支持向量机（SVM）和径向基神经网络（RBFNN）方法在化学、环境化学和药物化学中的应用研究
|
摘要
定量结构-性质/活性相关(QSPR/QSAR)研究是计算化学和化学信息学研究中的重要研究热点之一。它主要应用各种统计学方法和理论计算方法研究有机化合物的结构与其各种物理化学性质以及生物活性之间的定量关系。QSPR/QSAR的研究对象包括化合物的各种物理化学性质、生物活性、毒性、药物的各种代谢动力学参数等等,研究领域涉及化学、化工、环境化学、药物化学等诸多学科。
建立准确的定量数学模型一直是QSPR/QSAR研究的追求目标之一,而建模方法又是决定模型好坏的一个关键因素,因此新方法的发展一直是QSPR/QSAR研究中的一个重要任务。本论文在研究小组过去10余年来对神经网络方法(ANN),包括BP网络和RBFNN网络的研究基础上,将支持向量机(SVM)方法应用到化学、环境化学和药物化学等领域中,进行了1100多种化学物质的性质、环境毒物的毒性和药物有关的性质的预测,建立了准确的定量结构性质/活性关系模型。
论文第一章简述了定量结构性质/活性关系(QSPR/QSAR)的基本原理,研究过程以及研究现状,其中在研究过程中着重介绍了建模方法。在指出当前神经网络建模方法不足的基础上,详细介绍了一种新的机器学习算法—支持向量机方法,并概括和展望了其在QSPR/QSAR中的应用。
在第二章中,我们将SVM和RBFNN方法应用到化学领域中,主要包括以下几个方面的研究工作:
(1)应用多元线性回归(MLR)和SVM方法建立了预测364个有机化合物的范德华常数的QSPR模型。MLR不仅用来建立线性回归模型,同时也作为选择SVM输入描述符的方法。SVM模型的训练集、交互检验集、测试集和整个数据集的均方误差(Mean Square Error,MSE)分别为:常数a:5.96,8.00,6.67和6.65;常数b:9.56×10~(-5),3.18×10~(-4),4.22×10~(-4)和2.33×10~(-4)。
(2)应用启发式(HM)和SVM方法分别建立了149个易挥发有机化合物的气相色谱保留时间和5个分子描述符之间的线性和非线性QSRR模型。非线性的SVM模型的结果优于线性HM模型的结果,对于测试集均方误差MSE分别为1.094和1.644。而且预测值与实验值是非常一致的。
(3)用HM和RBFNN方法建立了预测63个有机小分子化合物在低密度聚乙烯上的渗透系数的定量模型。它建立的模型与以往的模型相比,有同样的可靠性。这
Quantitative structure-property/activity relationships (QSPR/QSAR) studies are important research topics in computational chemistry and chemoinformatics. They have been widely used for the prediction of various physicochemical properties and biological activities of organic compounds by using different statistical methods and various kinds of molecular descriptors.To build a rapid, simple and valid model is one of the important topics of QSPR/QSAR study. Since modeling method is one of the major factors, it is necessary to search for novel type of learning machine. On the basis of the research on artifical neural networks (ANN) by our group in recent 10 years, support vector machine (SVM) was introduced to chemistry, environmental chemistry and medicine chemistry and predicted the important properties of organic compounds, environmental pollutants and drugs in this dissertation. We showed the capability of Radial Basis Function Neural Networks (RBFNN) and SVM in QSPR and QSAR analysis and their potential utilities to solve problems in biology, chemistry and environment science through several applications in classification and correlation analysisA brief description of the QSPR/QSAR principle, research process and status was given in Chapter 1, and among them we gave an emphasis on the methods of model building. In this section, we also indicated the shortcoming of the present modeling method such as ANN and then introduced the new machine learning method—the support vector machine in detail. At last we gave a review and prospect of the application of SVM in QSPR/QSAR field.In Chapter 2, we applied SVM and RBFNN in chemistry. A brief description was given as follows:(1) Multiple linear regression (MLR) and SVM was used to develop QSPR models to predict the van der Waals' constants of a diverse set of 364 compounds.
MLR was utilized to not only select the molecular descriptors but also construct the linear model. The SVM models gave Mean Square Error (MSE) of 5.96 for the training set, 8.00 for the validation set, 6.67 for the test set and overall data sets are 6.65 to constant a. To constant b the value were 9.56x 10"5 for training set, 3.18 x 10 "4 for validation set, 4.22 x 10 ~4 for test set and 2.33 x 10 ~4 for the whole set.(2) The Heuristic Method (HM) and SVM was used to develop the linear and nonlinear QSRR models between the retention time (RT) and five molecular descriptors of 149 volatile organic compounds (VOCs). The mean squared eixors (MSE) in RT predictions for the test data set given by HM and SVM were 1.644 and 1.094, respectively, which showed the performance of SVM model was better than of the HM model. The prediction results are in agreement with the experimental values very well.(3) QSPR study was performed by HM and RBFNN to study the permeability coefficients of 63 various compounds through low-density polyethylene at 21.1 °C. Comparison of the models obtained by us and by others, it can be seen that their performance was comparative. It implied that this approach was suitable and alternative one in the field of polymer science.In Chapter 3, SVM and RBFNN were applied to environmental chemistry.(1) SVM, as a novel type of learning machine, was used to develop a classification model of carcinogenic property of 148 N-Nitroso compounds (NOCs). 7 descriptors calculated solely from the molecular structures of compounds by forward stepwise linear discriminant analysis (LDA) were used as inputs of the SVM model. The accuracy of training set for SVM was 97.4% and the test set was 86.6%. The total accuracy for SVM was 95.2%, which is higher than that of LDA (89.8%). It can be concluded that the steric and electric factor are likely two major factors in the process of carcinogenicity. And it gave a useful and convenient way for classification of the carcinogenicity of N-Nitroso compounds.(2) QSAR models for 93 polychlorinated dibenzofurans (PCDFs), dibenzodioxins (PCDDs), and biphenyls (PCBs) binding to the aryl hydrocarbon receptor (AhR) have been developed based on HM and SVM. Since various members
of the three classes of compounds have been shown to produce qualitatively similar toxicities, a combination of the different classes for each bioactivity were performed in one QSAR study. A subset of five molecular descriptors selected by HM in CODESSA was used as inputs for SVM. The results obtained by none linear SVM model were compared with those obtained by the linear heuristic method. The prediction result of the SVM model was better than that obtained by HM. The model of SVM led to a correlation coefficient (R) of 0.928 and root-mean-square error (RMS) of 0.324 for the test set and the values for HM model are 0.845 and 0.667 respectively. The work clearly demonstrated that single QSAR equation could be developed for the prediction of binding affinity of PCDFs, PCDDs, and PCBs.(3) Quantitative classification and regression models for prediction of sensory irritants (logRD5o) of 142 volatile organic chemicals (VOCs) have been developed. The best classification results were found using SVM: the accuracy for training, test and overall data set was 96.5%, 85.7% and 94.4%, respectively. The nonlinear regression models were built by RNFNN and SVM, respectively. The root mean squared errors (RMS) in prediction for the training, test and overall data set were 0.4755, 0.6322 and 0.5009 for reactive group;0.2430, 0.4798 and 0.3064 for nonreactive group by RBFNN. The comparative results obtained by SVM were 0.4415, 0.7430 and 0.5140 for reactive group;0.3920,0.4520 and 0.4050 for nonreactive group, respectively. This paper proposed an effective method for poisonous chemicals screening and considering.(4) Rat blood: air partition coefficient (logwood) for 100 volatile organic compounds (VOCs) was predicted by QSPR models. Simple molecular descriptors that calculated from the molecular structures alone were used to represent the characteristics of compounds. HM was used to pre-select the whole descriptor sets and to build the linear model. The model of HM led to a correlation coefficient square (R2) of 0.8832. This QSPR models provided a rapid, simple and valid way to predict the log^biood values of VOCs.In Chapter 4, we introduced SVM and RBFNN to medicine chemistry. Two research works were related:
(1) QSPR studuies were developed to predict pKa values of a set of 74 neutral and basic drugs by the linear and nonlinear methods based on the HM andRBFNN, respectively. The linear model obtained had a correlation coefficient (R) of 0.884 with an RMS error of 0.482 for the training set, while R was 0.693 and RMS was 0.987 for the test set. The RMS in prediction for overall data set was 0.619. The RBFNN model gave better results: for the training set R= 0.886, RMS= 0.458 and for the test set R= 0.737, RMS- 0.613. The RMS in prediction for overall data set was 0.493. And the model was useful to predict pKa during the discovery course of new drugs when the experimental data were unknown.(2) QSPR method was performed for the prediction of the standard Gibbs energies (AG9) of the transfer of 54 peptide anions from aqueous solution to nitrobenzene based on HM, RBFNN, and SVM. Comparison the results obtained by the three methods, it could be seen that the results of nonlinear model were better than of linear model. And of the nonlinear model, SVM was better than RBFNN. To SVM model, the RMS errors of the training set, the prediction set and the whole data were 1.604, 2.478 and 1.817, and the correlation coefficient were 0.968, 0.947 and 0.962, respectively.
建立准确的定量数学模型一直是QSPR/QSAR研究的追求目标之一,而建模方法又是决定模型好坏的一个关键因素,因此新方法的发展一直是QSPR/QSAR研究中的一个重要任务。本论文在研究小组过去10余年来对神经网络方法(ANN),包括BP网络和RBFNN网络的研究基础上,将支持向量机(SVM)方法应用到化学、环境化学和药物化学等领域中,进行了1100多种化学物质的性质、环境毒物的毒性和药物有关的性质的预测,建立了准确的定量结构性质/活性关系模型。
论文第一章简述了定量结构性质/活性关系(QSPR/QSAR)的基本原理,研究过程以及研究现状,其中在研究过程中着重介绍了建模方法。在指出当前神经网络建模方法不足的基础上,详细介绍了一种新的机器学习算法—支持向量机方法,并概括和展望了其在QSPR/QSAR中的应用。
在第二章中,我们将SVM和RBFNN方法应用到化学领域中,主要包括以下几个方面的研究工作:
(1)应用多元线性回归(MLR)和SVM方法建立了预测364个有机化合物的范德华常数的QSPR模型。MLR不仅用来建立线性回归模型,同时也作为选择SVM输入描述符的方法。SVM模型的训练集、交互检验集、测试集和整个数据集的均方误差(Mean Square Error,MSE)分别为:常数a:5.96,8.00,6.67和6.65;常数b:9.56×10~(-5),3.18×10~(-4),4.22×10~(-4)和2.33×10~(-4)。
(2)应用启发式(HM)和SVM方法分别建立了149个易挥发有机化合物的气相色谱保留时间和5个分子描述符之间的线性和非线性QSRR模型。非线性的SVM模型的结果优于线性HM模型的结果,对于测试集均方误差MSE分别为1.094和1.644。而且预测值与实验值是非常一致的。
(3)用HM和RBFNN方法建立了预测63个有机小分子化合物在低密度聚乙烯上的渗透系数的定量模型。它建立的模型与以往的模型相比,有同样的可靠性。这
Quantitative structure-property/activity relationships (QSPR/QSAR) studies are important research topics in computational chemistry and chemoinformatics. They have been widely used for the prediction of various physicochemical properties and biological activities of organic compounds by using different statistical methods and various kinds of molecular descriptors.To build a rapid, simple and valid model is one of the important topics of QSPR/QSAR study. Since modeling method is one of the major factors, it is necessary to search for novel type of learning machine. On the basis of the research on artifical neural networks (ANN) by our group in recent 10 years, support vector machine (SVM) was introduced to chemistry, environmental chemistry and medicine chemistry and predicted the important properties of organic compounds, environmental pollutants and drugs in this dissertation. We showed the capability of Radial Basis Function Neural Networks (RBFNN) and SVM in QSPR and QSAR analysis and their potential utilities to solve problems in biology, chemistry and environment science through several applications in classification and correlation analysisA brief description of the QSPR/QSAR principle, research process and status was given in Chapter 1, and among them we gave an emphasis on the methods of model building. In this section, we also indicated the shortcoming of the present modeling method such as ANN and then introduced the new machine learning method—the support vector machine in detail. At last we gave a review and prospect of the application of SVM in QSPR/QSAR field.In Chapter 2, we applied SVM and RBFNN in chemistry. A brief description was given as follows:(1) Multiple linear regression (MLR) and SVM was used to develop QSPR models to predict the van der Waals' constants of a diverse set of 364 compounds.
