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Toxicity prediction based on artificial intelligence: A multidisciplinary overview
Author(s) -
Pérez Santín Efrén,
Rodríguez Solana Raquel,
González García Mariano,
García Suárez María Del Mar,
Blanco Díaz Gerardo David,
Cima Cabal María Dolores,
Moreno Rojas José Manuel,
López Sánchez José Ignacio
Publication year - 2021
Publication title -
wiley interdisciplinary reviews: computational molecular science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.126
H-Index - 81
eISSN - 1759-0884
pISSN - 1759-0876
DOI - 10.1002/wcms.1516
Subject(s) - multidisciplinary approach , computer science , artificial intelligence , european union , robustness (evolution) , biochemical engineering , risk analysis (engineering) , microbiology and biotechnology , engineering , business , chemistry , biology , political science , law , economic policy , biochemistry , gene
Abstract 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