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题名: Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors
作者: Dong, Pei-pei1, 2;  Zhang, Yan-yan1, 2;  Ge, Guang-bo1, 2;  Ai, Chun-zhi1, 2;  Liu, Yong1;  Yang, Ling1;  Liu, Chang-xiao3
通讯作者: 杨凌
关键词: artificial neural network model ;  taxoids ;  multidrug resistance ;  resistance index ;  electrotopological state indices ;  principle component analysis ;  quantitative structure-activity relationship
刊名: ACTA PHARMACOLOGICA SINICA
发表日期: 2008-03-01
DOI: 10.1111/j.1745-7254.2008.00746.x
卷: 29, 期:3, 页:385-396
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 ;  Physical Sciences ;  Life Sciences & Biomedicine
类目[WOS]: Chemistry, Multidisciplinary ;  Pharmacology & Pharmacy
研究领域[WOS]: Chemistry ;  Pharmacology & Pharmacy
英文摘要: Aim: To develop an artificial neural network model for predicting the resistance index (RI) of taxoids. Methods: A dataset of 63 experimental data points were compiled from published studies and randomly subdivided into training and external test sets. Electrotopological state (E-state) indices were calculated to characterize molecular structure together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network technique was used to build the models. Five-fold cross-validation was performed and 5 models with different compound composition in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models. Results: The final model proved to be good with the cross-validation Q(cv)(2)0.62, external testing R-2 0.84, and the slope of the regression line through the origin for the testing set at 0.9933. Conclusion: The quantitative structure-activity relationship model can predict the RI to a relative nicety, which will aid in the development of new anti-multidrug resistance taxoids.
关键词[WOS]: MOLECULAR-FIELD ANALYSIS ;  PACLITAXEL ANALOGS ;  ANTICANCER AGENTS ;  NEURAL-NETWORKS ;  P-GLYCOPROTEIN ;  BETA-TUBULIN ;  QSAR ;  BINDING ;  CANCER ;  CYTOTOXICITY
语种: 英语
原文出处: 查看原文
WOS记录号: WOS:000253605500013
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/100021
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, Lab Pharmaceut Resource Discovery, Dalian 116023, Peoples R China
2.Chinese Acad Sci, Grad Sch, Beijing 100049, Peoples R China
3.Tianjin Inst Pharmaceut Res, Tianjin Key Lab Pharmacodynam & Pharmacokinet, Tianjin 300193, Peoples R China

Recommended Citation:
Dong, Pei-pei,Zhang, Yan-yan,Ge, Guang-bo,et al. Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors[J]. ACTA PHARMACOLOGICA SINICA,2008,29(3):385-396.
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