MLR was utilized to not only select the molecular descriptors but also construct the linear model. The SVM models gave Mean Square Error (MSE) of 5.96 for the training set, 8.00 for the validation set, 6.67 for the test set and overall data sets are 6.65 to constant a. To constant b the value were 9.56x 10"5 for training set, 3.18 x 10 "4 for validation set, 4.22 x 10 ~4 for test set and 2.33 x 10 ~4 for the whole set.(2) The Heuristic Method (HM) and SVM was used to develop the linear and nonlinear QSRR models between the retention time (RT) and five molecular descriptors of 149 volatile organic compounds (VOCs). The mean squared eixors (MSE) in RT predictions for the test data set given by HM and SVM were 1.644 and 1.094, respectively, which showed the performance of SVM model was better than of the HM model. The prediction results are in agreement with the experimental values very well.(3) QSPR study was performed by HM and RBFNN to study the permeability coefficients of 63 various compounds through low-density polyethylene at 21.1 °C. Comparison of the models obtained by us and by others, it can be seen that their performance was comparative. It implied that this approach was suitable and alternative one in the field of polymer science.In Chapter 3, SVM and RBFNN were applied to environmental chemistry.(1) SVM, as a novel type of learning machine, was used to develop a classification model of carcinogenic property of 148 N-Nitroso compounds (NOCs). 7 descriptors calculated solely from the molecular structures of compounds by forward stepwise linear discriminant analysis (LDA) were used as inputs of the SVM model. The accuracy of training set for SVM was 97.4% and the test set was 86.6%. The total accuracy for SVM was 95.2%, which is higher than that of LDA (89.8%). It can be concluded that the steric and electric factor are likely two major factors in the process of carcinogenicity. And it gave a useful and convenient way for classification of the carcinogenicity of N-Nitroso compounds.(2) QSAR models for 93 polychlorinated dibenzofurans (PCDFs), dibenzodioxins (PCDDs), and biphenyls (PCBs) binding to the aryl hydrocarbon receptor (AhR) have been developed based on HM and SVM. Since various members
of the three classes of compounds have been shown to produce qualitatively similar toxicities, a combination of the different classes for each bioactivity were performed in one QSAR study. A subset of five molecular descriptors selected by HM in CODESSA was used as inputs for SVM. The results obtained by none linear SVM model were compared with those obtained by the linear heuristic method. The prediction result of the SVM model was better than that obtained by HM. The model of SVM led to a correlation coefficient (R) of 0.928 and root-mean-square error (RMS) of 0.324 for the test set and the values for HM model are 0.845 and 0.667 respectively. The work clearly demonstrated that single QSAR equation could be developed for the prediction of binding affinity of PCDFs, PCDDs, and PCBs.(3) Quantitative classification and regression models for prediction of sensory irritants (logRD5o) of 142 volatile organic chemicals (VOCs) have been developed. The best classification results were found using SVM: the accuracy for training, test and overall data set was 96.5%, 85.7% and 94.4%, respectively. The nonlinear regression models were built by RNFNN and SVM, respectively. The root mean squared errors (RMS) in prediction for the training, test and overall data set were 0.4755, 0.6322 and 0.5009 for reactive group;0.2430, 0.4798 and 0.3064 for nonreactive group by RBFNN. The comparative results obtained by SVM were 0.4415, 0.7430 and 0.5140 for reactive group;0.3920,0.4520 and 0.4050 for nonreactive group, respectively. This paper proposed an effective method for poisonous chemicals screening and considering.(4) Rat blood: air partition coefficient (logwood) for 100 volatile organic compounds (VOCs) was predicted by QSPR models. Simple molecular descriptors that calculated from the molecular structures alone were used to represent the characteristics of compounds. HM was used to pre-select the whole descriptor sets and to build the linear model. The model of HM led to a correlation coefficient square (R2) of 0.8832. This QSPR models provided a rapid, simple and valid way to predict the log^biood values of VOCs.In Chapter 4, we introduced SVM and RBFNN to medicine chemistry. Two research works were related:
(1) QSPR studuies were developed to predict pKa values of a set of 74 neutral and basic drugs by the linear and nonlinear methods based on the HM andRBFNN, respectively. The linear model obtained had a correlation coefficient (R) of 0.884 with an RMS error of 0.482 for the training set, while R was 0.693 and RMS was 0.987 for the test set. The RMS in prediction for overall data set was 0.619. The RBFNN model gave better results: for the training set R= 0.886, RMS= 0.458 and for the test set R= 0.737, RMS- 0.613. The RMS in prediction for overall data set was 0.493. And the model was useful to predict pKa during the discovery course of new drugs when the experimental data were unknown.(2) QSPR method was performed for the prediction of the standard Gibbs energies (AG9) of the transfer of 54 peptide anions from aqueous solution to nitrobenzene based on HM, RBFNN, and SVM. Comparison the results obtained by the three methods, it could be seen that the results of nonlinear model were better than of linear model. And of the nonlinear model, SVM was better than RBFNN. To SVM model, the RMS errors of the training set, the prediction set and the whole data were 1.604, 2.478 and 1.817, and the correlation coefficient were 0.968, 0.947 and 0.962, respectively.
引文
[1] 王连生,韩朔睽.有机物定量结构活性相关.北京,中国环境科学出版社,1993.
[2] Murugan, R.;Grendze, M. E;Toomey, J. E.;Jr.;Katritzky, A. R.;Karelson, M.;Lobanov, V.;Rachwal, P. Predicting Physical Properties from Molecular Structure. Chemtech, 1994, 24, 17-23.
[3] Katritzky, A. R.;Lobanov, V. S.;Karelson, M. QSPR: The Correlation and Quantitative Prediction of Chemical and Physical Properties from Structure. Chem. Soc. Rev. 1995, 279-287.
[4] Karelson, M.;Maran, U.;Wang, Y.;Katritzky, A. R. QSPR and QSAR models derived using large descriptor spaces. A review of CODESSA applications. Collect. Czech. Chem. Commun. 1999, 64, 1551-1571.
[5] Katritzky, A. R.;Maran, U.;Lobanov, V. S.;Karelson, M. Structurally Diverse Quantitative Structure-Property Relationship Correlations of Technologically Relevant Physical Properties. J. Chem. Inf Comput. Sci. 2000, 40, 1-18.
[6] Katritzky, A. R.;Petrukhin, R.;Tatham, D.;Basak, S.;Benfenati, E.;Karelson, M.;Maran, U. Interpretation of quantitative structure-property and -activity relationships. J. Chem. Inf Comput. Sci. 2001, 41,679-685.
[7] Katritzky, A. R.;Fara, D. C.;Petntkhin, R. O.;Tatham, D. B.;Maran, U.;Lomaka, A.;Karelson, M. The present utility and future potential for medicinal chemistry of QSAR/QSPR with whole molecule descriptors. Current Topics in Medicinal Chemistry. 2002, 2, 1333-1356.
[8] 王连生,支正良.分子连接性与分子结构—活性.北京,中国环境科学出版社,1992.
[9] 许禄,胡昌玉《应用化学图论》北京,科学出版社,2000年.
[10] 王连生,韩朔睽《分子结构性质与活性》北京,化学工业出版社,1997年.
[11] Gasteiger,J.;Engle,T.著.梁逸曾,徐峻,姚建华,等译.《化学信息学教程》.北京,化学工业出版社,2005年.
[12] 周家驹,王亭《药物设计中的分子模型化方法》北京,科学出版社,2001年
[13] 陈凯先,蒋华良,嵇汝运《计算机辅助药物设计—原理、方法及应用》上海,上海科学技术出版社,2000年
[14] 俞庆森,朱龙观《分子设计导论》北京,高等教育出版社,2000年
[15] Kier, L. B.;Hall, L. H. Molecular Connectivity in Chemistry and Drug Research;Academic Press: New York, 1976.
[16] Kier, L. B.;Hall, L. H. Molecular Connectivity in Structure-Activity Analysis;John Wiley & Sons: New York, 1986.
[17]Balaban, A. T. From Chemical Topology to Three-Dimensional Geometry;Plenum Press: New York, 1996.
[18]Devillers, J., Balaban, A. T., Eds. Topological Indices and Related Descriptors in QSAR and QSPR;Gordon and Breach: Amsterdam, The Netherlands, 1999.
[19]Karelson, M. Molecular Descriptors in QSAR/QSPR;John Wiley & Sons: New York, 2000.
[20]Todeschini, R.;Consonni, V. Handbook of Molecular Descriptors;Wiley-VCH: Weinheim, Germany, 2000.
[21]Karelson, M.;Lobanov, V. S.;Katritzky, A. R. Quantum-Chemical Descriptors in QSAR/QSPR Studies. Chem. Rev. 1996, 96, 1027-1043.
[22]Balaban, A. T. From Chemical Topology to 3D Geometry. J. Chem. Inf. Comput. Sci. 1997, 37, 645-650.
[23]Balaban, A. T. Topological and stereochemical molecular descriptors for dastabases useful in QSAR, similarity/dissimilarity and drug design. SAR QSAR Environ. Res. 1998, 8, 1-21.
[24]Estrada, E., Uriarte, E. Recent advances on the role of topological indices in drug discovery research. Curt Med. Chem. 2001,8,1699-1714.
[25]Estrada, E.;Molina, E. 3D Connectivity Indices in QSPR/QSAR Studies. J. Chem. Inf. Comput. Sci. 2001, 41,791-797.
[26]Livingstone, D. J. The Characterization of Chemical Structures Using Molecular Properties. A Survey J. Chem. Inf. Comput. Sci.,2000, 40, 195-209.
[27]Gasteiger, J.;Sadowski, J.;Seizer, P.;Steinhauer, L.;Steinhauer, V. Chemical Information in 3D Space J. Chem. Inf. Comput. Sci.,1996,36, 1030-1037.
[28]Gasteiger, J.;Burkard,S.U., Hemmer, M.C.;Herwig, A.;von Homeyer, A., Hrllering, R.;Kleinrder, T.;Kostka,T.;Schwab, C.;Seizer, P.;Steinhauer, L. Decision Support Systems for Chemical Structure Representation, Reaction Modeling, and Spectra Simulation SAR QSAR in Environm.Res., 2002,13,89-110.
[29]Engel,T.;Gasteiger, J. Chemical Structure Representation for Information Exchange Online Information Review, 2002, 26,139-145.
[30]姚小军.博士毕业论文:人工神经网络和支撑向量机在化学中的应用.兰州大学,2005年.
[31]叶永德《计算机辅助药物设计导论》北京,化学工业出版社,2004.
[32]Ghose,A. K.;Crippen,G. M.Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. modeling dispersive and hydrophobic interactions. J. Chem. Inf. Comput. Sci. 1987, 27, 21-35.
[33]Ghose, A. K.;Pritchett, A.;Crippen, G. M. Atomic physicochemical parameters for 3-dimensional-structure-directed quantitative structure-activity relationships. 2. modeling dispersive and hydrophobic interactions. J. Comput. Chem. 1988, 9, 80-90.
[34]Convard, T.;Dubost, J.P.;Le Solleu H. Smilogp A program for fast evaluation of theoretical logP from smiles code of a molecule. Quant. Struct.-Act. Relat. 1994, 13, 34-37.
[35]Hou, T. J.;Xu, X. J. ADME evaluation in drug discovery. 2. prediction of partition coefficient by atom-additive approach based on atom-weighted accessible surface areas. J. Chem. Inf. Comput. Sci. 2003, 43, 1058-1067.
[36]Hansch, C.;Leo, A. Substituent constants for correlation analysis in chemistry and biology. New York, NY: Wiley, 1979.
[37]Leo, A. Comprehensive medicinal chemistry. Vol 4. Oxford: Pergamon, 1990.
[38]Klopman, G.;Li, J. Y.;Wang, S.;Dimayuga, M. Computer automated logP calculations based on an extended group-contribution approach. J. Chem. Inf. Comput. Sci. 1994, 34, 752-781.
[39]Meylan, W. M.;Howard, P. H. Atom fragment contribution method for estimating octanol-water partition-coefficients. J. Pharm. Sci. 1995, 84, 83-92.
