摘要
本文结合传统分子结构表征(MSC)技术的优点提出了一种全新的3D结构描述子:三维原子场全息作用矢量( Three Dimensional Holographic Vector of Atomic Interaction Field, 3D-HoVAIF)。该方法从分子立体结构的两个空间不变量——原子相对距离和原子自身性质出发,基于三种常见的与生物活性直接相关的原子间非键作用方式(静电、立体和疏水作用),在不借助任何实验参数及无需样本构象重叠的前提下得到了用于表征药物分子结构特征的三维矢量描述子。使用Chemoffice 8.0构建待研究样本的分子立体结构,并使用Chem3D自带的MOPAC半经验量子化学软件在AM1水平上优化得到分子结构,并采用Mulliken布局分析法以单点(single-point)形式计算出原子的净电荷数量,将分子中每个原子的空间位置及电荷分别以笛卡儿坐标和净电子数目的形式输入到由C语言编写的应用程序中加以处理得到3D-HoVAIF描述子。三维原子场全息作用矢量最多含有165个描述子,由于缺少一些原子类型,所以对于大多数体系,作用矢量少于165个。然后运用逐步线性回归(SMR)筛选变量后,用多元线性回归(MLR),偏最小二乘(PLS)等统计工具建立药物定量结构-活性关系(QSAR)模型,取得了较满意的结果。该方法创新之处在于无须任何实验参数及无需样本构象叠合、计算简便迅速、物理化学意义明确并且避免了当今大多3D药物设计技术在应用前需要对分子进行叠合操作等方面缺点;另外3D-HoVAIF按族对原子进行分类使其可以适用于含有多种杂原子的复杂分子体系。
(1)采用3D-HoVAIF对19个喹诺酮类化合物进行结构表征。采用逐步回归对变量进行筛选后,运用偏最小二乘回归建立了3D-HoVAIF描述子与喹诺酮类化合物抗肿瘤活性之间的QSAR模型,该模型的复相关系数(R~2)、交互校验复相关系数(Q~2)和模型的拟和均方根误差(RMSEE)分别为R~2= 0.912, Q_(cum)~2= 0.811,RMSEE= 0.284。表明三维原子场全息作用矢量能有效地提取该类分子结构信息并与其生物活性呈良好线性关系。
(2)采用3D-HoVAIF对26个噻唑并[2,3-d]嘧啶-4-酮类化合物的6种抗菌活性进行了系统的QSAR研究,建立了SMR-PLS定量构效关系模型,所建模型的复相关系数(R~2)、交互校验复相关系数(Q~2)和模型的拟和均方根误差(RMSEE)分别为R~2≥0.891, Q_(cum)~2≥0.85和RMSEE≤0.174,得到的6个模型预测能力和稳定性均较好,优于文献利用拓扑指数建立的Hansch分析结果。
(3)采用3D-HoVAIF对68个甲状腺激素受体(TR)配体化合物的化学结构与活性建立定量构效关系。对化合物进行了结构参数化表达, 55个样本为训练集,13个样本为预测集,采用SMR-PLS建立了定量构效关系模型,模型的复相关系数和交互检验复相关系数和拟和均方根误差分别为R~2=0.766, Q_(cum)~2=0.586,RMS=0.641 (TRα); R~2=0.754, Qcum2=0.593,RMS=0.672 (TRβ),与文献的二维结果q~2=0.781 (TRα)和q~2=0.693 (TRβ)相当,但本模型物理意义明确。
(4)采用3D-HoVAIF对123个流感病毒神经氨酸酶抑制剂建立定量构效关系,运用逐步线性回归(SMR)筛选变量后,用多元线性回归(MLR)建模的得到了9变量模型,相关系数为R=0.885,留一法检验的复相关系数为RCV2=0.736。显然,3D-HoVAIF能够较好地表征神经氨酸酶抑制剂的构效关系,所建模型具有良好的内部估计能力和外部预测能力。
为了深入分析3D-HoVAIF对神经氨酸酶抑制剂样本集的表达和建模性能,采用D-优化算法将123个抑制剂分为两部分,即训练集和测试集各为100和23个样本。同样用3D-HoVAIF对100个神经氨酸酶抑制剂进行结构表征,然后采用逐步回归对变量进行筛选后,运用偏最小二乘建立3D-HoVAIF描述子与神经氨酸酶抑制剂活性之间的QSAR模型。结果:复相关系数(R~2),交互校验的复相关系数(Q2)和模型的标准偏差分别为R~2=0.805,Q2=0.657,SD=0.936,模型具有良好的稳定性和预测能力,并对文献中23个药物和设计的32个化合物进行了预测。表明三维全息原子场作用矢量能较好表征该类分子结构信息,值得进一步推广应用。
(5)基于分子三维结构信息的三维原子场全息作用矢量(3D-HoVAIF)的构效关系研究,再根据流感病毒神经氨酸酶(NA)的活性点以及与神经氨酸酶抑制剂(NAI)的缀合特性并参考已有研究成果,设计出了11个系列共计82个化合物,其中有的预测活性较已上市的奥司米韦(oseltamivir, Tamiflu?)要高,这些设计化合物母环结构分别是环己烯系、呋喃系、吡唑系和吡嗪系,母环为吡啶和嘧啶系的设计化合物的预测生物活性与奥司米韦接近,它们均将作为重点后续研究对象。
A novel molecular structural characterization (MSC) method, three dimensional holographic vector of atomic interaction field (3D-HoVAIF), is proposed in our laboratory, focusing its idea on three points: (a) Atoms are typed for 10 kinds according to their families in periodic table of elements and self hybridization states; (b) Three non-bonded (electrostatic, van der Waals and hydrophobic) factors, directly related to bioactivities, are utilized to express intramolecular potential energies; (c) Based on molecular steric structures, 165 non-bond interaction items calculated are taken as the three-dimensional (3D) structural descriptors of the molecule. Original spatial structures of the target compounds are autogenerated by software Chemoffice 8.0, then implementing a molecular mechanic (MM) conformation optimization (adopting MM+force field) with molecular simulation software HyperChem 7.5, and the end semi-experimental quantum chemistry software MOPAC 6.0 is further utilized to generate the ultimate 3D structures at AM1 levels. Simultaneously, atomic partial charges are calculated by Mülliken population analysis. Taking forms of Cartesian coordinates and partial charges respectively, spatial position for each atom in a molecule and the atomic charges are input into C-edited program Super-3D.EXE, giving rise to 3D-HoVAIF descriptors of the molecules. In the modeling process, stepwise multiple linear regression (SMR), multiple linear regression (MLR), partial least squares (PLS) regression, are used to correlate the 3D vector of molecules with their dissolved data, most obtained models have superior quality compared with literatures.
