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    Toxicity prediction based on artificial intelligence: A multidisciplinary overview

    Autor: 
    Peréz-Santín, Efren (1)
    ;
    Rodríguez Solana, Raquel
    ;
    González García, Mariano (1)
    ;
    García-Suárez, María del Mar (1)
    ;
    Blanco Díaz, Gerardo David (1)
    ;
    Cima-Cabal, María Dolores (1)
    ;
    Moreno Rojas, José Manuel
    ;
    López-Sánchez, José Ignacio (1)
    Fecha: 
    2021
    Palabra clave: 
    artificial intelligence; in silico; multidisciplinar; prediction; toxicity; Scopus; JCR
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/13121
    DOI: 
    https://doi.org/10.1002/wcms.1516
    Dirección web: 
    https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1516
    Resumen:
    The use and production of chemical compounds are subjected to strong legislative pressure. Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory aspects for a multitude of industries, such as chemical, pharmaceutical, or food, due to direct harm to humans, animals, plants, or the environment. Simultaneously, there are growing demands on the authorities to replace traditional in vivo toxicity tests carried out on laboratory animals (e.g., European Union REACH/3R principles, Tox21 and ToxCast by the U.S. government, etc.) with in silica computational models. This is not only for ethical aspects, but also because of its greater economic and time efficiency, as well as more recently because of their superior reliability and robustness than in vivo tests, mainly since the entry into the scene of artificial intelligence (AI)-based models, promoting and setting the necessary requirements that these new in silico methodologies must meet. 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. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning.
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