AIMC Topic: Toxicity Tests

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

Journal of applied toxicology : JAT Feb 26, 2024
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...
Neural Networks, Computer Humans Deep Learning Toxicity Tests
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Report of the First ONTOX Stakeholder Network Meeting: Digging Under the Surface of ONTOX Together With the Stakeholders.

Alternatives to laboratory animals : ATLA Jan 18, 2024
The first Stakeholder Network Meeting of the EU Horizon 2020-funded ONTOX project was held on 13-14 March 2023, in Brussels, Belgium. The discussion centred around identifying specific challenges, barriers and drivers in relation to the implementatio...
Animals Risk Assessment Toxicity Tests Humans Artificial Intelligence Adverse Outcome Pathways Belgium
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Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives.

Expert opinion on drug metabolism & toxicology Dec 29, 2023
INTRODUCTION: The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Dev...
Animals Child Toxicology Toxicity Tests Decision Making Artificial Intelligence Pregnancy Female Humans
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New approach methodologies in human regulatory toxicology - Not if, but how and when!

Environment international Jul 4, 2023
The predominantly animal-centric approach of chemical safety assessment has increasingly come under pressure. Society is questioning overall performance, sustainability, continued relevance for human health risk assessment and ethics of this system, ...
Artificial Intelligence Reproducibility of Results Risk Assessment Humans Toxicity Tests
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Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data.

Chemical research in toxicology Dec 7, 2022
The development of toxicity classification models using the ToxCast database has been extensively studied. Machine learning approaches are effective in identifying the bioactivity of untested chemicals. However, ToxCast assays differ in the amount of...
Bayes Theorem Biological Assay Toxicology Algorithms Logistic Models Toxicity Tests Machine Learning
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Raster plots machine learning to predict the seizure liability of drugs and to identify drugs.

Scientific reports Feb 10, 2022
In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to pre...
Forecasting Drug-Related Side Effects and Adverse Reactions Cells, Cultured Toxicity Tests Seizures Adult Induced Pluripotent Stem Cells Middle Aged Pharmaceutical Preparations Male Microelectrodes Dose-Response Relationship, Drug Liability, Legal Female Humans Neural Networks, Computer Machine Learning
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Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System.

International journal of molecular sciences Oct 6, 2021
In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysi...
Computer Simulation Deep Learning Pharmaceutical Preparations Quantitative Structure-Activity Relationship Models, Statistical Humans Algorithms Toxicity Tests Receptors, Cytoplasmic and Nuclear
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Emerging Technologies for In Vitro Inhalation Toxicology.

Advanced healthcare materials Jul 22, 2021
Respiratory toxicology remains a major research area in the 21st century since current scenario of airborne viral infection transmission and pollutant inhalation is expected to raise the annual morbidity beyond 2 million. Clinical and epidemiological...
Reproducibility of Results Toxicity Tests Humans Artificial Intelligence
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Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.

PLoS computational biology Jul 2, 2021
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...
Embryo, Nonmammalian Toxicology Toxicity Tests Neural Networks, Computer Animals Zebrafish Computational Biology Models, Chemical High-Throughput Screening Assays
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Safer chemicals using less animals: kick-off of the European ONTOX project.

Toxicology Jun 30, 2021
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...
Computer Simulation In Vitro Techniques Animals Gene Ontology Humans Toxicity Tests Animal Testing Alternatives Risk Assessment European Union Artificial Intelligence
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