(1) The developed three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) was used to describe the chemical structures of 19 antitumor quinolone agents. After the structural characterization, the descriptors obtained were screened by stepwise multiple regression (SMR) then a model of 3D-HoVAIF descriptors and antitumor quinolone agents’activity was built with partial least square regression ( PLS). The obtained model with the cumulative multiple correlation coefficient (R~2) , cumulative cross-validated (Q~2) and the Root Mean Square Error of Estimation (RMSEE) were R~2=0.912, Q_(cum)~2=0.811, RMSEE=0.284 respectively. The result confirmed that 3D-HoVAIF is able to extract molecular steric potential information efficiently,and well to relate with the bioactivities.
(2) And the developed descriptor, three dimensional holographic vector of atomic inter-action field(3D-HoVAIF), was also used to describe the chemical structures of 26 thienopyrimidones. After variable screening by stepwise multiple regression (SMR) technique, a partial least square (PLS) regression model was built with 3D-HoVAIF. The model is satisfactory comparing to reference since correlation coefficients of molecular modeling(R~2), cross-validation(Q_(cum)~2) and root-mean-square error of estimations(RMSEE) are≥0.891,≥0.85 and≤0.174, respectively, showing that the model has favorable estimation and prediction capabilities.
(3) To study the relationships between the chemical structures of Thyroid receptors ligands with activities, the developed three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) was used, 55 samples for the training set and 13 samples for the test set. After the structural characterization, the descriptors obtained were screened by stepwise multiple regression (SMR), correlation coefficients and crossvalidated correlation coefficients were obtained , R~2=0.766,Q2=0.586, RMS=0.641 (TRα) and R~2=0.754, Q~2=0.593, RMS=0.672 (TRβ) respectively. These results show that the model have favorable stability and good prediction capability and the 3D-HoVAIF is applicable to the molecular structural characterization and biological activity prediction, suggesting that the models could be useful in the design of novel, more potent TR ligands.
(4) To study the quantitative structure-activity relationship QSAR of 100 influenza neuraminidase inhibitors, three-dimensional holographicvector of atomic interaction field (3D-HoVAIF) method was used to describe the chemical structure of influenza neuraminidase inhibitors. After the structural characterization, the descriptors obtained were screened by least square regression ( PLS). The obtained model with the cumulative multiple correlation coefficient (R_(cum)~2 ) , cumulative cross-validated (Qcum2 ) , and standard error of estimation ( SD) were R_(cum)~2 = 0.805, Q_(cum)~2 = 0.657 and SD = 0.936, respectively. The result shows that the model has favorable stability and good prediction capability and predicted the literature 23 drugs as well as design of 32 compounds. The 3D-HoVAIF is applicable to the molecular structural characterization and biologicalactivity prediction.
