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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)
关键词Back-propagation Neural Network Bayesian-regularized Neural Network Flavonoid Log K-d Partial Least Squares Analysis P-glycoprotein Quantitative Structure-activity Relationship
刊名JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
2005-03-01
DOI10.1007/s10822-005-3321-5
19期:3页:137-147
收录类别SCI
文章类型Article
部门归属18
项目归属1806
产权排名1;1
WOS标题词Science & Technology ; Life Sciences & Biomedicine ; Technology
类目[WOS]Biochemistry & Molecular Biology ; Biophysics ; Computer Science, Interdisciplinary Applications
研究领域[WOS]Biochemistry & Molecular Biology ; Biophysics ; Computer Science
关键词[WOS]PARTIAL LEAST-SQUARES ; MULTIDRUG-RESISTANCE ; TRANSPORT ; TISSUES ; LOCALIZATION ; QUERCETIN ; QSAR
英文摘要P-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.
语种英语
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WOS记录号WOS:000231700500001
引用统计
被引频次:49[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://cas-ir.dicp.ac.cn/handle/321008/92347
专题中国科学院大连化学物理研究所
通讯作者Yang L(杨凌); Yang L(杨凌)
作者单位1.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
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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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