[40]Suzuki, T.;Kudo, Y. Automatic lgP-estimation based on combined additive modeling methods. J. Comput.-Aided. Mol. Des. 1990, 4, 155-198.
[41]Hammett, L. P. Physical organic chemistry. New York: MaGraw-Hill Book Co., 1940.
[42] Branch, G. E. K.;Calvin, M. The Theory of Organic Chemistry. New York: Prentice Hall, 1941.
[43] Swain, C. G.;Lupton, E. C. Jr. Field and resonance components of substituent effects. J. Am. Chem. Soc. 1968, 90, 4328-4337.
[44] Charton, M. Nature of ortho effect, ⅱ .composition of the Taft steric parameters, J. Am. Chem. Soc. 1969, 91,615-618.
[45] Kutter, E.;Hansch, C. Steric parameters in drug design. Monoamine oxidase inhibitors and antistamines. J. Med. Chem. 1969, 12, 647-652.
[46] Hancock, C. K.;Meyers, E. A.;Yager, B. J. Quantitative separation of hyperconjugation effects from steric substituent constants. J. Am. Chem. Soc. 1961, 83, 4211-4213.
[47] Taft, R. W. Steric effects in organic chemistry. Newman M. S. ed. New York, N. Y: John Wiley & Sons, Inc., 1965, p556-675.
[48] Katritzky, A. R.;Lobanov, V. S.;Karelson, M. CODESSA: Reference Manual;Version 2;University of Florida, 1994.
[49] Connolly, M. L. Solvent-accessible surfaces of proteins and nucleic acids. Science. 1983, 221,709-713.
[50] Stanton, D. T.;Jurs, R C. Development and use of charge partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies. Anal. Chem. 1990, 62, 2323-2329.
[51] Livingstone, D. J. The Characterization of Chemical Structures Using Molecular Properties. A Survey J. Chem. Inf. Comput. Sci.,2000, 40, 195-209.
[52] Gasteiger, J.;Sadowski, J.;Selzer, P.;Steinhauer, L.;Steinhauer, V. Chemical Information in 3D Space J. Chem. Inf. Comput. Sci.,1996,36, 1030-1037.
[53] Gasteiger, J.;Burkard,S. U.;Hemmer, M. C.;Herwig, A.;von Homeyer, A.;Hollering, R.;Kleinoder, T.;Kostka,T.;Schwab,C.;Selzer, R;Steinhauer, L. Decision Support Systems for Chemical Structure Representation, Reaction Modeling, and Spectra Simulation SAR QSAR in Environm. Res., 2002,13,89-110.
[54] Engel,T.;Gasteiger, J. Chemical Structure Representation for Information Exchange Online Information Review, 2002,26, 139-145.
[55] Todeschini, R.;Gramatica, P. New 3D Molecular Descriptors: The WHIM theory and QSAR applications. In 3D QSAR in Drug Design (Kubinyi, H., Folkers, G. and Martin, Y.C., eds.), Kluwer/Escom, Dordrecht, The Netherlands, pp. 355-380.
[56] Bravi, G.;Gancia, E.;Mascagni, P.;Pegna, M.;Todeschini, R.;Zaliani, A. MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: a comparative 3D-QSAR study in a series of steroids. J. Computer-Aided Mol.Des. 1997,11, 79-92.
[57] Chiorboli, C.;Piazza, M.;Gramatica, P.;Pino, A.;Todeschini, R. 3D-modelling and prediction by WHIM descriptors. Part 7. Physico-chemical properties of haloaromatics: comparison between WHIM and topological descriptors. SAR QSAR Environ.Res. 1997, 7,133-150.
[58] Todeschini, R.;Gramatica, P. 3D-modelling and prediction by WHIM descriptors. Part 6. Applications of the WHIM descriptors in QSAR studies. Quant.Struct.-Act.Relat. 1997,16, 120-125.
[59] Todeschini, R., Moro, G., Boggia, R., Cosentino, U., Lasagni, M.;Pitea, D. Modeling and prediction of molecular properties. Theory of Grid-Weighted Holistic Invariant Molecular (G-WHIM) descriptors. Chemometrics & Intell.Lab.Syst. 1997,36, 65-73.
[60] Todeschini, R.;Gramatica, P. 3D-modelling and prediction by WHIM descriptors. Part 5. Theory development and chemical meaning of the WHIM descriptors. Quant. Struct.-Act.Relat. 1997,16, 113-119.
[61] Todeschini, R., Vighi, M., Finizio, A. & Gramatica, P. 3D-modelling and prediction by WHIM descriptors. Part 8. Toxicity and physico-chemical properties of environmental priority chemicals by 2D-TI and 3D-WHIM descriptors. SAR QSAR Environ.Res. 1997,7,173-193.
[62] Todeschini, R.;Gramatica, P.The WHIM Theory: New 3D Molecular Descriptors for QSAR in Environmental Modelling. SAR QSAR Environ.Res. 1997, 7,89-115.
[63] Todeschini, R.;Vighi, M.;Provenzani, R.;Finizio, A.;Gramatica, P. Modeling and prediction by using WHIM descriptors in QSAR studies: Toxicity of heterogeneous chemicals on Daphnia magna. Chemosphere 1996,32,1527-1545.
[64] Todeschini, R., Bettiol, C.;Giurin, G.;Gramatica, P.;Miana, P.;Argese, E. Modeling and prediction by using WHIM descriptors in QSAR studies: Submitochondrial Particles (SMP) as toxicity biosensors of chlorophenols. Chemosphere 1996,33,71-79.
[65] Todeschini, R.;Gramatica, P.;Marengo, E.;Provenzani, R. Weighted Holistic Invariant Molecular descriptors. Part 2. Theory development and applications on modeling physico-chemical properties of PolyAromatic Hydrocarbons (PAH). Chemometrics & Intell.Lab.Syst. 1995,27,221-229.
[66] Todeschini, R.;Lasagni, M.;Marengo, E. New Molecular Descriptors for 2D-and 3D-structures. Theory. J. Chemometrics 1994,8,263-273.
[67] http://www.tripos.com/
[68] Puri, S., Chickos, J. S.;Welsh, W. J. Three-Dimensional Quantitative Structure-Property Relationship (3D-QSPR) Models for Prediction of Thermodynamic Properties of Polychlorinated Biphenyls (PCBs): Enthalpy of Sublimation. J. Chem. Inf. Comput. Sci. 2002, 42, 109-116.
[69] Puri, S.;Chickos, J. S.;Welsh, W. J. Three-Dimensional Quantitative Structure-Property Relationship (3D-QSPR) Models for Prediction of Thermodynamic Properties of Polychlorinated Biphenyls (PCBs): Enthalpy of Vaporization. J. Chem. Inf. Comput. Sci. 2002, 42, 299-304.
[70] Hopfinger, A. J.;Burke, B. J.;Dunn, W. F., Ⅲ A Generalized Formalism of Three-dimensional Quantitative Structure-Property Relationship Analysis for Flexible Molecules Using Tensor Representation. J. Med. Chem. 1994, 37, 3768-3774.
[71] Hopfinger, A. J.;Wang, S., Tokarski, J. S.;Jin, B.;Albuquerque, M.;Madhav, P. J.;Duraiswami, C. Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism. J. Am. Chem. Soc. 1997, 119, 10509-10524.
[72] Hopfinger, A. J.;Wang, S., Tokarski, J. S.;Jin, B.;Albuquerque, M.;Madhav, P. J.;Duraiswami, C. Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism. J. Am. Chem. Soc. 1997, 119, 10509-10524.
[73] Duca, J. S.;Hopfinger, A. J. 4D-QSPR Analysis and Virtual Screening of Calcite Growth Inhibitor Libraries. Chem. Mater. 2000, 12, 3821-3829.
[74] Klein, C. D.;Hopfinger, A. J. Pharmacological activity and membrane interactions of antiarrhythmics: 4D-QSAR/QSPR analysis. Pharm. Res. 1998, 15, 303-311.
[75] Duca, J. S.;Tseng, Y.-E;Hopfinger, A. J. 4D-QSPR Analysis and Virtual Screening in Materials Science. Adv. Mater. 2001, 13, 1713-1717.
[76] http://www.mdli.com/.
[77] http://www.daylight.com/.
[78] 卢纹岱 SPSS for Windows,统计分析北京,电子工业出版社2002.
[79] 许禄《化学计量学方法》北京,科学出版社,1995年.
[80] Clark,D.E. (Ed.) Evolutionary Algorithms in Molecular Design Wiley-VCH, Weinheim (GER).
[81] Devillers, J. Genetic Algorithms in Molecular Modeling Academic Press, London 1996.
[82] 李华昌,谢淑兰,易忠胜 遗传算法的原理与应用。矿冶 2005,14,87-90。
[83] Holland J H. Adaptation in Natural and artificial Systems. Ann Arbor: The University of Michigan Press, 1975.
[84] Cong P.G.;Li T. H. Numeric genetic algorithm An al. Chem. Acta. 1994, 293, 191-203.
[85] Kovalishyn,V.V.;Tetko, I. V.;Luik,A. I.;Kholodovych,V.V.;Villa,A. E. P.;Livingstone, D. J. Neural Network studies. 3. Variable selection in the cascade-correlation learning architecture J. Chem. lnf Comput. Sci., 1998, 38, 651-659.
[86] Ferguson, A. P.;Heritage, T.;Jonathon, P.;Pack,S.E.;Phillips, L.;Rogan, J.;Snaith,P. J. EVA: A new theoretically based molecular descriptor for use in QSAR/QSPR analysis Journal of Computer Aided Molecular Design, 1997,11, 143-152.
[87] Livingstone,D. J.;Manallack, D.T.;Tetko,I. V. Data modelling with neural networks: advantages and limitations J. Computer-Aided Mol.Des. 1997,11,135-142.
[88] Tetko, I. V.;Villa, A. E. P.;Livingstone, D. J. Neural network studies. 2. Variable selection. J. Chem. Inf. Comput. Sci. 1996,36,794-803.
[89] Tetko, I. V.;Livinsgtone, D. J., Luik, A. I. Neural network studies. 1. Comparison of overfitting and overtraining J. Chem. Inf. Comput. Sci., 1995,35,826-833.
[90] Manallack, D. T.;Ellis, D. D.;Livingstone, D. J. Analysis of linear and non-linear QSAR Data for using neural networks J. Med. Chem.,1994,37,3758-67.
[91] Livingstone, D.J.;Manallack, D.T Statistics using neural networks: chance effects. J. Med. Chem,1993,36, 1295-1297.
[92] McCulloch, W. C.;Pitts W. A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophy 1943,5, 115-133.
[93] Hebb, D. O. The Organization of Behavior, Willey J., New York, 1949.
[94] Rosenblatt, F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 1958,65,386—407.
[95] Minsky, M.;Papert, S. Perceptrons: An Introducton to Computational Geometry, MIT Press, Cambridge, USA, 1969.
[96] Hopfield, J Neural networks and physical systems with emergent collective computational abilities. J. Proc. Nat. Acad. Sci., 1982, 79, 2554-2558.
[97] Hopfield, J. Neurons with graded response have collective computational properties like those of two-state neurons J. Proc. Nat. Acad. Sci., 1984, 81, 3088-3092.
[98] Rumelhart, D. E., Hinton, G. E.;Williams, R. J. in Microstructure of cognition, MIT Press, Cambridge, 1986, 11, 318-362.
[99] Lippmann, R. P. An introduction to computing with neural nets. IEEE ASSP Magazine.1987, 4, 4-22.
[100] Zupan, J.;Gasteiger, J. For a detailed overview of neural network applications in Chemistry up to 1990. Anal. Chim. Acta. 1991, 248, 1-30.
[101] Sumpter, B. G.;Gettino, C.;Noid, D. W. Theory and Applications of Neural Computing in Chemical Science Annu. Rev. Phys, Chem. 1994, 45, 439-481.
[102] Sumpter, B.G;Noid, D. W. On the design, analysis, and characterization of materials using computational neural networks. Annu. Rev. Mater. Sci. 1996, 26, 223-277.
[103] 韩相明 利用神经网络预测阴离子共偶碱金属卤化物二元系的相图特征,河南科学,2000,18,374—377.
[104] 李一波,黄小原 径向基函神经网络用于重叠色谱峰解析,色谱,2001,19,112-115.
[105] 李一波,黄小原.基于改进排序遗传算法的径向基函数神经网络色谱峰解析,分析化学,2001,29,253-257.