(5) Finally, using 3D-HoVAIF method, 11 series of the designed neuraminidase inhibitors were predicted , according to the active sites of influenza neuraminidase. Some of them may be higher activities than oseltamivir, an approved neuraminidase inhibitor recently. This designed compounds will be studied in the future in our laboratory.
引文
[1] [德]约汉·加斯泰格尔,托马斯·恩格尔著.梁逸曾,徐峻,姚建华[译].化学信息学教程[M].北京:化学工业出版社, 2005.
[2]许禄.化学计量学方法[M].北京:科学出版社, 1995.
[3]刘树深,易忠胜.基础化学计量学[M].北京:科学出版社, 1999.
[4]倪永年.化学计量学在分析化学中的应用[M].北京:科学出版社, 2004.
[5]胡育筑.化学计量学简明教程[M].北京:中国医药科技出版社, 1997.
[6]梁逸曾,俞汝勤.分析化学手册.化学计量学,第十分册[M].北京:化学工业出版社, 2000.
[7]朱尔一,杨原.化学计量学技术及应用[M].北京:科学出版社, 2001.
[8]陆晓华.化学计量学[M].武汉:华中理工大学出版社, 1997.
[9] Massart D L, Vandeginste B G M, Buydens L M C, Jong S de, Lewi P J,Smeyers-Verbeke, J. Amsterdam: Handbook of chemometrics and qualimetrics: Part A[M]. Elsevier, 1997.
[10] Massart D L, Vandeginste B G M, Buydens L M C, Jong S de, Lewi P J,Smeyers-Verbeke J. Handbook of chemometrics and qualimetrics, Part B[M]. Elsevier: Amsterdam, 1998.
[11] Kunick C, Lauenroth K, Wieking K, Xie X, Schultz C, Gussio R, Zaharevitz D, Leost,M, Meijer L, Weber A, Jorgensen F S, Lemcke T. Evaluation and comparison of 3D-QSAR CoMSIA models for CDK1, CDK5, and GSK-3 inhibition by paullones[J]. J. Med. Chem., 2004, 47: 22-36.
[12] Katritzky A R, Maran U, Lobanov V S, Karelson M. Perspective: Structurally diverse quantitative structure-property relationship correlations of technologically relevant physical properties[J]. J. Chem. Inf. Comput. Sci., 2000, 40: 1-18.
[13] Senese C L, Duca J, Pan D. 4D-fingerprints, universal QSAR and QSPR descriptors[J]. J. Chem. Inf. Comput. Sci., 2004, 44: 1526-1539.
[14]王连生,韩朔睽.分子结构、性质与活性[M].北京:化学工业出版社, 1997,177-178.
[15] Ekins S. Predicting undesirable drug interactions with promiscuous proteins in silico[J]. Drug Discovery Today, 2004, 9: 276-285.
[16]刘树深,有机物分子电性距离矢量表征及其应用[M].北京:高等教育出版社, 2005.
[17] Oprea T I, Waller C L. Rational drug design: Novel methodology and practical applications[M], Oxford University Press: New York, 1999, 256-257.
[18] Klebe G, Abraham U, Mietzner T. Molecular similarity indexes in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity[J]. J. Med.Chem., 1994, 37: 4130-4146.
[19] [美]马丁原著,王尔华编译.定量药物设计[M].北京:人民卫生出版社, 1983,1-39.
[20]李志良.定量构效关系研究进展[J].化学通报, 1995, 9: 5-10.
[21]徐筱杰,侯廷军,乔学斌,章威.计算机辅助药物分子设计.北京:化学工业出版社. 2004: 246.
[22] Ferguson, J. The use of chemical potentials as indices of toxicity[J]. Proceedings of the Roval Sociity. Ser. B., 1939, 127: 487-503.
[23] Hansch C, Muir R M, Fujita T, Maloney P P, Geiger F, Streich M. The correlation of biological activity of plant growth regulators and chloromycetin derivatives with Hammett constants and partition coefficients[J]. J. Am. Chem. Soc., 1963, 85: 2817-2824.
[24] Free S M, Wilson J W. A mathematical contribution to Structure-Activity studies[J]. J. Med. Chem., 1964, 7: 395-399.
[25] Battershell, C.; Malhotra, D.; Hopfinger, A. J. Inhibition of Dihydrofolate Reductase: Structure-Activity Correlations of Quinazolines Based Upon Molecular Shape Analysis[J]. J.Med. Chem. 1981, 24: 812-818.
[26] Hopfinger, A. J. Inhibition of Dihydrofolate Reductase: Structure-Activity Correlations of2,4-Diamino-5-Benzylpyrimidines Based Upon Molecular Shape Analysis[J]. J. Med. Chem. 1981, 24: 818-822.
