A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges |
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Affiliation: | 1. Department of Bio & Medical Big Data (BK4), Division of Life Sciences, Research Institute of Natural Sciences (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Republic of Korea;2. Department of Biotechnology, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India |
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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. |
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Keywords: | Pharmacokinetics QSAR Chemical Big Data Drug development |
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