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In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method

Title
In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method
Authors
Lee, J. H.Basith, S.Cui, M.Kim, B.Choi, S.
Ewha Authors
최선
SCOPUS Author ID
최선scopus
Issue Date
2017
Journal Title
SAR AND QSAR IN ENVIRONMENTAL RESEARCH
ISSN
1062-936XJCR Link1029-046XJCR Link
Citation
vol. 28, no. 10, pp. 863 - 874
Keywords
Cytochrome P450classificationmachine-learningmultiple-category modellingdescriptorsinhibitorsnon-inhibitors
Publisher
TAYLOR & FRANCIS LTD
Indexed
SCI; SCIE; SCOPUS WOS scopus
Abstract
The cytochrome P450 (CYP) enzyme superfamily is involved in phase I metabolism which chemically modifies a variety of substrates via oxidative reactions to make them more water-soluble and easier to eliminate. Inhibition of these enzymes leads to undesirable effects, including toxic drug accumulations and adverse drug-drug interactions. Hence, it is necessary to develop in silico models that can predict the inhibition potential of compounds for different CYP isoforms. This study focused on five major CYP isoforms, including CYP1A2, 2C9, 2C19, 2D6 and 3A4, that are responsible for more than 90% of the metabolism of clinical drugs. The main aim of this study is to develop a multiple-category classification model (MCM) for the major CYP isoforms using a Laplacian-modified naive Bayesian method. The dataset composed of more than 4500 compounds was collected from the PubChem Bioassay database. VolSurf+ descriptors and FCFP_8 fingerprint were used as input features to build classification models. The results demonstrated that the developed MCM using Laplacian-modified naive Bayesian method was successful in classifying inhibitors and non-inhibitors for each CYP isoform. Moreover, the accuracy, sensitivity and specificity values for both training and test sets were above 80% and also yielded satisfactory area under the receiver operating characteristic curve and Matthews correlation coefficient values.
DOI
10.1080/1062936X.2017.1399925
Appears in Collections:
약학대학 > 약학과 > Journal papers
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