[27] Hopfinger, A. J. A General QSAR for Dihydrofolate Reductase Inhibition by 2, 4-Diamino-triazines Based Upon Molecular Shape Analysis[J]. Arch. Biochem. Biophys. 1981, 206: 153-163.
[28] Hopfinger, A. J.; Potenzone, R. Ames Test and Antitumor Activity of 1-(X-Phenyl)-3, 3-Dialkyltriazenes. Quantitative Structure-Activity Studies Based Upon Molecular ShapeAnalysis[J].. Mol. Pharmacol. 1982, 21: 187-195.
[29] Crippen, G. M.; Oppenheimer, N. J.; Connolly, M. L. Distance Geometry Analysis of theN.M.R. Evidence on the Solution Conformation of Bleomycin[J]. Int. J. Pept. Protein Res. 1981,17: 156-169.
[30] Havel, T. F.; Crippen, G. M.; Kuntz, I. D.; Blaney, J. M. The Combinatorial DistanceGeometry Method for the Calculation of Molecular Conformation. II. Sample Problems and Computational Statistics[J]. J. Theor. Biol. 1983, 104: 383-400.
[31] Cramer, R. D.; Patterson, D. E.; Bunce, J. D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins[J]. J. Am. Chem. Soc. 1988, 110: 5959-5967.
[32] 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]. J. Am. Chem. Soc. 1997, 119: 10509-10524.
[33] Albuquerque, M. G.; Hopfinger, A. J.; Barreiro, E. J.; de Alencastro, R. B. Four -Dimensional Quantitative Structure-Activity Relationship Analysis of a Series of Interphenylene 7-Oxabi-cycloheptane Oxazole Thromboxane A2 Receptor Antagonists[J]. J. Chem. Inf. Comput. Sci. 1998, 38: 925-938.
[34] Vedani, A.; Dobler, M.; Dollinger, H.; Hasselbach, K. M.; Birke, F.; Lill, M. A. Novel Ligands for the Chemokine Receptor-3 (CCR3): a Receptor-Modeling Study Based on 5D-QSAR[J]. J.Med. Chem. 2005, 48: 1515-1527.
[35] Vedani, A.; Dobler, M. 5D-QSAR: the Key for Simulating Induced Fit? [J]. J. Med. Chem. 2002, 45: 2139-2149.
[36] Vedani, A.; Briem, H.; Dobler, M.; Dollinger, H.; McMasters, D. R. Multiple-Conformation and Protonation-State Representation in 4D-QSAR: the Neurokinin-1 Receptor System[J]. J.Med. Chem. 2000, 43: 4416-4427.
[37] Ducki, S.; Mackenzie, G.; Lawrence, N. J.; Snyder, J. P. Quantitative Structure-Activity Relationship (5D-QSAR) Study of Combretastatin-Like Analogues As Inhibitors of Tubulin Assembly[J]. J. Med. Chem. 2005, 48: 457-465.
[38] Vedani, A.; Dobler, M.; Lill, M. A. Combining Protein Modeling and 6D-QSAR. Simulating the Binding of Structurally Diverse Ligands to the Estrogen Receptor[J]. J. Med. Chem. 2005, 48: 3700-3703.
[39]李志良,曾鸽鸣,胡芳,梁本熹,村松Y,松本S,李梦龙.多维定量构效关系及药物分子设计研究进展[J].化学研究与应用, 1997, 9 : 7-14.
[40] Hansch, C.; Hoekman, D.; Leo, A.; Weininger, D.; Selassie, C. D. Chem-Bioinformatics: Comparative QSAR at the Interface Between Chemistry and Biology[J]. Chem. Rev. 2002, 102: 783-812.
[41]丁俊杰,丁晓琴,赵立峰,陈冀胜.多肽定量构效关系与分子设计[J]..化学进展, 2005, 17: 130-136.
[42] Karelson, M.; Lobanov, V. S.; Katritzky, A. R. Quantum-Chemical Descriptors in QSAR /QSPR Studies[J]. Chem. Rev. 1996, 96: 1027-1044.
[43]王连生,韩朔睽.分子结构、性质与活性[M].北京:化学工业出版社, 1997: 177-178.
[44]李仁利.药物构效关系[M].北京:中国医药科技出版社, 2004: 218-239.
[45]郭宗儒.药物分子设计[M].北京:科学出版社, 2005: 436-440.
[46]徐筱杰,侯廷军,乔学斌,章威.计算机辅助药物分子设计[M].北京:化学工业出版社. 2004: 246-277.
[47] Estrada, E.; Ramirez, A. Edge Adjacency Relationships and Molecular Topographic Descriptors. Definition and QSAR applications[J]. J. Chem. Inf. Comput. Sci. 1997, 37: 837-843.
