Coping with unbalanced class data sets in oral absorption models

Newby, Danielle, Freitas, Alex A and Ghafourian, Taravat (2013) Coping with unbalanced class data sets in oral absorption models. Journal of Chemical Information and Modeling, 53 (2). pp. 461-474. ISSN 1549-9596

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Class imbalance occurs frequently in drug discovery data sets. In oral absorption data sets, in the literature, there are considerably more highly absorbed compounds compared to poorly absorbed compounds. This produces models that are biased toward highly absorbed compounds which lack generalization to industry settings where more early stage drug candidates are poorly absorbed. This paper presents two strategies to cope with unbalanced class data sets: undersampling the majority high absorption class and misclassification costs using classification decision trees. The published data set by Hou et al. J. Chem. Inf. Model.2007, 47, 208-218, which contained percentage human intestinal absorption of 645 drug and drug-like compounds, was used for the development and validation of classification trees using classification and regression tree (C&RT) analysis. The results indicate that undersampling the majority class, highly absorbed compounds, leads to a balanced distribution (50:50) training set which can achieve better accuracies for poorly absorbed compounds, whereas the biased training set achieved higher accuracies for highly absorbed compounds. The use of misclassification costs resulted in improved class predictions, when applied to reduce false positives or false negatives. Moreover, it was shown that the classical overall accuracy measure used in many publications is particularly misleading in the case of unbalanced data sets and more appropriate measures presented here may be used for a more realistic assessment of the classification models' performance. Thus, these strategies offer improvements to cope with unbalanced class data sets to obtain classification models applicable in industry. © 2013 American Chemical Society.

Item Type: Article
Keywords: Class imbalance; Class prediction; Classification and regression tree; Classification decision; Classification models; Classification trees; Data set; Drug candidates; Drug discovery; False negatives; False positive; Human intestinal absorptions; Misclassification costs; Oral absorption; Overall accuracies; Training sets; Unbalanced data; Under-sampling, Decision trees; Drug products, Classification (of information), absorption; article; biological model; decision tree; drug database; drug development; human; methodology; oral drug administration; regression analysis, Absorption; Administration, Oral; Databases, Pharmaceutical; Decision Trees; Drug Discovery; Humans; Models, Biological; Regression Analysis
Schools and Departments: School of Life Sciences > Biochemistry
Depositing User: Taravat Ghafourian
Date Deposited: 30 Nov 2017 16:27
Last Modified: 02 Jul 2019 17:17

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