[106] Livingstone, D. Data analysis for chemists: application to QSAR and chemiscal product design. OUP, Oxford, 1995.
[107] Devillers, J. Neural networks in QSAR and drug design. Academic Press, London,1996.
[108] Burden, F. R.;Ford, M. G.;Whitley, D. C.;Winkler, D. A. The use of automatic relevance determination in QSAR studies using Bayesian neural networks. J. Chem. Inf Comput. Sci. 2000, 40, 1423-1430.
[109] Lucic, B.;Trinajstic, N. Multivariate regression outperforms several robust architectures of neural networks in QSAR modeling. J. Chem. Inf Comput. Sci. 1999, 39, 121-132.
[110] Zupan,J.;Gasteiger,J.著.潘忠孝,陈玲然.译.张懋森.校.神经网络及其在化学中的应用.合肥,中国科学技术大学出版社,2000.
[111] 吕庆章,王晓钰.计算机与应用化学,1998,2,100—104.
[112] 陈锋,周英.用改进的遗传算法训练神经网络以研究有机磷杀虫剂的构效关系.计算机与应用化学.1998.6,357-360.
[113] 高守国,相秉仁,安定奎.计算机与应用化学,1998,5,381-382.
[114] 吴新生,谢益民,刘焕彬.主成分分析法用于化工过程人工神经网络建模. 计算机与应用化学,1999,3,219—222.
[115] Walczak, B.;Massart, D. L. Local modelling with radial basis function networks, Chemometrics & Intell.Lab.Syst. 2000,50,179-198.
[116] Walczak, B.;Massart, D. L.The Radial Basis Functions - Partial Least Squares approach as a flexible non-linear regression technique, Anal. Chim. Acta., 1996, 331,177-185.
[117] Walczak, B.;Massart, D. L. Application of Radial Basis Functions-Partial Least Squares to non-linear pattern recognition problems: diagnosis of process faults, Anal. Chim. Acta. 1996, 331,187-193.
[118] Tetteh, J.;Howells, S.;Metcalfe, E.;Suzuki T. Optimisation of radial basis function neural networks using biharmonic spline interpolation, Chemometrics & Intell.Lab.Syst, 1998,41,17-29.
[119] Orr, M.J.L. Introduction to Radial basis function networks, centre for cognitive science, Edinburgh University, 1996.
[120] Orr, M.J.L. MATLAB routines for subset selection and ridge regression in linear neural networks, Centre for cognitive science, Edinburgh University, 1996.
[121] Pulido, A.;Ruisnchez. I.;Rius, F. X. Radial basis functions applied to the classification of UV-visible spectra, Anal. Chim. Acta., 1999,388, 273-281.
[122] Stubbings, T.;Hutter, H. Classification of analytical images with radial basis function networks and forward selection, Chemometrics & Intell.Lab.Syst, 1999,49, 163-172.
[123] Loukas, Y. L. Radial basis function networks in host-guest interactions: instant and accurate formation constant calculations, Anal. Chim. Acta.,2000, 417,221-229.
[124] Evans, P.;Persaud, K. C.;McNeish, A. S.;Sneath, R.;W.;Hobson, N.;Magan, N. Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data, Sensors and Actuators B: Chemical, 2000,69,348-358.
[125] Neural Network FAQ Part3 Generalization.
[126] Sarle, W. S. Proceedings of the 27th Symposium on the interface of Computing Science and Statistics, 1995,352-360.
[127]Geman, S.;Bienenstock, E.;Doursat, R. Neural network and bias/variance dilemma. Neural Computation, 1992,4, 1-58.
[128]Nelson, M.C.;Ⅱ lingworth, W.T. A Practical Guide to Neural Nets, Reading, MA: Addison-Wesley. 1991.
[129]Finnoff, W.;Hergert ,F.;Zimmermann, H.G. An unsupervised learning technique for artificial neural networks Neural Networks, 1993, 6,707-711.
[130]Lang K. J.;Waibel A.;Hinton, G. E. A time-delay neural network architecture for isolated word recognition. Neural Networks, 1990, 3, 23-43.
[131]Morgan, N.;Bourlard, H.;Touretzky, D.S.;Mateo, S.;Morgan Kaufman, Advances in Neural Information Processing Systems 2,ed. California, 1989,p. 107.
[132]袁曾任编著.《人工神经元及其应用》清华大学出版社,1999,1-27.
[133]Vapnik, N. Statistical Learning Theory. John Wiley & Sons, New York. 1998.
[134]Schlkopf, B.;Smola, A. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
[135]叶晨洲,杨杰,姚莉秀,陈念贻 统计学习理论的原理与应用,计算机与应用化学 2002,19,712—716.
[136]陈念贻,陆文聪,陆治荣.优化建模技术和机器学习理论的新发展,计算机与应用化学.2002,19,677—682.
[137]Cristianini, N.;Shawe-Taylor ,J.. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK, 2000.
[138]Thorsten J. Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms. Kluwer, 2002
[139]陈念贻,陆文聪,叶晨洲,李国正.支持向量机及其他核函数算法在化学计量学的应用,计算机与应用化学.2002,19,691—696.
[140]陈念贻,陆文聪 支持向量机算法在化学和化工中的应用,计算机与应用化学.2002,19,673—676.
[141]陆文聪,陈念贻,叶晨洲,李国正 支持向量机算法和ChemSVM介绍,计算 机与应用化学,2002,19,697—702.
[142]Burges, C. J. C. A Tutorial on Support Vector Machines for Pattern Recognition Data Min. Knowl. Disc., 1998,2,121-167.
[143]Belousov, A. I.;Verzakov, S. A.;von Frese, J. Applicational aspects of support vector machines. J. Chemomet. 2002,16,482-489.
[144]Belousov, A. I.;Verzakov. S. A.;von Frese, J. A flexible classification approach with optimal generalisation performance:support vector machines, Chemometrics & Intell.Lab.Syst, 2002,64, 15-25.
[145]Gunn, S.R.;Brown, M.;Bossley, K.M. Network performance assessment for neurofuzzy data modeling. Lect. Notes Comput. Sci. 1997, 1280, 313-323.
[146]Vapnik, V. The nature of statistical learning theory. New York, Springer-Verlag, 1995.
[147]Cristianini, N.;Shawe-Taylor, J. Introduction to support vector machine and other kernel based learning methods. Cambridge: Cambridge University Press, 2000.
[148]Vapnik, V.;Chervonenkis, A. Y. The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognit. Image Anal. 1991, 1,283-305.
[149]Vapnik, V. Estimation of Dependences Based on Empirical Data;Springer: Berlin, 1982.
[150]Smola, A. J.;Scholkopf, B. A tutorial on support Vector regression;NeuroCOL2 Technical report series, NC2-TR-1998-030;October, 1998.
[151]Burges, C. J. C. A tutorial of support vector machines for pattern recognition. http://svm.research.bell-labs.com/SVMdoc.html,1998.
[152]Vapnik, V.;Golowich, S.;Smola, A. Support Vector Method for function approximation, regression estimation, and signal processing. Adv. Neural Inform. Process. Systems 1997, 9, 281-287.
[153]陶卿,姚穗,范劲松,方廷健.一种新的机器学习算法:support vector machines.模式识别与人工智能.2000,13,285-290.
[154]薛春霞.博士毕业论文:SVM在QSPR中的应用及基于配体的计算机辅助药物设计,兰州大学,2005年
[155]Cai, Y. D.;Liu, X. J.;Xu, X. B.;Chou, K. C. Support vector machines for predicting HIV protease cleavage sites in protein, J. Comput. Chem. 2002, 23, 267-274.
[156]Bao, L;Sun, Z. Identifying genes related to drug anticancer mechanisms using support vector machine. FEBS Letters 2002, 521, 109-114.
[157]Furey, T.;Cristianini, N.;Duffy, N.;Bednarski, D.;Schummer, Michel.;Haussler, D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16, 906-914.
[158]Brown, M.;Grundy, W.;Lin, D.;Cristianini, N.;Sugnet, C.;Furey, T.;Ares Jr., M.;Haussler, D. Knowledge-based analysis of microarray gene expression data using support vector machines. Proc. Natl. Acad. Sci. U. S. A. 2000, 97, 262-267.
[159]Guyon, I.;Weston, J.;Barnhill, S.;Vapnik, V. Gene selection for cancer classification using support vector machines. Math. Learn. 2002, 46, 389-422.
[160]Ramaswamy, S.;Tamayo, E;Rifkin, R.;Mukherjee, S.;Yeang, C. H.;Angelo, M.;Ladd, C.;Reich, M.;Latulippe, E.;Mesirov, J. E;Poggio, T.;Gerald, W.;Loda, M.;Lander, E. S.;Golub, T. R. Multiclass cancer diagnosis using tumor gene expression signatures. Pro~. Natl. Acad. Sci. U.S.A. 2001, 98, 15149-15154.
[161]Cai, Y. D.;Liu, X. J.;Xu, X. B.;Chou, K. C. Prediction of protein structural classes by support vector machines. Comput. Chem. 2002, 26, 293-296.
[162]Cai, Y. D.;Liu, X. J.;Xu, X. B.;Chou, K. C. Support vector machines for the classification and prediction of beta-turn types. J. Pept. Sci. 2002, 8, 297-301.
[163]Hua, S.;Sun, Z. Novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J..Mol. Biol.2001, 308, 397-407.
[164]Ding, C. H. Q.;Dubchak, I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 2001, 17, 349-358.
[165]Cai, Y. D.;Liu, X. J.;Xu, X. B.;Chou, K. C. Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect. J. Cell. Biochem. 2002, 84, 343-348.
[166] Hua, S.;Sun, Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics 2001, 17, 721-728.
[167] Bock, J. R.;Gough, D. A. Predicting protein-protein interactions from primary structure. Bioinformatics 2001, 17, 455-460.
[168] Karchin, R.;Karplus, K.;Haussler, D. Classifying G-protein coupled receptors with support vector machines. Bioinformatics 2002, 18, 147-159.
[169] Lee, D.;Choi, S. W.;Kim, M.;Park, J. H.;Kim, M.;Kim, J.;Lee, I. B. Discovery of differentially expressed genes related to histological subtype of hepatocellular carcinoma. Biotechnol. Prog. 2003, 19, 1011-1015.
[170] Xue, Y.;Yap, C. W.;Sun, L. Z.;Cao, Z. W.;Wang, J. F.;Chen, Y. Z. Prediction of P-glycoprotein substrates by a support vector machine approach. J. Chem. Inf Comput. Sci. 2004, 44, 1497-1505.
[171] Anderson, D. C.;Li, W.;Payan, D. G.;Noble, W. S. A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores. J. Proteom. Res. 2003, 2, 137-146.
[172] Zhao, C. Y.;Zhang, R. S.;Liu, H. X.;Xue, C. X.;Zhao, S. G.;Zhou, X. F.;Liu, M. C.;Fan, B. T. Diagnosing anorexia based on partial least squares, back propagation neural network, and support vector machines. J. Chem. Inf Comput. Sci. 2004, 44, 2040-2046.
[173] Liu, H. X.;Zhang, R. S.;Luan, F.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Diagnosing breast cancer based on support vector machines. J. Chem. Inf. Comput. Sci. 2003, 43, 900-907.
[174] 刘焕香.博士毕业论文:基于支持向量机方法的QSAR/QSPR在化学、生物及环境科学中的应用研究.兰州大学,2005年.
[175] Song, M.;Breneman, C. M.;Bi, J.;Sukumar, N.;Bennett, K. P.;Cramer, S.;Tugcu, N. Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J. Chem. Inf. Comput. Sci. 2002, 42, 1347-1357.
[176] Lind, P.;Maltseva, T. Support vector machines for the estimation of aqueous solubility. J. Chem. Inf. Comput. Sci. 2003, 43, 1855-1859.
[177] Xue, C. X.;Zhang, R. S.;Liu, H. X.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines. J. Chem. Inf. Comput. Sci. 2004, 44, 669-677.
[178] Xue, C. X.;Zhang, R. S., Liu, M. C.;Hu, Z. D., Fan, B. T. Study of the quantitative structure-mobility relationship of carboxylic acids in capillary electrophoresis based on support vector machines J. Chem. Inf Comput. Sci. 2004, 44, 950-957.