[48] Cunningham, S. L.; Cunningham, A. R.; Day, B. W. CoMFA, HQSAR and Molecular Docking Studies of Butitaxel AnaloguesWith Beta-Tubulin[J]. J. Mol. Model. 2005, 11: 48-54.
[49] Rodrigues, C. R.; Flaherty, T. M.; Springer, C.; McKerrow, J. H.; Cohen, F. E. CoMFA and HQSAR of Acylhydrazide Cruzain Inhibitors[J]. Bioorg. Med. Chem. Lett. 2002, 12: 537-1541.
[50]侯廷军,徐筱杰.比较分子场分析方法研究的最新进展[J].化学进展, 2001, 13: 436-440.
[51] Randic, M. On Characterization of Molecular Branching[J]. J. Am. Chem. Soc. 1975, 97: 6609-6615.
[52] Kier, L. B.; Hall, L. H.; Murray, W. J.; Randic, M. Molecular Connectivity 1: Relationship to Nonspecific Local Anesthesia[J]. J. Pharm. Sci. 1975, 64: 1974-1977.
[53] Kier, L. B; Murray, W. J.; Hall, L. H. Molecular Connectivity 4: Relationship to Biological Activity[J]. J. Med. Chem. 1975, 18: 1272-1274.
[54] Kier, L. B.; Hall, L. H. Molecular Connectivity in Chemistry and Drug Research[M]. Academic Press, New York, 1976: 1-10.
[55] Hall, L. H.; Kier, L. B.; Murray, W. J. Molecular Connectivity 2: Relationship to Water SolubilityAnd Boiling Points[J]. J. Pharm. Sci. 1975, 64: 1974-1977.
[56] Murray, W. J.; Hall, L. H.; Kier, L. B. Molecular Connectivity 3: Partition Coefficients[J]. J. Pharm. Sci. 1975, 64: 1978-1981.
[57] David, A. W.; Frank, R. B. Holographic QSAR of Benzodiazepines[J]. Quant. Struct. Act. Relat.1998, 17: 224-231.
[58] Rodrigues, C. R.; Flaherty, T. M.; Springer, C.; McKerrow, J. H.; Cohen, F. E. CoMFA and HQSAR of Acylhydrazide Cruzain Inhibitors[J]. Bioorg. Med. Chem. Lett. 2002, 12: 1537-1541.
[59] Pungpo, P.; Hannongbua, S.; Wolschann, P. Hologram Quantitative Structure-Activity Relationships Investigations of Non-Nucleoside Reverse Transcriptase Inhibitors[J]. Curr. Med.Chem. 2003, 10: 1661-1677.
[60] Vrtacnik, M.; Voda, K. HQSAR and CoMFA Approaches in Predicting Reactivity of Halogenated Compounds with Hydroxyl Radicals[J]. Chemosphere, 2003, 52: 1689-1699.
[61] Cunningham, S. L.; Cunningham, A. R.; Day, B. W. CoMFA, HQSAR and MolecularDocking Studies of Butitaxel AnaloguesWith Beta-Tubulin[J]. J. Mol. Model. 2005, 11: 48-54.
[62] Honorio, K. M.; Garratt, R. C.; Andricopulo, A. D. Hologram Quantitative Structure-Activity Relationships for a Series of Farnesoid X Receptor Activators[J]. Bioorg. Med. Chem. Lett. 2005, 15: 3119-3125.
[63]崔世海,刘树深,王晓栋,王连生.雌二醇衍生物的HQSAR研究[J].科学通报, 2003, 48: 239-242.
[64]戴玄吏,王晓栋,黄宏,陈达,王连生.应用分子全息QSAR技术预测硝基芳烃的遗传毒性[J].环境科学学报, 2003, 23: 674-678.
[65]张兵,邹建卫,郑柯文,刘海春,曾敏,俞庆森.基于5-芳基乙内酰脲类化合物的CoMFA和HQSAR研究[J].物理化学学报, 2004, 20: 1204-1210.
[66]李华,张华北. 3-吡啶基醚类化合物的分子全息QSAR研究[J].化学学报, 2005, 63: 1018-1022.
[67]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[M].上海:上海科学技术出版社, 2000: 141-149.
[68] Klebe G, Abraham U, Mietzner T. Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity[J]. J. Med. Chem., 1994, 37 (24): 4130-4146.
[69] Viswanadban V N, Ghose A K, Revankar G R, Robins R K. Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics[J]. J. Chem. Inf. Comput. Sci., 1989, 29: 163-172.
[70] Klebe G. Pseudoreceptor Modeling: The Construction of Three-DimensionalReceptor Surrogates[J]. J. Mol. Biol., 1994, 237: 212-235.
