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) (KAR id:47803)
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Official URL http://dx.doi.org/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 |
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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: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Alex Freitas |
Date Deposited: | 27 Mar 2015 17:28 UTC |
Last Modified: | 16 Feb 2021 13:23 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/47803 (The current URI for this page, for reference purposes) |
Freitas, Alex A.: | ![]() |
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