Artificial Intelligence in Drug Safety and Metabolism
- PMID: 34731484
- DOI: 10.1007/978-1-0716-1787-8_22
Artificial Intelligence in Drug Safety and Metabolism
Abstract
The use of artificial intelligence methods in drug safety began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been endlessly expanding ever since and the models have become more complex. These approaches are now integrated into molecule risk assessment processes along with in vitro and in vivo methods. Today, artificial intelligence can be used in every phase of drug discovery and development, from profiling chemical libraries in early discovery, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life cycle management. This chapter provides an overview of artificial intelligence in drug safety and describes its application throughout the entire discovery and development process.
Keywords: Applicability Domain; Artificial Intelligence; Cardiotoxicity; Computational Toxicology; DILI; Deep Learning; Digital Pathology; Drug Safety; Experimental Error; Genotoxicity; Hepatotoxicity; Machine Learning; Metabolism; Pharmacokinetics; Similarity; Structure Alerts.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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