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An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network
Wang, YH; Li, Y; Yang, SL; Yang, L; Yang L(杨凌); Yang L(杨凌)
KeywordBack-propagation Neural Network Bayesian-regularized Neural Network Flavonoid Log K-d Partial Least Squares Analysis P-glycoprotein Quantitative Structure-activity Relationship
Source PublicationJOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
2005-03-01
DOI10.1007/s10822-005-3321-5
Volume19Issue:3Pages:137-147
Indexed BySCI
SubtypeArticle
Department18
Funding Project1806
Contribution Rank1;1
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine ; Technology
WOS SubjectBiochemistry & Molecular Biology ; Biophysics ; Computer Science, Interdisciplinary Applications
WOS Research AreaBiochemistry & Molecular Biology ; Biophysics ; Computer Science
WOS KeywordPARTIAL LEAST-SQUARES ; MULTIDRUG-RESISTANCE ; TRANSPORT ; TISSUES ; LOCALIZATION ; QUERCETIN ; QSAR
AbstractP-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.
Language英语
URL查看原文
WOS IDWOS:000231700500001
Citation statistics
Cited Times:50[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/92347
Collection中国科学院大连化学物理研究所
Corresponding AuthorYang L(杨凌); Yang L(杨凌)
Affiliation1.Chinese Acad Sci, Dalian Inst Chem Phys, Grad Sch, Lab Pharmaceut Resource Discovery, Dalian 116023, Peoples R China
2.Dalian Univ Technol, Sch Chem Engn, Dalian 116012, Peoples R China
Recommended Citation
GB/T 7714
Wang, YH,Li, Y,Yang, SL,et al. An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network[J]. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN,2005,19(3):137-147.
APA Wang, YH,Li, Y,Yang, SL,Yang, L,杨凌,&杨凌.(2005).An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network.JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN,19(3),137-147.
MLA Wang, YH,et al."An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network".JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 19.3(2005):137-147.
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