[179] Xue, C. X.;Zhang, R. S.;Liu, H. X.;Liu, M. C., Hu, Z. D.;Fan, B. T. Support vector machines-based quantitative structure-property relationship for the prediction of heat capacity. J. Chem. Inf Comput. Sci. 2004, 44, 1267-1274.
[180] Liu, H. X.;Zhang, R. S.;Yao, X. J., Liu, M. C.;Hu, Z. D., Fan, B. T. Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs. J. Chem. Inf Comput. Sci. 2004, 44, 161-167.
[181] Liu, H. X.;Xue, C. X.;Zhang, R. S.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine. J. Chem. Inf. Comput. Sci. 2004, 44, 1979-1986.
[182] Liu, H. X., Zhang, R. S.;Yao, X. J.;Liu, M. C., Hu, Z.D., Fan, B.T. Prediction of electrophoretic mobility of substituted aromatic acids in different aqueous-alcoholic solvents by capillary zone electrophoresis based on support vector machine. Anal. Chim. Acta 2004, 525, 31-41.
[183] Luan, F., Zhang, R. S., Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Support vector machine-based QSPR for the prediction of Van der Waals'constants. QSAR Comb. Sci. 2005, 24, 227-239..
[184] Luan, F., Xue, C X.;Zhang, R. S., Zhao, C. Y.;Liu, M. C., Hu, Z. D., Fan, B. T. Prediction of retention time of a variety of volatile organic compounds based on the heuristic method and support vector machine. Anal. Chim. Acta 2005, 537, 101-110.
[185] Ma, W. P., Zhang, X. Y.;Luan, F.;Zhang, H. X. Zhang, R. S., Liu, M. C.;Hu, Z. D.;Fan. B.T. Support Vector Machine and the Heuristic Method to Predict the Solubility of Hydrocarbons in Electrolyte, J. Phys. chem. A, 2005, 109, 3485-3492.
[186] Ma, W. P.;Luan, E;Zhang, H. X., Zhang, X. Y.;Liu, M. C.;Hu, Z. D.;Fan. B.T. Quantitative Structure-Property Relationships for Pesticides in Biopartitioning Micellar Chromatography. J. chromatogr. A 2006 ,In Press.
[187] Luan, F.;Zhang, X. Y.;Zhang, H. X.;Zhang, R. S.;Liu, M. C.;Hu, Z. D.;Fan. B.T. Prediction of Standard Gibbs Energies of the Transfer of Peptide Anions from Aqueous Solution to Nitrobenzene Based on Support Vector Machine and the Heuristic Method. J. Computer-Aided Mol.Des.. 2006 In press.
[188] Luan, F.;Zhang, X. Y., Zhang, H. X.;Zhang, R. S.;Liu, M. C.;Hu, Z. D.;Fan. B. T.QSPR Study of Permeability Coefficients Through Low-density Polyethylene Based on Radial Basis Function Neural Networks and the Heuristic method, Comp. Mater. Sci. 2006, In Press.
[189] Byvatov, E.;Fechner, U.;Sadowski, J., Schneider, Gisbert. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification, or. Chem. Inf. Comput. Sci. 2003, 43, 1882-1889.
[190] Liu, Y. A comparative study on feature selection methods for drug discovery. J. Chem. Inf. Comput. Sci. 2004, 44, 1823-1828.
[191] Xue, Y., Li, Z. R.;Yap, C. W.;Sun, L. Z.;Chen, X., Chen, Y. Z. Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. J. Chem. Inf. Comput. Sci. 2004, 44, 1630-1638.
[192] Sadik, O.;Land, Jr., W. H.;Wanekaya, A. K.;Uematsu, M., Embrechts, M. J.;Wong, L., Leibensperger, D., Volykin, A. Detection and classification of organophosphate nerve agent simulants using support vector machines with multiarray sensors. J. Chem. Inf Comput. Sci. 2004, 44, 499-507.
[193] Sorich, M. J.;Miners, J. O., McKinnon, R. A.;Winkler, D. A., Burden, F. R.;Smith, P. A. Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP-Glucuronosyltransferase isoforms. J. Chem. Inf. Comput. Sci. 2003, 43, 2019-2024.
[194] Czerminske, R.;Yasri, A.;Hartsough, D. Use of support vector machines in pattern classification: application to QSAR studies. Quant. Struct.-Act. Relat. 2001, 20, 227-240.
[195] Zernov, V. V.;Balakin, K. V., Ivaschenko, A. A.;Savchuk, N. P.;Pletnev, I. V. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J. Chem. Inf. Comput. Sci. 2003, 43, 2048-2056.
[196] Zhao, C. Y., Zhang, H.X.;Zhang, X. Y.;Zhang, R.S.;Luan, F.;Liu, M.C.;Hu, Z.D.;Fan. B.T. Prediction of Milk-to-Plasma Drug Concentration (M/P) Ratio Using Support Vector Machine (SVM) Method. Pharm Res. 2006, 23, 41-48.
[197] Burbidge, R.;Trotter, M., Buxton, B., Holden, S. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 2001, 26, 5-14.
[198] Norinder, U. Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimizations and variable selection. Neurocomputing 2003, 55, 337-346.
[199] Kovatcheva, A., Golbraikh, A.;Oloff, S.;Xiao, Y.-D., Zheng, W., Wolschann, P.;Buchbauer., G.;Tropsha, A. Combinatorial QSAR of ambergris fragrance compounds. J. Chem. Inf. Comput. Sci. 2004, 44, 582-595.
[200] Xue, C. X.;Zhang, R. S.;Liu, H. X.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine. J. Chem. Inf. Comput. Sci. 2004, 44, 1693-1700.
[201] Yao, X. J.;Panaye, A.;Doucet, J. P.;Zhang, R. S.;Chen, H. F.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. J. Chem. Inf. Comput. Sci. 2004, 44, 1257-1266.
[202]Liu, H. X.;Zhang, R. S.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: an inhibitor of AP-1 and NF-κB mediated gene expression based on support vector machines. J. Chem. Inf. Comput. Sci. 2003, 43, 1288-1296.
[203]Kramer, S.;Frank, E.;Helma, C. Fragment generation and support vector machines for inducing SARs. SAR QSAR Environ.Res. 2002, 13, 509-523.
[204]Ivanciuc, O. Structure-odor relationships for pyrazines with support vector machines. Internet Electronic Journal of Molecular Design. 2002, 1,269-284.
[205]Ivanciuc, O. Support vector machine classification of the carcinogenic activity of polycyclic aromatic hydrocarbons. Internet Electronic Journal of Molecular Design. 2002, 1,203-218.
[206]陆文聪,王琨,陈念贻.支持向量机算法用于拮抗药化合物活性的模式识别.计算机与应用化学 2002,19,741—744.
[207]赵慧,陆文聪,张良苗,陈念贻.支持向量机用于胍类化合物Na/H交换抑制活性的模式识别.计算机与应用化学 2002,19,745—748.
[208]Ivanciuc, O. Support vector machine identification of the aquatic toxicity mechanism of organic compounds. Internet Electronic Journal of Molecular Design. 2002, 1, 151-172.
[209]Luan, F.;Zhang, R. S;Zhao, C. Y.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis. Chem. Res. Toxicol. 2005, 18, 198-203.
[210]Luan, F.;Ma, W. P.;Zhang, H. X.;Zhang, X. Y.;Liu, M. C.;Hu, Z. D.;Fan. B.T. QSAR Study of Polychlorinated Dibenzodioxins, Dibenzofurans, and Biphenyls using the Heuristic Method and Support Vector Machine. QSAR Comb. Sci.2006, 25, 46-55.
[211] Luan, F.;Ma, W. P.;Zhang, H. X.;Zhang, X. Y.;Liu, M. C.;Hu, Z. D.;Fan. B.T. Quantitative Structure-Activity Relationship Models for Prediction of Sensory Irritants (logRD_(50)) of Volatile Organic Chemicals. Chemosphere, 2006 In press.
[212] Thissen, U.;Ostün, B.;Melssen, W. J.;Buydens, L. M. C. Multivariate calibration with least-squares support vector machines. Anal. Chem. 2004, 76, 3099-3105.
[213] Byvatov, E.;Schneider, G. SVM-based feature selection for characterization of focused compound collections. J. Chem. Inf Comput. Sci. 2004, 44, 993-999.
[214] 陈念贻,陆文聪,叶晨洲,李国正.支持向量机及其他核函数算法在化学计量学中的应用.计算机与应用化学,2002,19,691-696.
[215] 王冰,栾锋,刘满仓,胡之德,基于线形判别式和支撑向量机的肾结石分类方法,兰州大学学报,2006,In press.
[216] Zhai, H. L.;Gao, H.;Chen, X. G.;Hu, Z. D. An assisted approach of the global optimization for the experimental conditions in capillary electrophoresis. Anal. Chim. Acta, 2005,546,112-118.
[2] Murugan, R.;Grendze, M. E;Toomey, J. E.;Jr.;Katritzky, A. R.;Karelson, M.;Lobanov, V.;Rachwal, P. Predicting Physical Properties from Molecular Structure. Chemtech, 1994, 24, 17-23.
[3] Katritzky, A. R.;Lobanov, V. S.;Karelson, M. QSPR: The Correlation and Quantitative Prediction of Chemical and Physical Properties from Structure. Chem. Soc. Rev. 1995, 279-287.
[4] Karelson, M.;Maran, U.;Wang, Y.;Katritzky, A. R. QSPR and QSAR models derived using large descriptor spaces. A review of CODESSA applications. Collect. Czech. Chem. Commun. 1999, 64, 1551-1571.
[5] Katritzky, A. R.;Maran, U.;Lobanov, V. S.;Karelson, M. Structurally Diverse Quantitative Structure-Property Relationship Correlations of Technologically Relevant Physical Properties. J. Chem. Inf Comput. Sci. 2000, 40, 1-18.
[6] Katritzky, A. R.;Petrukhin, R.;Tatham, D.;Basak, S.;Benfenati, E.;Karelson, M.;Maran, U. Interpretation of quantitative structure-property and -activity relationships. J. Chem. Inf Comput. Sci. 2001, 41,679-685.
[7] Katritzky, A. R.;Fara, D. C.;Petntkhin, R. O.;Tatham, D. B.;Maran, U.;Lomaka, A.;Karelson, M. The present utility and future potential for medicinal chemistry of QSAR/QSPR with whole molecule descriptors. Current Topics in Medicinal Chemistry. 2002, 2, 1333-1356.
[8] 王连生,支正良.分子连接性与分子结构—活性.北京,中国环境科学出版社,1992.
[9] 许禄,胡昌玉《应用化学图论》北京,科学出版社,2000年.
[10] 王连生,韩朔睽《分子结构性质与活性》北京,化学工业出版社,1997年.
[11] Gasteiger,J.;Engle,T.著.梁逸曾,徐峻,姚建华,等译.《化学信息学教程》.北京,化学工业出版社,2005年.
[12] 周家驹,王亭《药物设计中的分子模型化方法》北京,科学出版社,2001年
[13] 陈凯先,蒋华良,嵇汝运《计算机辅助药物设计—原理、方法及应用》上海,上海科学技术出版社,2000年
[14] 俞庆森,朱龙观《分子设计导论》北京,高等教育出版社,2000年
[15] Kier, L. B.;Hall, L. H. Molecular Connectivity in Chemistry and Drug Research;Academic Press: New York, 1976.
[16] Kier, L. B.;Hall, L. H. Molecular Connectivity in Structure-Activity Analysis;John Wiley & Sons: New York, 1986.
[17]Balaban, A. T. From Chemical Topology to Three-Dimensional Geometry;Plenum Press: New York, 1996.
[18]Devillers, J., Balaban, A. T., Eds. Topological Indices and Related Descriptors in QSAR and QSPR;Gordon and Breach: Amsterdam, The Netherlands, 1999.
[19]Karelson, M. Molecular Descriptors in QSAR/QSPR;John Wiley & Sons: New York, 2000.
[20]Todeschini, R.;Consonni, V. Handbook of Molecular Descriptors;Wiley-VCH: Weinheim, Germany, 2000.
[21]Karelson, M.;Lobanov, V. S.;Katritzky, A. R. Quantum-Chemical Descriptors in QSAR/QSPR Studies. Chem. Rev. 1996, 96, 1027-1043.
