** This centre has implemented and maintains a Quality Management System which fulfils the requirements of the ISO standard 9001:2000 AbstractSquid gelatin obtained from inner and outer tunics was hydrolyzed with Alcalase to isolate antioxidant peptide sequences. The ACE-inhibitory activity of the isolated peptides was also evaluated. After fractionation by ultrafiltration and size-exclusion chromatography into four fractions, the antioxidant activity of the peptide fractions was determined by radical scavenging ability and ferric reducing power. Fraction FIII showed the highest antioxidant activity, although slight differences could be expected in the antioxidant activity of the different fractions based on the amino acid composition.FIII was subjected to liquid chromatography and tandem mass spectrometry (LC-MS/MS) and two major compounds were identified: the compound with m/z 952.42, which could be mostly comprised by the carbohydrate fucose, and the peptide with m/z 1410.63. Three possible sequences were proposed for this peptide, and the contribution of Leu or Hyp residues to the antioxidant and ACE-inhibitory activities of the resulting sequence was evaluated. The presence of Leu residues in the peptide sequence in *Manuscript Click here to view linked References 2 replacement of Hyp seems to play an important role in the antioxidant and ACEinhibitory activity.

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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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