AIMC Topic: Toxicity Tests

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Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.

PLoS computational biology
There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) an...

Safer chemicals using less animals: kick-off of the European ONTOX project.

Toxicology
The 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, is receiving increasing attention around the world, and has found its way to legislation, in particular in the European Union. This is aligned by continuing...

Multi-label classification and label dependence in in silico toxicity prediction.

Toxicology in vitro : an international journal published in association with BIBRA
Most computational predictive models are specifically trained for a single toxicity endpoint and lack the ability to learn dependencies between endpoints, such as those targeting similar biological pathways. In this study, we compare the performance ...

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.

Chemical research in toxicology
In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from studies toward studies. Currently, methods together with other computational methods such as quantitative structure-activity relati...

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity.

Chemical research in toxicology
The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an integrated approach to deduce the molecular targets and biological pathways involved in chemically induced toxicity for eight common human organ leve...

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

Scientific reports
It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a ...

GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics.

Chemical research in toxicology
Predicting the structures of metabolites formed in humans can provide advantageous insights for the development of drugs and other compounds. Here we present GLORYx, which integrates machine learning-based site of metabolism (SoM) prediction with rea...

TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

Methods (San Diego, Calif.)
At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a correspond...

Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods.

Environmental science and pollution research international
Difenoconazole is a fungicide extensively used in agriculture. The aim of this study was to evaluate the effects of difenoconazole fungicide on the sperm quality of rats. Wistar rats were divided into four groups: control and exposed to 5 (D5), 10 (D...