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Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Freitas, Alex A., Limbu, Kriti, Ghafourian, Taravat (2015) Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients. Journal of Cheminformatics, 7 (6). ISSN 1758-2946. (doi:10.1186/s13321-015-0054-x)

Abstract

Background

Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied.

Conclusions

Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models.

Item Type: Article
DOI/Identification number: 10.1186/s13321-015-0054-x
Uncontrolled keywords: data mining, machine learning, volume of distribution, pharmacokinetics, cheminformatics, pharmaceutical sciences
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 27 Mar 2015 17:28 UTC
Last Modified: 04 Feb 2020 04:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/47803 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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