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题名: An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network
作者: Wang, YH;  Li, Y;  Yang, SL;  Yang, L
通讯作者: 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
发表日期: 2005-03-01
DOI: 10.1007/s10822-005-3321-5
卷: 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
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.
关键词[WOS]: PARTIAL LEAST-SQUARES ;  MULTIDRUG-RESISTANCE ;  TRANSPORT ;  TISSUES ;  LOCALIZATION ;  QUERCETIN ;  QSAR
语种: 英语
原文出处: 查看原文
WOS记录号: WOS:000231700500001
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/92347
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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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

Recommended Citation:
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.
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