[71] Doweyko A M. Pharmacophores from Binding Data. Pharmacophores from Binding Data[J]. J. Med. Chem., 1994, 37: 1769-1778.
[72] Woolfrey J R, Avery M A, Doweyko A. Comparison of 3D Quantitative Structure-Activity Relationship Methods: Analysis of the In Vitro Antimalarial Activity of 154 Artemisinin Analogues by Hypothetical Active Site Lattice and Comparitive Molecular Field Analysis[J]. J. Comput.-Aided Mol. Des., 1998, 12: 165-181.
[73] Todeschini R, Lasagni M, Marengo E. New Molecular Descriptors for 2D and 3D Structures-Theory[J]. J. Chemom., 1994, 8: 263-272.
[74] Todeschini R, Gramatica P, Marengo E, Provenzani R. Modeling and Prediction by UsingWHIM Descriptors in QSAR Studies: Submitochondrial Particles (SMP) as Toxicity Biosensors of Chlorophenols[J]. Chemosphere, 1995, 33: 71-79.
[75] 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[J]. Chemosphere, 1996, 32: 1527-1545.
[76] Todeschini R, Gramatice P. 3D-modelling and prediction by WHIM descriptors. Part 6. Applications of the WHIM descriptors in QSAR studies[J]. Quant. Struct.-Act. Relat., 1997, 16: 120-125.
[77] Todeschini R, Moro G, Boggia R, Bonati L, Cosentino U, Lasagni M, Pitea D. Modeling and prediction of molecular properties. Theory of grid-weighted holistic invariant molecular (G-WHIM) descriptors[J]. Chemom. Intell. Lab. Syst., 1997, 36: 65-73.
[78] Ginn C M R, Turner D B, Willett P, Ferguson A M, Heritage T W. Similarity Searching in Files of Three-Dimensional Chemical Structures: Evaluation of the EVA Descriptor and Combination of Rankings Using Data Fusion[J]. J. Chem. Inf. Comput. Sci., 1997, 37: 23-27.
[79] Cartier A, Rivail J L. Electronic Descriptors in Quantitative Structure-Activity Relationships[J]. Chemom. Intell. Lab. Sys., 1987, 1: 335-347.
[80] Karelson M, Lobanov V S. Quantum-Chemical Descriptors in QSAR/QSPR Studies[J]. Chem. Rev., 1996, 96: 1027-1043.
[81] Gupta S P, Singh P, Bindal M C. QSAR Studies on Hallucinogens[J]. Chem. Rev., 1983, 83(6): 633-649.
[82] Gupta S P. QSAR studies on Enzyme Inhibitors[J], Chem. Rev., 1987, 87(5): 1183-1253.
[83] Gupta S P. QSAR (quantitative structure-activity relationship) Studies on Local Anesthetics[J]. Chem. Rev., 1991, 91(6): 1109-1119.
[84] Cocchi M, Menziani M C, De B P G, Cruciani G. Theoretical Versus Empirical Molecular Descriptors in Monosubstituted Benzenes: A Chemometric Study[J]. Chemom. Intell. Lab. Sys., 1992, 14: 209-224.
[85] Clare B W, Supuran C T. Carbonic Anhydrase Activators 3: Structure-Activity Correlations for a Series of IsoenzymeⅡActivators[J]. J. Pharm. Sci., 1994, 83: 768-773.
[86] Doichinova I A, Natcheva R N, Mihailova D N. QSAR Studies of 8-Substituted Xanthines as Adenosine Receptor Antagonists[J]. Eur. J. Med. Chem., 1994, 133-138.
[87] Bodor N, Gabanyi Z, Wong C K. A New Method for the Estimation of Partition Coefficient[J]. J. Am. Chem. Soc., 1989, 111(11): 3783-3786.
[88] Buydens L, Massart D L, Geerlings P. Prediction of Gas Chromatographic RetentionIndexes with Topological, Physicochemical, and Quantum Chemical Parameters[J]. Anal. Chem., 1983, 55(4): 738-744.
[89] Klopman G, Iroff L D. Calculation of Partition Coefficients by the Charge Density Method[J]. J. Comput. Chem., 1981, 2: 157-160.
[90] Nakayama A, Hagiwara K, Hashimoto S, Shimoda S. QSAR of Fungicidal 1,2,4-Thiadiazo lines. Reactivity-Activity Correlation of Sulfhydryl in Hibitors[J].Quant. Struct.-Act. Relat., 1993, 12: 251-255.
[91] Cammarata A. An Apparent Correlation between the in Vitro Activity of Chloramphenicol Analogs and Electronic Polarizability[J]. J. Med. Chem., 1967, 10(4): 525-527.