[22]Balaban, A. T. From Chemical Topology to 3D Geometry. J. Chem. Inf. Comput. Sci. 1997, 37, 645-650.
[23]Balaban, A. T. Topological and stereochemical molecular descriptors for dastabases useful in QSAR, similarity/dissimilarity and drug design. SAR QSAR Environ. Res. 1998, 8, 1-21.
[24]Estrada, E., Uriarte, E. Recent advances on the role of topological indices in drug discovery research. Curt Med. Chem. 2001,8,1699-1714.
[25]Estrada, E.;Molina, E. 3D Connectivity Indices in QSPR/QSAR Studies. J. Chem. Inf. Comput. Sci. 2001, 41,791-797.
[26]Livingstone, D. J. The Characterization of Chemical Structures Using Molecular Properties. A Survey J. Chem. Inf. Comput. Sci.,2000, 40, 195-209.
[27]Gasteiger, J.;Sadowski, J.;Seizer, P.;Steinhauer, L.;Steinhauer, V. Chemical Information in 3D Space J. Chem. Inf. Comput. Sci.,1996,36, 1030-1037.
[28]Gasteiger, J.;Burkard,S.U., Hemmer, M.C.;Herwig, A.;von Homeyer, A., Hrllering, R.;Kleinrder, T.;Kostka,T.;Schwab, C.;Seizer, P.;Steinhauer, L. Decision Support Systems for Chemical Structure Representation, Reaction Modeling, and Spectra Simulation SAR QSAR in Environm.Res., 2002,13,89-110.
[29]Engel,T.;Gasteiger, J. Chemical Structure Representation for Information Exchange Online Information Review, 2002, 26,139-145.
[30]姚小军.博士毕业论文:人工神经网络和支撑向量机在化学中的应用.兰州大学,2005年.
[31]叶永德《计算机辅助药物设计导论》北京,化学工业出版社,2004.
[32]Ghose,A. K.;Crippen,G. M.Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. modeling dispersive and hydrophobic interactions. J. Chem. Inf. Comput. Sci. 1987, 27, 21-35.
[33]Ghose, A. K.;Pritchett, A.;Crippen, G. M. Atomic physicochemical parameters for 3-dimensional-structure-directed quantitative structure-activity relationships. 2. modeling dispersive and hydrophobic interactions. J. Comput. Chem. 1988, 9, 80-90.
[34]Convard, T.;Dubost, J.P.;Le Solleu H. Smilogp A program for fast evaluation of theoretical logP from smiles code of a molecule. Quant. Struct.-Act. Relat. 1994, 13, 34-37.
[35]Hou, T. J.;Xu, X. J. ADME evaluation in drug discovery. 2. prediction of partition coefficient by atom-additive approach based on atom-weighted accessible surface areas. J. Chem. Inf. Comput. Sci. 2003, 43, 1058-1067.
[36]Hansch, C.;Leo, A. Substituent constants for correlation analysis in chemistry and biology. New York, NY: Wiley, 1979.
[37]Leo, A. Comprehensive medicinal chemistry. Vol 4. Oxford: Pergamon, 1990.
[38]Klopman, G.;Li, J. Y.;Wang, S.;Dimayuga, M. Computer automated logP calculations based on an extended group-contribution approach. J. Chem. Inf. Comput. Sci. 1994, 34, 752-781.
[39]Meylan, W. M.;Howard, P. H. Atom fragment contribution method for estimating octanol-water partition-coefficients. J. Pharm. Sci. 1995, 84, 83-92.
[40]Suzuki, T.;Kudo, Y. Automatic lgP-estimation based on combined additive modeling methods. J. Comput.-Aided. Mol. Des. 1990, 4, 155-198.
[41]Hammett, L. P. Physical organic chemistry. New York: MaGraw-Hill Book Co., 1940.
[42] Branch, G. E. K.;Calvin, M. The Theory of Organic Chemistry. New York: Prentice Hall, 1941.
[43] Swain, C. G.;Lupton, E. C. Jr. Field and resonance components of substituent effects. J. Am. Chem. Soc. 1968, 90, 4328-4337.
[44] Charton, M. Nature of ortho effect, ⅱ .composition of the Taft steric parameters, J. Am. Chem. Soc. 1969, 91,615-618.
[45] Kutter, E.;Hansch, C. Steric parameters in drug design. Monoamine oxidase inhibitors and antistamines. J. Med. Chem. 1969, 12, 647-652.
[46] Hancock, C. K.;Meyers, E. A.;Yager, B. J. Quantitative separation of hyperconjugation effects from steric substituent constants. J. Am. Chem. Soc. 1961, 83, 4211-4213.
[47] Taft, R. W. Steric effects in organic chemistry. Newman M. S. ed. New York, N. Y: John Wiley & Sons, Inc., 1965, p556-675.
[48] Katritzky, A. R.;Lobanov, V. S.;Karelson, M. CODESSA: Reference Manual;Version 2;University of Florida, 1994.
[49] Connolly, M. L. Solvent-accessible surfaces of proteins and nucleic acids. Science. 1983, 221,709-713.
[50] Stanton, D. T.;Jurs, R C. Development and use of charge partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies. Anal. Chem. 1990, 62, 2323-2329.
[51] Livingstone, D. J. The Characterization of Chemical Structures Using Molecular Properties. A Survey J. Chem. Inf. Comput. Sci.,2000, 40, 195-209.
[52] Gasteiger, J.;Sadowski, J.;Selzer, P.;Steinhauer, L.;Steinhauer, V. Chemical Information in 3D Space J. Chem. Inf. Comput. Sci.,1996,36, 1030-1037.
[53] Gasteiger, J.;Burkard,S. U.;Hemmer, M. C.;Herwig, A.;von Homeyer, A.;Hollering, R.;Kleinoder, T.;Kostka,T.;Schwab,C.;Selzer, R;Steinhauer, L. Decision Support Systems for Chemical Structure Representation, Reaction Modeling, and Spectra Simulation SAR QSAR in Environm. Res., 2002,13,89-110.
[54] Engel,T.;Gasteiger, J. Chemical Structure Representation for Information Exchange Online Information Review, 2002,26, 139-145.
[55] Todeschini, R.;Gramatica, P. New 3D Molecular Descriptors: The WHIM theory and QSAR applications. In 3D QSAR in Drug Design (Kubinyi, H., Folkers, G. and Martin, Y.C., eds.), Kluwer/Escom, Dordrecht, The Netherlands, pp. 355-380.
[56] Bravi, G.;Gancia, E.;Mascagni, P.;Pegna, M.;Todeschini, R.;Zaliani, A. MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: a comparative 3D-QSAR study in a series of steroids. J. Computer-Aided Mol.Des. 1997,11, 79-92.
[57] Chiorboli, C.;Piazza, M.;Gramatica, P.;Pino, A.;Todeschini, R. 3D-modelling and prediction by WHIM descriptors. Part 7. Physico-chemical properties of haloaromatics: comparison between WHIM and topological descriptors. SAR QSAR Environ.Res. 1997, 7,133-150.
[58] Todeschini, R.;Gramatica, P. 3D-modelling and prediction by WHIM descriptors. Part 6. Applications of the WHIM descriptors in QSAR studies. Quant.Struct.-Act.Relat. 1997,16, 120-125.
[59] Todeschini, R., Moro, G., Boggia, R., Cosentino, U., Lasagni, M.;Pitea, D. Modeling and prediction of molecular properties. Theory of Grid-Weighted Holistic Invariant Molecular (G-WHIM) descriptors. Chemometrics & Intell.Lab.Syst. 1997,36, 65-73.
[60] Todeschini, R.;Gramatica, P. 3D-modelling and prediction by WHIM descriptors. Part 5. Theory development and chemical meaning of the WHIM descriptors. Quant. Struct.-Act.Relat. 1997,16, 113-119.
[61] Todeschini, R., Vighi, M., Finizio, A. & Gramatica, P. 3D-modelling and prediction by WHIM descriptors. Part 8. Toxicity and physico-chemical properties of environmental priority chemicals by 2D-TI and 3D-WHIM descriptors. SAR QSAR Environ.Res. 1997,7,173-193.
[62] Todeschini, R.;Gramatica, P.The WHIM Theory: New 3D Molecular Descriptors for QSAR in Environmental Modelling. SAR QSAR Environ.Res. 1997, 7,89-115.
[63] Todeschini, R.;Vighi, M.;Provenzani, R.;Finizio, A.;Gramatica, P. Modeling and prediction by using WHIM descriptors in QSAR studies: Toxicity of heterogeneous chemicals on Daphnia magna. Chemosphere 1996,32,1527-1545.
[64] Todeschini, R., Bettiol, C.;Giurin, G.;Gramatica, P.;Miana, P.;Argese, E. Modeling and prediction by using WHIM descriptors in QSAR studies: Submitochondrial Particles (SMP) as toxicity biosensors of chlorophenols. Chemosphere 1996,33,71-79.
[65] Todeschini, R.;Gramatica, P.;Marengo, E.;Provenzani, R. Weighted Holistic Invariant Molecular descriptors. Part 2. Theory development and applications on modeling physico-chemical properties of PolyAromatic Hydrocarbons (PAH). Chemometrics & Intell.Lab.Syst. 1995,27,221-229.
[66] Todeschini, R.;Lasagni, M.;Marengo, E. New Molecular Descriptors for 2D-and 3D-structures. Theory. J. Chemometrics 1994,8,263-273.
[67] http://www.tripos.com/
[68] Puri, S., Chickos, J. S.;Welsh, W. J. Three-Dimensional Quantitative Structure-Property Relationship (3D-QSPR) Models for Prediction of Thermodynamic Properties of Polychlorinated Biphenyls (PCBs): Enthalpy of Sublimation. J. Chem. Inf. Comput. Sci. 2002, 42, 109-116.
[69] Puri, S.;Chickos, J. S.;Welsh, W. J. Three-Dimensional Quantitative Structure-Property Relationship (3D-QSPR) Models for Prediction of Thermodynamic Properties of Polychlorinated Biphenyls (PCBs): Enthalpy of Vaporization. J. Chem. Inf. Comput. Sci. 2002, 42, 299-304.
[70] Hopfinger, A. J.;Burke, B. J.;Dunn, W. F., Ⅲ A Generalized Formalism of Three-dimensional Quantitative Structure-Property Relationship Analysis for Flexible Molecules Using Tensor Representation. J. Med. Chem. 1994, 37, 3768-3774.
[71] Hopfinger, A. J.;Wang, S., Tokarski, J. S.;Jin, B.;Albuquerque, M.;Madhav, P. J.;Duraiswami, C. Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism. J. Am. Chem. Soc. 1997, 119, 10509-10524.
[72] Hopfinger, A. J.;Wang, S., Tokarski, J. S.;Jin, B.;Albuquerque, M.;Madhav, P. J.;Duraiswami, C. Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism. J. Am. Chem. Soc. 1997, 119, 10509-10524.
[73] Duca, J. S.;Hopfinger, A. J. 4D-QSPR Analysis and Virtual Screening of Calcite Growth Inhibitor Libraries. Chem. Mater. 2000, 12, 3821-3829.
[74] Klein, C. D.;Hopfinger, A. J. Pharmacological activity and membrane interactions of antiarrhythmics: 4D-QSAR/QSPR analysis. Pharm. Res. 1998, 15, 303-311.
[75] Duca, J. S.;Tseng, Y.-E;Hopfinger, A. J. 4D-QSPR Analysis and Virtual Screening in Materials Science. Adv. Mater. 2001, 13, 1713-1717.
[76] http://www.mdli.com/.
[77] http://www.daylight.com/.
[78] 卢纹岱 SPSS for Windows,统计分析北京,电子工业出版社2002.
[79] 许禄《化学计量学方法》北京,科学出版社,1995年.
[80] Clark,D.E. (Ed.) Evolutionary Algorithms in Molecular Design Wiley-VCH, Weinheim (GER).
[81] Devillers, J. Genetic Algorithms in Molecular Modeling Academic Press, London 1996.
[82] 李华昌,谢淑兰,易忠胜 遗传算法的原理与应用。矿冶 2005,14,87-90。
[83] Holland J H. Adaptation in Natural and artificial Systems. Ann Arbor: The University of Michigan Press, 1975.
[84] Cong P.G.;Li T. H. Numeric genetic algorithm An al. Chem. Acta. 1994, 293, 191-203.
