In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method
- 주제(키워드) Cytochrome P450 , classification , machine-learning , multiple-category modelling , descriptors , inhibitors , non-inhibitors
- 주제(기타) Chemistry, Multidisciplinary; Computer Science, Interdisciplinary Applications; Environmental Sciences; Mathematical & Computational Biology; Toxicology
- 설명문(일반) [Choi, S.] Ewha Womans Univ, Coll Pharm, Natl Leading Res Lab NLRL Mol Modeling & Drug Des, Seoul, South Korea; Ewha Womans Univ, Grad Sch Pharmaceut Sci, Seoul, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 TAYLOR & FRANCIS LTD
- 발행년도 2017
- 회의명 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (CMTPI)
- 개최지 INDIA
- 일자 OCT 27-30, 2017
- URI http://www.dcollection.net/handler/ewha/000000150092
- 본문언어 영어
- Published As http://dx.doi.org/10.1080/1062936X.2017.1399925
초록/요약
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.
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