[92] Lewis D F V, Ioannides C, Parke D V. Interaction of a Series of Nitriles with the Alcohol-Indicible Indicible Isoform of P450: Computer Analysis of Structure-Activity Relationship[J]. Xenobiotica, 1994, 24: 401-408.
[93] Gaudio A C, Korolkovas A, Takahata Y. Quantitative Structure-Activity Relationships for 1,4–Dihydropyridine Calcium Channel Antagonists (Nifedipine Analogs): A Quantum Chemical/Classical Approach[J]. J. Pharm. Sci., 1994, 83: 1110-1115.
[94] Grunenberg J, Herges R. Prediction of Chromatographic Retention Values (RM) and Partition Coefficients (logPoct) Using a Combination of Semiempirical Self-Consistent Reaction Field Calculations and Neural Networks[J]. J. Chem. Inf. Comput. Sci., 1995, 35(5): 905-911.
[95] Klopman G, Iroff L D. Calculation of Partition Coefficients by the Charge Density Method[J]. J. Comput. Chem., 1981, 2: 157-160.
[96] Bodor N, Gabanyi Z, Wong C K. A New Method for the Estimation of Partition Coefficient[J]. J. Am. Chem. Soc., 1989, 111(11): 3783-3786.
[97] Buydens L, Massart D L, Geerlings P. Prediction of Gas Chromatographic Retention Indexes with Topological, Physicochemical, and Quantum Chemical Parameters[J]. Anal. Chem., 1983, 55(4): 738-744.
[98] Osmialowski K, Halkiewicz J, Radecki A, Kaliszan R. Quantum Chemical Parameters in Correlation Analysis of Gas-Liquid Chromatographic Retention Indices of Amines[J]. J. Chromatogr., 1985, 346: 53-60.
[99] Osmialowski K, Halkiewicz J, Kaliszan R. Quantum chemical parameters in correlation analysis of gas-liquid chromatographic retention indices of amines:Ⅱ.Topological electronic index[J]. J. Chromatogr., 1986, 361: 63-69.
[100] Sotomatsu T, Murata Y, Fujita T. Correlation Analysis of Substituent Effects on the Acidity of Benzoic Acids by the AM1 Method[J]. J. Comput. Chem., 1989, 10: 94-98.
[101] Harding L B, Schlegel H B, Krishnan R, Pople J A. Moeller-Plesset Study of the H4CO Potential Energy Surface[J]. J. Phys. Chem., 1980, 84(25): 3394-3401.
[102] Bartlett R J. Coupled-Cluster Approach to Molecular Structure and Spectra: A Step toward Predictive Quantum Chemistry[J]. J. Phys. Chem., 1989, 93(5): 1697-1708.
[103] Detrich J L, Konecny R, Vetter W M, Doren D, Rheingold A L, Theopold K H. Structural Distortion of the TpCo-L Fragment (Tp = tris(pyrazolyl)borate). Analysisby X-ray Diffraction and Density Functional Theory[J]. J. Am. Chem. Soc.; 1996,118(7): 1703-1712.
[104] Dewar M J S, Thiel W. Ground States of Molecules. 38. The MNDO Method. Approximations and Parameters[J]. J. Am. Chem. Soc., 1977, 99(15): 4899-4907.
[105] Dewar M J S, Zoebisch E G, Healy E F, Stewart J J P. Development and Use of Quantum Mechanical Molecular Models. 76. AM1: a New General Purpose Quantum Mechanical Molecular Model[J]. J. Am. Chem. Soc., 1985, 107(13): 3902-3909.
[106] Howarth N M, Purohit A, Reed M J, Potter B V L. Estrone sulfamates: potent inhibitors of estrone sulfatase with therapeutic potential[J]. J. Med. Chem., 1994, 37(2): 219-221.
[107] Hasegawa K, Matsuoka S, Arakawa M, Funatsu K. New Molecular Surface-Based 3D-QSAR Method Using Kohonen Neural Network and 3-Way PLS[J]. J. Comput. Chem., 2002, 26: 583-589.
[108] Polanski J, Gieleciak R, Bak A. The Comparative Molecular Surface Analysis (COMSA) - A Nongrid 3D QSAR Method by a Coupled Neural Network and PLS System: Predicting pKa Values of Benzoic and Alkanoic Acids[J]. J. Chem. Inf. Comput. Sci., 2002, 42: 184-191.
[109] 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]. J. Am. Chem. Soc., 1997, 119: 10509-10524.
[110] Albuquerque M G, Hopfinger A J, Barreiro E J, de Alencastro, R B. Four-Dimensional Quantitative Structure-Activity Relationship Analysis of a Series of Interphenylene 7-Oxabicycloheptane Oxazole Thromboxane A2 Receptor Antagonists[J]. J. Chem. Inf. Comput. Sci., 1998, 38: 925-938.
