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Cited 28 time in webofscience Cited 38 time in scopus
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A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges

Authors
DanishuddinKumar, VikasFaheem, MohammadLee, Keun Woo
Issue Date
Feb-2022
Publisher
Elsevier BV
Keywords
Pharmacokinetics; QSAR; Chemical Big Data; Drug development
Citation
Drug Discovery Today, v.27, no.2, pp 529 - 537
Pages
9
Indexed
SCIE
SCOPUS
Journal Title
Drug Discovery Today
Volume
27
Number
2
Start Page
529
End Page
537
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/72535
DOI
10.1016/j.drudis.2021.09.013
ISSN
1359-6446
1878-5832
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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