Toxicity prediction based on artificial intelligence: A multidisciplinary overview
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Summary
This review offers a multidisciplinary overview of the state of the art in the application of AI‐based methodologies for the fulfillment of regulatory‐related toxicological issues.
- Type
- article
- Published
- 2021-02-01
- Cited by
- 122
- References
- 229
- OpenAlex
- https://openalex.org/W3127020663
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:234080887
Keywords
Multidisciplinary approach, Computer science, Artificial intelligence, European union, Robustness (evolution)
References
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- Fractal colour: a new approach for evaluation of acrylamide contents in biscuits.
- Developing the function of 'Magnitude-of-Effect' (MoE) for artificial neural networks to demonstrate the causal effect of exposure variables on outcome variable.
- Differences in Birth Weight Associated with the 2008 Beijing Olympics Air Pollution Reduction: Results from a Natural Experiment
- Distribution and sources of polycyclic aromatic hydrocarbons in the surface sediments of Gorgan Bay, Caspian Sea.
Cited by
- Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
- The review of food safety inspection system based on artificial intelligence, image processing, and robotic
- Probabilistic Risk Assessment – The Keystone for the Future of Toxicology
- Alternative Plasticizers As Emerging Global Environmental and Health Threat: Another Regrettable Substitution?
- Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency
- Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications.
- Methodology to classify hazardous compounds via deep learning based on convolutional neural networks
- Multitask Deep Neural Networks for Ames Mutagenicity Prediction
- Molecular mechanism of the anti-inflammatory effects of plant essential oils: A systematic review.
- Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors.
- ToxMVA: An end-to-end multi-view deep autoencoder method for protein toxicity prediction
- Assessing chemical hazard and unraveling binding affinity of priority pollutants to lignin modifying enzymes for environmental remediation.
- Traditional Machine and Deep Learning for Predicting Toxicity Endpoints
- In vitro Assessment of Anti-Microbial Activity of Aloe vera (Barbadensis miller) Supported through Computational Studies
- Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review
- Creating 3D Objects with Integrated Electronics via Multiphoton Fabrication In Vitro and In Vivo
- Next Generation Radiotheranostics Promoting Precision Medicine.
- Artificial intelligence and nanotechnology for cervical cancer treatment: Current status and future perspectives
- A Horizon Scan to Support Chemical Pollution–Related Policymaking for Sustainable and Climate‐Resilient Economies
- Learning from real world data about combinatorial treatment selection for COVID-19
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