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Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors
Dong, Pei-pei1,2; Zhang, Yan-yan1,2; Ge, Guang-bo1,2; Ai, Chun-zhi1,2; Liu, Yong1; Yang, Ling1; Liu, Chang-xiao3; Yang L(杨凌); Yang L(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
2008-03-01
DOI10.1111/j.1745-7254.2008.00746.x
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
关键词[WOS]MOLECULAR-FIELD ANALYSIS ; PACLITAXEL ANALOGS ; ANTICANCER AGENTS ; NEURAL-NETWORKS ; P-GLYCOPROTEIN ; BETA-TUBULIN ; QSAR ; BINDING ; CANCER ; CYTOTOXICITY
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.
语种英语
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WOS记录号WOS:000253605500013
引用统计
被引频次:3[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/321008/100021
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, 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
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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.
APA Dong, Pei-pei.,Zhang, Yan-yan.,Ge, Guang-bo.,Ai, Chun-zhi.,Liu, Yong.,...&杨凌.(2008).Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors.ACTA PHARMACOLOGICA SINICA,29(3),385-396.
MLA Dong, Pei-pei,et al."Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors".ACTA PHARMACOLOGICA SINICA 29.3(2008):385-396.
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