[85] Kovalishyn,V.V.;Tetko, I. V.;Luik,A. I.;Kholodovych,V.V.;Villa,A. E. P.;Livingstone, D. J. Neural Network studies. 3. Variable selection in the cascade-correlation learning architecture J. Chem. lnf Comput. Sci., 1998, 38, 651-659.
[86] Ferguson, A. P.;Heritage, T.;Jonathon, P.;Pack,S.E.;Phillips, L.;Rogan, J.;Snaith,P. J. EVA: A new theoretically based molecular descriptor for use in QSAR/QSPR analysis Journal of Computer Aided Molecular Design, 1997,11, 143-152.
[87] Livingstone,D. J.;Manallack, D.T.;Tetko,I. V. Data modelling with neural networks: advantages and limitations J. Computer-Aided Mol.Des. 1997,11,135-142.
[88] Tetko, I. V.;Villa, A. E. P.;Livingstone, D. J. Neural network studies. 2. Variable selection. J. Chem. Inf. Comput. Sci. 1996,36,794-803.
[89] Tetko, I. V.;Livinsgtone, D. J., Luik, A. I. Neural network studies. 1. Comparison of overfitting and overtraining J. Chem. Inf. Comput. Sci., 1995,35,826-833.
[90] Manallack, D. T.;Ellis, D. D.;Livingstone, D. J. Analysis of linear and non-linear QSAR Data for using neural networks J. Med. Chem.,1994,37,3758-67.
[91] Livingstone, D.J.;Manallack, D.T Statistics using neural networks: chance effects. J. Med. Chem,1993,36, 1295-1297.
[92] McCulloch, W. C.;Pitts W. A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophy 1943,5, 115-133.
[93] Hebb, D. O. The Organization of Behavior, Willey J., New York, 1949.
[94] Rosenblatt, F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 1958,65,386—407.
[95] Minsky, M.;Papert, S. Perceptrons: An Introducton to Computational Geometry, MIT Press, Cambridge, USA, 1969.
[96] Hopfield, J Neural networks and physical systems with emergent collective computational abilities. J. Proc. Nat. Acad. Sci., 1982, 79, 2554-2558.
[97] Hopfield, J. Neurons with graded response have collective computational properties like those of two-state neurons J. Proc. Nat. Acad. Sci., 1984, 81, 3088-3092.
[98] Rumelhart, D. E., Hinton, G. E.;Williams, R. J. in Microstructure of cognition, MIT Press, Cambridge, 1986, 11, 318-362.
[99] Lippmann, R. P. An introduction to computing with neural nets. IEEE ASSP Magazine.1987, 4, 4-22.
[100] Zupan, J.;Gasteiger, J. For a detailed overview of neural network applications in Chemistry up to 1990. Anal. Chim. Acta. 1991, 248, 1-30.
[101] Sumpter, B. G.;Gettino, C.;Noid, D. W. Theory and Applications of Neural Computing in Chemical Science Annu. Rev. Phys, Chem. 1994, 45, 439-481.
[102] Sumpter, B.G;Noid, D. W. On the design, analysis, and characterization of materials using computational neural networks. Annu. Rev. Mater. Sci. 1996, 26, 223-277.
[103] 韩相明 利用神经网络预测阴离子共偶碱金属卤化物二元系的相图特征,河南科学,2000,18,374—377.
[104] 李一波,黄小原 径向基函神经网络用于重叠色谱峰解析,色谱,2001,19,112-115.
[105] 李一波,黄小原.基于改进排序遗传算法的径向基函数神经网络色谱峰解析,分析化学,2001,29,253-257.
[106] Livingstone, D. Data analysis for chemists: application to QSAR and chemiscal product design. OUP, Oxford, 1995.
[107] Devillers, J. Neural networks in QSAR and drug design. Academic Press, London,1996.
[108] Burden, F. R.;Ford, M. G.;Whitley, D. C.;Winkler, D. A. The use of automatic relevance determination in QSAR studies using Bayesian neural networks. J. Chem. Inf Comput. Sci. 2000, 40, 1423-1430.
[109] Lucic, B.;Trinajstic, N. Multivariate regression outperforms several robust architectures of neural networks in QSAR modeling. J. Chem. Inf Comput. Sci. 1999, 39, 121-132.
[110] Zupan,J.;Gasteiger,J.著.潘忠孝,陈玲然.译.张懋森.校.神经网络及其在化学中的应用.合肥,中国科学技术大学出版社,2000.
[111] 吕庆章,王晓钰.计算机与应用化学,1998,2,100—104.
[112] 陈锋,周英.用改进的遗传算法训练神经网络以研究有机磷杀虫剂的构效关系.计算机与应用化学.1998.6,357-360.
[113] 高守国,相秉仁,安定奎.计算机与应用化学,1998,5,381-382.
[114] 吴新生,谢益民,刘焕彬.主成分分析法用于化工过程人工神经网络建模. 计算机与应用化学,1999,3,219—222.
[115] Walczak, B.;Massart, D. L. Local modelling with radial basis function networks, Chemometrics & Intell.Lab.Syst. 2000,50,179-198.
[116] Walczak, B.;Massart, D. L.The Radial Basis Functions - Partial Least Squares approach as a flexible non-linear regression technique, Anal. Chim. Acta., 1996, 331,177-185.
[117] Walczak, B.;Massart, D. L. Application of Radial Basis Functions-Partial Least Squares to non-linear pattern recognition problems: diagnosis of process faults, Anal. Chim. Acta. 1996, 331,187-193.
[118] Tetteh, J.;Howells, S.;Metcalfe, E.;Suzuki T. Optimisation of radial basis function neural networks using biharmonic spline interpolation, Chemometrics & Intell.Lab.Syst, 1998,41,17-29.
[119] Orr, M.J.L. Introduction to Radial basis function networks, centre for cognitive science, Edinburgh University, 1996.
[120] Orr, M.J.L. MATLAB routines for subset selection and ridge regression in linear neural networks, Centre for cognitive science, Edinburgh University, 1996.
[121] Pulido, A.;Ruisnchez. I.;Rius, F. X. Radial basis functions applied to the classification of UV-visible spectra, Anal. Chim. Acta., 1999,388, 273-281.
[122] Stubbings, T.;Hutter, H. Classification of analytical images with radial basis function networks and forward selection, Chemometrics & Intell.Lab.Syst, 1999,49, 163-172.
[123] Loukas, Y. L. Radial basis function networks in host-guest interactions: instant and accurate formation constant calculations, Anal. Chim. Acta.,2000, 417,221-229.
[124] Evans, P.;Persaud, K. C.;McNeish, A. S.;Sneath, R.;W.;Hobson, N.;Magan, N. Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data, Sensors and Actuators B: Chemical, 2000,69,348-358.
[125] Neural Network FAQ Part3 Generalization.
[126] Sarle, W. S. Proceedings of the 27th Symposium on the interface of Computing Science and Statistics, 1995,352-360.
[127]Geman, S.;Bienenstock, E.;Doursat, R. Neural network and bias/variance dilemma. Neural Computation, 1992,4, 1-58.
[128]Nelson, M.C.;Ⅱ lingworth, W.T. A Practical Guide to Neural Nets, Reading, MA: Addison-Wesley. 1991.
[129]Finnoff, W.;Hergert ,F.;Zimmermann, H.G. An unsupervised learning technique for artificial neural networks Neural Networks, 1993, 6,707-711.
[130]Lang K. J.;Waibel A.;Hinton, G. E. A time-delay neural network architecture for isolated word recognition. Neural Networks, 1990, 3, 23-43.
[131]Morgan, N.;Bourlard, H.;Touretzky, D.S.;Mateo, S.;Morgan Kaufman, Advances in Neural Information Processing Systems 2,ed. California, 1989,p. 107.
[132]袁曾任编著.《人工神经元及其应用》清华大学出版社,1999,1-27.
[133]Vapnik, N. Statistical Learning Theory. John Wiley & Sons, New York. 1998.
[134]Schlkopf, B.;Smola, A. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
[135]叶晨洲,杨杰,姚莉秀,陈念贻 统计学习理论的原理与应用,计算机与应用化学 2002,19,712—716.
[136]陈念贻,陆文聪,陆治荣.优化建模技术和机器学习理论的新发展,计算机与应用化学.2002,19,677—682.
[137]Cristianini, N.;Shawe-Taylor ,J.. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK, 2000.
[138]Thorsten J. Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms. Kluwer, 2002
[139]陈念贻,陆文聪,叶晨洲,李国正.支持向量机及其他核函数算法在化学计量学的应用,计算机与应用化学.2002,19,691—696.
[140]陈念贻,陆文聪 支持向量机算法在化学和化工中的应用,计算机与应用化学.2002,19,673—676.
[141]陆文聪,陈念贻,叶晨洲,李国正 支持向量机算法和ChemSVM介绍,计算 机与应用化学,2002,19,697—702.
[142]Burges, C. J. C. A Tutorial on Support Vector Machines for Pattern Recognition Data Min. Knowl. Disc., 1998,2,121-167.
[143]Belousov, A. I.;Verzakov, S. A.;von Frese, J. Applicational aspects of support vector machines. J. Chemomet. 2002,16,482-489.
[144]Belousov, A. I.;Verzakov. S. A.;von Frese, J. A flexible classification approach with optimal generalisation performance:support vector machines, Chemometrics & Intell.Lab.Syst, 2002,64, 15-25.
[145]Gunn, S.R.;Brown, M.;Bossley, K.M. Network performance assessment for neurofuzzy data modeling. Lect. Notes Comput. Sci. 1997, 1280, 313-323.
[146]Vapnik, V. The nature of statistical learning theory. New York, Springer-Verlag, 1995.
[147]Cristianini, N.;Shawe-Taylor, J. Introduction to support vector machine and other kernel based learning methods. Cambridge: Cambridge University Press, 2000.
[148]Vapnik, V.;Chervonenkis, A. Y. The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognit. Image Anal. 1991, 1,283-305.
[149]Vapnik, V. Estimation of Dependences Based on Empirical Data;Springer: Berlin, 1982.
[150]Smola, A. J.;Scholkopf, B. A tutorial on support Vector regression;NeuroCOL2 Technical report series, NC2-TR-1998-030;October, 1998.
[151]Burges, C. J. C. A tutorial of support vector machines for pattern recognition. http://svm.research.bell-labs.com/SVMdoc.html,1998.
[152]Vapnik, V.;Golowich, S.;Smola, A. Support Vector Method for function approximation, regression estimation, and signal processing. Adv. Neural Inform. Process. Systems 1997, 9, 281-287.
[153]陶卿,姚穗,范劲松,方廷健.一种新的机器学习算法:support vector machines.模式识别与人工智能.2000,13,285-290.
[154]薛春霞.博士毕业论文:SVM在QSPR中的应用及基于配体的计算机辅助药物设计,兰州大学,2005年
[155]Cai, Y. D.;Liu, X. J.;Xu, X. B.;Chou, K. C. Support vector machines for predicting HIV protease cleavage sites in protein, J. Comput. Chem. 2002, 23, 267-274.
[156]Bao, L;Sun, Z. Identifying genes related to drug anticancer mechanisms using support vector machine. FEBS Letters 2002, 521, 109-114.
[157]Furey, T.;Cristianini, N.;Duffy, N.;Bednarski, D.;Schummer, Michel.;Haussler, D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16, 906-914.
[158]Brown, M.;Grundy, W.;Lin, D.;Cristianini, N.;Sugnet, C.;Furey, T.;Ares Jr., M.;Haussler, D. Knowledge-based analysis of microarray gene expression data using support vector machines. Proc. Natl. Acad. Sci. U. S. A. 2000, 97, 262-267.
[159]Guyon, I.;Weston, J.;Barnhill, S.;Vapnik, V. Gene selection for cancer classification using support vector machines. Math. Learn. 2002, 46, 389-422.
[160]Ramaswamy, S.;Tamayo, E;Rifkin, R.;Mukherjee, S.;Yeang, C. H.;Angelo, M.;Ladd, C.;Reich, M.;Latulippe, E.;Mesirov, J. E;Poggio, T.;Gerald, W.;Loda, M.;Lander, E. S.;Golub, T. R. Multiclass cancer diagnosis using tumor gene expression signatures. Pro~. Natl. Acad. Sci. U.S.A. 2001, 98, 15149-15154.
