AI Medical Compendium Journal:
Journal of applied toxicology : JAT

Showing 1 to 10 of 18 articles

Respiratory Exposure to Agriculture Dust Extract Alters Gut Commensal Species and Key Metabolites in Mice.

Journal of applied toxicology : JAT
Exposure to agricultural dust containing antimicrobial-resistant pathogens poses significant health risks for workers in animal agriculture production. Beyond causing severe airway inflammation, pollutants are linked to intestinal diseases. Swine far...

The Mechanism of Bisphenol S-Induced Atherosclerosis Elucidated Based on Network Toxicology, Molecular Docking, and Machine Learning.

Journal of applied toxicology : JAT
The increasing prevalence of environmental pollutants has raised public concern about their potential role in diseases such as atherosclerosis (AS). Existing studies suggest that chemicals, including bisphenol S (BPS), may adversely affect cardiovasc...

Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition.

Journal of applied toxicology : JAT
Cytochrome P450 (CYP) enzymes are involved in the metabolism of approximately 75% of marketed drugs. Inhibition of the major drug-metabolizing P450s could alter drug metabolism and lead to undesirable drug-drug interactions. Therefore, it is of great...

ToxMPNN: A deep learning model for small molecule toxicity prediction.

Journal of applied toxicology : JAT
Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpo...

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches.

Journal of applied toxicology : JAT
The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evalu...

In silico prediction of hERG blockers using machine learning and deep learning approaches.

Journal of applied toxicology : JAT
The human ether-à-go-go-related gene (hERG) is associated with drug cardiotoxicity. If the hERG channel is blocked, it will lead to prolonged QT interval and cause sudden death in severe cases. Therefore, it is important to evaluate the hERG-blocking...

In silico prediction of chemical aquatic toxicity by multiple machine learning and deep learning approaches.

Journal of applied toxicology : JAT
Fish is one of the model animals used to evaluate the adverse effects of a chemical exposed to the ecosystem. However, its low throughput and relevantly high expense make it impossible to test all new chemicals in manufacture. Hence, using in silico ...

In silico prediction of potential drug-induced nephrotoxicity with machine learning methods.

Journal of applied toxicology : JAT
In recent years, drug-induced nephrotoxicity has been one of the main reasons for the failure of drug development. Early prediction of the nephrotoxicity for drug candidates is critical to the success of clinical trials. Therefore, it is very importa...

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach.

Journal of applied toxicology : JAT
Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the brain across a permeable blood brain barrier damages neurons and other supporting cells such as astroc...

In vitro and in silico genetic toxicity screening of flavor compounds and other ingredients in tobacco products with emphasis on ENDS.

Journal of applied toxicology : JAT
Electronic nicotine delivery systems (ENDS) are regulated tobacco products and often contain flavor compounds. Given the concern of increased use and the appeal of ENDS by young people, evaluating the potential of flavors to induce DNA damage is impo...