AIMC Topic: Toxicity Tests

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

Toxicology in vitro : an international journal published in association with BIBRA Apr 9, 2021
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 Biological Assay Toxicity Tests Hazardous Substances Logistic Models Models, Theoretical Decision Trees
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Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.

Chemical research in toxicology Dec 23, 2020
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...
Quantitative Structure-Activity Relationship Toxicity Tests Humans Machine Learning Models, Molecular
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Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity.

Chemical research in toxicology Nov 29, 2020
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...
Humans Machine Learning Environmental Pollutants Toxicity Tests
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Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

Scientific reports Sep 14, 2020
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 ...
Drug Resistance, Bacterial Escherichia coli Mutation Machine Learning Anti-Bacterial Agents DNA Gyrase Intracellular Signaling Peptides and Proteins Genes, Bacterial Escherichia coli Proteins Toxicity Tests Ciprofloxacin Inhibitory Concentration 50
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GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics.

Chemical research in toxicology Aug 26, 2020
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...
Molecular Structure Biotransformation Toxicity Tests Humans Machine Learning Xenobiotics
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Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling.

Archives of toxicology Jun 12, 2020
Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid ...
Proto-Oncogene Mas High-Throughput Screening Assays Cellular Senescence Machine Learning Toxicity Tests Phenotype Cell Death Transcription Factor RelA Risk Assessment Hepatocytes Chemical and Drug Induced Liver Injury Image Processing, Computer-Assisted DNA Damage Humans Primary Cell Culture Hep G2 Cells Microscopy, Fluorescence Actin Cytoskeleton Dose-Response Relationship, Drug
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TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

Methods (San Diego, Calif.) May 21, 2020
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...
Humans Forecasting Drug Discovery Cheminformatics Pharmacology, Clinical Deep Learning Datasets as Topic Toxicity Tests
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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 Nov 7, 2019
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...
Sperm Count Dioxolanes Epididymis Sperm Motility Triazoles Machine Learning Toxicity Tests Animals Spermatozoa Male Support Vector Machine Fungicides, Industrial Rats, Wistar Testis Rats DNA Damage Spectrum Analysis, Raman
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An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions.

Scientific reports Sep 26, 2019
In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans or pre-clinical species. For small molecules, less animal data is often needed because there are a ...
Cell Membrane Cell Line Animals Toxicity Tests Drug Discovery Mice Spectrophotometry Artificial Intelligence Software Quantum Dots Lysosomes
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Machine learning for mixture toxicity analysis based on high-throughput printing technology.

Talanta Aug 26, 2019
Analysis on mixture toxicity (Mix-tox) of the multi-chemical space is constantly followed with interest for many researchers. Conventional toxicity tests with time-consuming and costly operations make researchers can only establish some toxicity pred...
Printing Machine Learning Toxicity Tests
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