An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network
文献类型：期刊论文
| 作者 | Wang, YH; Li, Y; Yang, SL; Yang, L |
| 刊名 | journal of computer-aided molecular design
 |
| 出版日期 | 2005-03-01 |
| 卷号 | 19期号:3页码:137-147 |
| 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 |
| 产权排序 | 1;1 |
| 通讯作者 | 杨凌 |
| 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. |
| 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 |
| 收录类别 | SCI |
| 原文出处 | true |
| 语种 | 英语 |
| WOS记录号 | WOS:000231700500001 |
| 公开日期 | 2010-11-30 |
| 源URL | [http://159.226.238.44/handle/321008/92347]  |
| 专题 | 大连化学物理研究所_中国科学院大连化学物理研究所 |
| 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 |
| 推荐引用方式 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.(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. |
入库方式： OAI收割
来源：大连化学物理研究所
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