Advancing Computational Toxicology by Interpretable Machine Learning. [PDF]
Environ Sci Technol, 2023 Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in ...
Jia X, Wang T, Zhu H.
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The rapid development of computational toxicology. [PDF]
Arch Toxicol, 2020 Many drugs have the potential to cause drug-induced liver injury (DILI); however, underlying mechanisms are diverse. The concept of adverse outcome pathways (AOPs) has become instrumental for risk assessment of drug class effects. We report AOPs specific for immune-mediated and drug hypersensitivity/allergic hepatitis by considering genomic, histo- and
Bolt HM, Hengstler JG.
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Navigating the Minefield of Computational Toxicology and Informatics: Looking Back and Charting a New Horizon. [PDF]
Front Toxicol, 2020 As we enter 2020, it is worth looking back at the development and progression of the computational toxicology discipline, how it has evolved and what some opportunities might be going forward.
Patlewicz G.
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Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery [PDF]
Journal of XenobioticsIn the field of computational chemistry, computer models are quickly and cheaply constructed to predict toxicology hazards and results, with no need for test material or animals as these computational predictions are often based on physicochemical ...
Jose I. Bueso-Bordils +3 more
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A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Study [PDF]
Biomolecules, 2019 In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies.
Shuaibing He +5 more
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Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon. [PDF]
Chem Res Toxicol, 2023 In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency.
Bassani D, Brigo A, Andrews-Morger A.
europepmc +2 more sources
In Vitro and Predictive Computational Toxicology Methods for the Neurotoxic Pesticide Amitraz and Its Metabolites. [PDF]
Brain Sci, 2023 The Varroa destructor parasite is responsible for varroasis in honeybees worldwide, the most destructive disease among parasitic diseases. Thus, different insecticides/acaricides have been widely used within beehives to control these parasitic diseases ...
Giorgini M +4 more
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The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. [PDF]
Toxicol Sci, 2019 The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data.
Thomas RS +41 more
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Usage of model combination in computational toxicology
Toxicology Letters, 2023 New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and ...
Rodríguez-Belenguer, Pablo +4 more
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Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity. [PDF]
Chem Res Toxicol, 2019 In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals.
Ciallella HL, Zhu H.
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