hydrocarbon receptor (AhR) is an inducible transcription factor whose ligands include the potent environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Ligand-activated AhR binds to DNA at dioxin response elements (DREs) conta...
Humans Genome, Human Organ Specificity Machine Learning Receptors, Aryl Hydrocarbon Basic Helix-Loop-Helix Transcription Factors
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Profiling mechanisms that drive acute oral toxicity in mammals and its prediction via machine learning.

Toxicological sciences : an official journal of the Society of Toxicology May 12, 2023
We present a mechanistic machine-learning quantitative structure-activity relationship (QSAR) model to predict mammalian acute oral toxicity. We trained our model using a rat acute toxicity database compiled by the US National Toxicology Program. We ...
Machine Learning Databases, Factual Quantitative Structure-Activity Relationship Toxicity Tests, Acute Animals Rats Mammals
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Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.

Toxicological sciences : an official journal of the Society of Toxicology Jan 31, 2023
Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorptio...
Models, Biological Machine Learning Risk Assessment Drug Development Neural Networks, Computer Artificial Intelligence
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Machine Learning and Artificial Intelligence in Toxicological Sciences.

Toxicological sciences : an official journal of the Society of Toxicology Aug 25, 2022
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different ...
Animals High-Throughput Screening Assays Quantitative Structure-Activity Relationship Adverse Outcome Pathways Artificial Intelligence Machine Learning
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Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies-A Case Study With Toxicogenomics.

Toxicological sciences : an official journal of the Society of Toxicology Mar 28, 2022
Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide effort in toxicology toward "reducing, refining, and replacing" animal use. Here, we proposed a deep ...
Toxicogenetics Genomics Artificial Intelligence Rats Biomarkers Animals Transcriptome
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Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography.

Toxicological sciences : an official journal of the Society of Toxicology Aug 3, 2021
Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electroencephalography has been known to be effective for the detection of seizure liability, no establish...
Seizures Deep Learning Artificial Intelligence Pharmaceutical Preparations Rats Animals Electroencephalography
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