[161]Cai, Y. D.;Liu, X. J.;Xu, X. B.;Chou, K. C. Prediction of protein structural classes by support vector machines. Comput. Chem. 2002, 26, 293-296.
[162]Cai, Y. D.;Liu, X. J.;Xu, X. B.;Chou, K. C. Support vector machines for the classification and prediction of beta-turn types. J. Pept. Sci. 2002, 8, 297-301.
[163]Hua, S.;Sun, Z. Novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J..Mol. Biol.2001, 308, 397-407.
[164]Ding, C. H. Q.;Dubchak, I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 2001, 17, 349-358.
[165]Cai, Y. D.;Liu, X. J.;Xu, X. B.;Chou, K. C. Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect. J. Cell. Biochem. 2002, 84, 343-348.
[166] Hua, S.;Sun, Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics 2001, 17, 721-728.
[167] Bock, J. R.;Gough, D. A. Predicting protein-protein interactions from primary structure. Bioinformatics 2001, 17, 455-460.
[168] Karchin, R.;Karplus, K.;Haussler, D. Classifying G-protein coupled receptors with support vector machines. Bioinformatics 2002, 18, 147-159.
[169] Lee, D.;Choi, S. W.;Kim, M.;Park, J. H.;Kim, M.;Kim, J.;Lee, I. B. Discovery of differentially expressed genes related to histological subtype of hepatocellular carcinoma. Biotechnol. Prog. 2003, 19, 1011-1015.
[170] Xue, Y.;Yap, C. W.;Sun, L. Z.;Cao, Z. W.;Wang, J. F.;Chen, Y. Z. Prediction of P-glycoprotein substrates by a support vector machine approach. J. Chem. Inf Comput. Sci. 2004, 44, 1497-1505.
[171] Anderson, D. C.;Li, W.;Payan, D. G.;Noble, W. S. A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores. J. Proteom. Res. 2003, 2, 137-146.
[172] Zhao, C. Y.;Zhang, R. S.;Liu, H. X.;Xue, C. X.;Zhao, S. G.;Zhou, X. F.;Liu, M. C.;Fan, B. T. Diagnosing anorexia based on partial least squares, back propagation neural network, and support vector machines. J. Chem. Inf Comput. Sci. 2004, 44, 2040-2046.
[173] Liu, H. X.;Zhang, R. S.;Luan, F.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Diagnosing breast cancer based on support vector machines. J. Chem. Inf. Comput. Sci. 2003, 43, 900-907.
[174] 刘焕香.博士毕业论文:基于支持向量机方法的QSAR/QSPR在化学、生物及环境科学中的应用研究.兰州大学,2005年.
[175] Song, M.;Breneman, C. M.;Bi, J.;Sukumar, N.;Bennett, K. P.;Cramer, S.;Tugcu, N. Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J. Chem. Inf. Comput. Sci. 2002, 42, 1347-1357.
[176] Lind, P.;Maltseva, T. Support vector machines for the estimation of aqueous solubility. J. Chem. Inf. Comput. Sci. 2003, 43, 1855-1859.
[177] Xue, C. X.;Zhang, R. S.;Liu, H. X.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines. J. Chem. Inf. Comput. Sci. 2004, 44, 669-677.
[178] Xue, C. X.;Zhang, R. S., Liu, M. C.;Hu, Z. D., Fan, B. T. Study of the quantitative structure-mobility relationship of carboxylic acids in capillary electrophoresis based on support vector machines J. Chem. Inf Comput. Sci. 2004, 44, 950-957.
[179] Xue, C. X.;Zhang, R. S.;Liu, H. X.;Liu, M. C., Hu, Z. D.;Fan, B. T. Support vector machines-based quantitative structure-property relationship for the prediction of heat capacity. J. Chem. Inf Comput. Sci. 2004, 44, 1267-1274.
[180] Liu, H. X.;Zhang, R. S.;Yao, X. J., Liu, M. C.;Hu, Z. D., Fan, B. T. Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs. J. Chem. Inf Comput. Sci. 2004, 44, 161-167.
[181] Liu, H. X.;Xue, C. X.;Zhang, R. S.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine. J. Chem. Inf. Comput. Sci. 2004, 44, 1979-1986.
[182] Liu, H. X., Zhang, R. S.;Yao, X. J.;Liu, M. C., Hu, Z.D., Fan, B.T. Prediction of electrophoretic mobility of substituted aromatic acids in different aqueous-alcoholic solvents by capillary zone electrophoresis based on support vector machine. Anal. Chim. Acta 2004, 525, 31-41.
[183] Luan, F., Zhang, R. S., Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Support vector machine-based QSPR for the prediction of Van der Waals'constants. QSAR Comb. Sci. 2005, 24, 227-239..
[184] Luan, F., Xue, C X.;Zhang, R. S., Zhao, C. Y.;Liu, M. C., Hu, Z. D., Fan, B. T. Prediction of retention time of a variety of volatile organic compounds based on the heuristic method and support vector machine. Anal. Chim. Acta 2005, 537, 101-110.
[185] Ma, W. P., Zhang, X. Y.;Luan, F.;Zhang, H. X. Zhang, R. S., Liu, M. C.;Hu, Z. D.;Fan. B.T. Support Vector Machine and the Heuristic Method to Predict the Solubility of Hydrocarbons in Electrolyte, J. Phys. chem. A, 2005, 109, 3485-3492.
[186] Ma, W. P.;Luan, E;Zhang, H. X., Zhang, X. Y.;Liu, M. C.;Hu, Z. D.;Fan. B.T. Quantitative Structure-Property Relationships for Pesticides in Biopartitioning Micellar Chromatography. J. chromatogr. A 2006 ,In Press.
[187] Luan, F.;Zhang, X. Y.;Zhang, H. X.;Zhang, R. S.;Liu, M. C.;Hu, Z. D.;Fan. B.T. Prediction of Standard Gibbs Energies of the Transfer of Peptide Anions from Aqueous Solution to Nitrobenzene Based on Support Vector Machine and the Heuristic Method. J. Computer-Aided Mol.Des.. 2006 In press.
[188] Luan, F.;Zhang, X. Y., Zhang, H. X.;Zhang, R. S.;Liu, M. C.;Hu, Z. D.;Fan. B. T.QSPR Study of Permeability Coefficients Through Low-density Polyethylene Based on Radial Basis Function Neural Networks and the Heuristic method, Comp. Mater. Sci. 2006, In Press.
[189] Byvatov, E.;Fechner, U.;Sadowski, J., Schneider, Gisbert. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification, or. Chem. Inf. Comput. Sci. 2003, 43, 1882-1889.
[190] Liu, Y. A comparative study on feature selection methods for drug discovery. J. Chem. Inf. Comput. Sci. 2004, 44, 1823-1828.
[191] Xue, Y., Li, Z. R.;Yap, C. W.;Sun, L. Z.;Chen, X., Chen, Y. Z. Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. J. Chem. Inf. Comput. Sci. 2004, 44, 1630-1638.
[192] Sadik, O.;Land, Jr., W. H.;Wanekaya, A. K.;Uematsu, M., Embrechts, M. J.;Wong, L., Leibensperger, D., Volykin, A. Detection and classification of organophosphate nerve agent simulants using support vector machines with multiarray sensors. J. Chem. Inf Comput. Sci. 2004, 44, 499-507.
[193] Sorich, M. J.;Miners, J. O., McKinnon, R. A.;Winkler, D. A., Burden, F. R.;Smith, P. A. Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP-Glucuronosyltransferase isoforms. J. Chem. Inf. Comput. Sci. 2003, 43, 2019-2024.
[194] Czerminske, R.;Yasri, A.;Hartsough, D. Use of support vector machines in pattern classification: application to QSAR studies. Quant. Struct.-Act. Relat. 2001, 20, 227-240.
[195] Zernov, V. V.;Balakin, K. V., Ivaschenko, A. A.;Savchuk, N. P.;Pletnev, I. V. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J. Chem. Inf. Comput. Sci. 2003, 43, 2048-2056.
[196] Zhao, C. Y., Zhang, H.X.;Zhang, X. Y.;Zhang, R.S.;Luan, F.;Liu, M.C.;Hu, Z.D.;Fan. B.T. Prediction of Milk-to-Plasma Drug Concentration (M/P) Ratio Using Support Vector Machine (SVM) Method. Pharm Res. 2006, 23, 41-48.
[197] Burbidge, R.;Trotter, M., Buxton, B., Holden, S. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 2001, 26, 5-14.
[198] Norinder, U. Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimizations and variable selection. Neurocomputing 2003, 55, 337-346.
[199] Kovatcheva, A., Golbraikh, A.;Oloff, S.;Xiao, Y.-D., Zheng, W., Wolschann, P.;Buchbauer., G.;Tropsha, A. Combinatorial QSAR of ambergris fragrance compounds. J. Chem. Inf. Comput. Sci. 2004, 44, 582-595.
[200] Xue, C. X.;Zhang, R. S.;Liu, H. X.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine. J. Chem. Inf. Comput. Sci. 2004, 44, 1693-1700.
[201] Yao, X. J.;Panaye, A.;Doucet, J. P.;Zhang, R. S.;Chen, H. F.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. J. Chem. Inf. Comput. Sci. 2004, 44, 1257-1266.
[202]Liu, H. X.;Zhang, R. S.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: an inhibitor of AP-1 and NF-κB mediated gene expression based on support vector machines. J. Chem. Inf. Comput. Sci. 2003, 43, 1288-1296.
[203]Kramer, S.;Frank, E.;Helma, C. Fragment generation and support vector machines for inducing SARs. SAR QSAR Environ.Res. 2002, 13, 509-523.
[204]Ivanciuc, O. Structure-odor relationships for pyrazines with support vector machines. Internet Electronic Journal of Molecular Design. 2002, 1,269-284.
[205]Ivanciuc, O. Support vector machine classification of the carcinogenic activity of polycyclic aromatic hydrocarbons. Internet Electronic Journal of Molecular Design. 2002, 1,203-218.
[206]陆文聪,王琨,陈念贻.支持向量机算法用于拮抗药化合物活性的模式识别.计算机与应用化学 2002,19,741—744.
[207]赵慧,陆文聪,张良苗,陈念贻.支持向量机用于胍类化合物Na/H交换抑制活性的模式识别.计算机与应用化学 2002,19,745—748.
[208]Ivanciuc, O. Support vector machine identification of the aquatic toxicity mechanism of organic compounds. Internet Electronic Journal of Molecular Design. 2002, 1, 151-172.
[209]Luan, F.;Zhang, R. S;Zhao, C. Y.;Yao, X. J.;Liu, M. C.;Hu, Z. D.;Fan, B. T. Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis. Chem. Res. Toxicol. 2005, 18, 198-203.
[210]Luan, F.;Ma, W. P.;Zhang, H. X.;Zhang, X. Y.;Liu, M. C.;Hu, Z. D.;Fan. B.T. QSAR Study of Polychlorinated Dibenzodioxins, Dibenzofurans, and Biphenyls using the Heuristic Method and Support Vector Machine. QSAR Comb. Sci.2006, 25, 46-55.
[211] Luan, F.;Ma, W. P.;Zhang, H. X.;Zhang, X. Y.;Liu, M. C.;Hu, Z. D.;Fan. B.T. Quantitative Structure-Activity Relationship Models for Prediction of Sensory Irritants (logRD_(50)) of Volatile Organic Chemicals. Chemosphere, 2006 In press.
[212] Thissen, U.;Ostün, B.;Melssen, W. J.;Buydens, L. M. C. Multivariate calibration with least-squares support vector machines. Anal. Chem. 2004, 76, 3099-3105.
[213] Byvatov, E.;Schneider, G. SVM-based feature selection for characterization of focused compound collections. J. Chem. Inf Comput. Sci. 2004, 44, 993-999.
[214] 陈念贻,陆文聪,叶晨洲,李国正.支持向量机及其他核函数算法在化学计量学中的应用.计算机与应用化学,2002,19,691-696.
[215] 王冰,栾锋,刘满仓,胡之德,基于线形判别式和支撑向量机的肾结石分类方法,兰州大学学报,2006,In press.
[216] Zhai, H. L.;Gao, H.;Chen, X. G.;Hu, Z. D. An assisted approach of the global optimization for the experimental conditions in capillary electrophoresis. Anal. Chim. Acta, 2005,546,112-118.