Application of Density Functional Theoretic Descriptors to Quantitative Structure-Activity Relationships with Temperature Constrained Cascade Correlation Network Models of Nitrobenzene Derivatives

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Application of Density Functional Theoretic Descriptors to Quantitative Structure-Activity Relationships with Temperature Constrained Cascade Correlation Network Models of Nitrobenzene Derivatives

机译：密度泛函理论描述子在硝基苯衍生物温度受限级联相关网络模型的定量构效关系中的应用

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摘要

A temperature-constrained cascade correlation network(TCCCN), a back-propagation neural network(BP), and multiple linear regression(MLR) models were applied to quantitative structure-activity relationship(QSAR) modeling, on the basis of a set of 35 nitrobenzene derivatives and their acute toxicities. These structural quantum-chemical descriptors were obtained from the density functional theory(DFT). Stepwise multiple regression analysis was performed and the model was obtained. The value of the calibration correlation coefficient R is 0.925, and the value of cross-validation correlation coefficient R is 0.87. The standard error S=0.308 and the cross-validated(leave-one-out) standard error S_ cv =0.381. Principal component analysis(PCA) was carried out for parameter selection. RMS errors for training set via TCCCN and BP are 0.067 and 0.095, respectively, and RMS errors for testing set via TCCCN and BP are 0.090 and 0.111, respectively. The results show that TCCCN performs better than BP and MLR.

机译：A temperature-constrained cascade correlation network(TCCCN), a back-propagation neural network(BP), and multiple linear regression(MLR) models were applied to quantitative structure-activity relationship(QSAR) modeling, on the basis of a set of 35 nitrobenzene derivatives and their acute toxicities. These structural quantum-chemical descriptors were obtained from the density functional theory(DFT). Stepwise multiple regression analysis was performed and the model was obtained. The value of the calibration correlation coefficient R is 0.925, and the value of cross-validation correlation coefficient R is 0.87. The standard error S=0.308 and the cross-validated(leave-one-out) standard error Scv=0.381. Principal component analysis(PCA) was carried out for parameter selection. RMS errors for training set via TCCCN and BP are 0.067 and 0.095, respectively, and RMS errors for testing set via TCCCN and BP are 0.090 and 0.111, respectively. The results show that TCCCN performs better than BP and MLR.

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《高等学校化学研究（英文版）》 |2006年第4期|439-442|共4页
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作者单位

Faculty of Chemistry Northeast Normal University Changchun 130024 P. R. China;

Department of Chemistry Capital Normal University Beijing 100037 P. R. China;

College of Urban and Environmental Science Northeast Normal University Changchun 130024 P. R. China;

Center for Intelligent Chemical Instrumentation Clippinger Laboratories Department of Chemistry and Biochemistry Ohio University Athens OH 45701-2979 USA;

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收录信息中国科学引文数据库(CSCD);
原文格式 PDF
正文语种 chi
中图分类化学;
关键词
DFT; MLR; PCA; BP; TCCCN; QSAR; Nitrobenzene derivative;

机译：DFT;MLR;PCA;BP;TCCCN;QSAR;硝基苯衍生物;

Application of Density Functional Theoretic Descriptors to Quantitative Structure-Activity Relationships with Temperature Constrained Cascade Correlation Network Models of Nitrobenzene Derivatives

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