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• W3204865406 abstract "Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data." @default.
• W3204865406 created "2021-10-11" @default.
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• W3204865406 date "2022-02-01" @default.
• W3204865406 modified "2023-10-01" @default.
• W3204865406 title "A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges" @default.
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• W3204865406 doi "https://doi.org/10.1016/j.drudis.2021.09.013" @default.
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