[111] Santos-Filhoa O A, Hopfingerb A J. The 4D-QSAR Paradigm: Application to a Novel Set of Nonpeptidic HIV Protease Inhibitors[J]. Quant. Struct.-Act. Relat., 2002, 21: 369-381.
[112] Hopfinger A J, Reaka A, Venkatarangan P, Duca J S, Wang S. Construction of a Virtual High Throughput Screen by 4D-QSAR Analysis: Application to a Combinatorial Library of Glucose Inhibitors of Glycogen Phosphorylase b[J]. J. Chem. Inf. Comput. Sci., 1999, 39: 1151-1160.
[113] Vedani A, Dobler M. Multidimensional QSAR: Moving from three- to five dimensionalconcepts[J]. Quant.struct.-Act.Relat., 2002, 21: 382-390.
[114]许禄,邵学广.化学计量学方法(第二版)[M].北京:科学出版社. 2004: 163-176.
[115] [德]加斯泰格尔,恩格尔著,梁逸曾,徐峻,姚建华译.化学信息学教程[M].北京:化学工业出版社. 2005: 375-398.
[116] Wold, S.; Sj?str?m, M.; Eriksson, L. PLS-regression: A Basic Tool of Chemometrics[J]. Chemom. Intell. Lab. Syst. 2001, 58: 109-130.
[117] Hansch C, Fujita T. p-σ-πAnalysis. A Method for the Correlation of Biological Activity and Chemical Structure[J]. J. Am. Chem. Soc. 1964, 86 (8): 1616-1626.
[118]李仁利.定量构效关系研究的现况与展望[J].国外医学药学分册. 1992, 19(6):321-326.
[119] Ekaterina Paliakov, Maged Henary, Martial Say, Steven E Patterson, Alesia Parker,Lori Manzel, Donald E Macfarlane, Andrzej J Bojarski, Lucjan Strekowski. Fujita-Ban QSAR analysis and CoMFA study of quinoline antagonists of immunostimulatory CpG-oligodeoxy-nucleotides[J]. Bioorg. Med. Chem., 2007, 15(1):324-332.
[120]俞汝勤.化学计量学导论[M].长沙:湖南教育出版社, 1991: 1-180.
[121]许禄,邵学广.化学计量学方法(第二版)[M].北京:科学出版社. 2004: 238-293.
[122] Holland, J. H. Adaptation in natural and artificial systems[M]. Ann Arbor: University of Michigan Press, 1975: 156-159.
[123]徐筱杰,侯廷军,乔学斌,章威.计算机辅助药物分子设计[M].北京:化学工业出版社.2004: 77-97.
[124] Leonard J T, Roy K. On selection of training and test sets for the development of predictive QSAR models. [J] . QSAR Comb. Sci., 2006, 25: 235-251.
[125] S?antai-Kis C, K?vesdi I, Kéri G, ?rfi L. Validation subset selections for extrapolation oriented QSPAR models[J]. Molecular Diversity, 2003, 7: 37-43.
[126] Gramatica P, Pilutti P, Papa E. Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus Modeling[J]. J. Chem. Inf. Comput. Sci., 2004, 44: 1794-1802.
[127] Golbraikh A, Tropsha A. Beware of q2! [J]. J. Mol. Graphics Mod., 2002, 20: 269-276.
[128] Tropsha A, Gramatica P, Gombar V K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models[J]. QSAR Comb. Sci., 2003, 22: 69-77.
[129] Wold, S. Validation of QSARs[J]. Quant. Struct. Act. Relat. 1991, 10: 191–193.
[130] Clark, M.; Cramer III, R. D. The Probability of Chance Correlation Using Partial Least Squares (PLS) [J]. Quant. Struct. Act. Relat. 1993, 12: 137-145.
[131] Walker, J. D.; Jaworska, J.; Comber, M. H. I.; Schultz, T. W.; Dearden, J. C. Guidelines forDeveloping and Using Quantitative Structure-Activity Relationships[J]. Environ. Toxicol. Chem. 2003, 22: 1653-1665.
[132] Golbraikh, A.; Tropsha, A. Beware of q2! [J]. J. Mol. Graph. Model. 2002, 20: 269-276.
[133] Tropsha, A.; Gramatica, P.; Gombar, V. K. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Inerpretation of QSPR Models[J]. QSAR Comb. Sci. 2003, 22: 69-77.
[134] Golbraikh, A.; Shen, M.; Xiao, Z.; Xiao, Y. D.; Lee, K. H.; Tropsha, A. ational Selection of Training and Test Sets for the Development of Validated QSAR Models[J]. J. Comput. Aided Mol. Des. 2003, 17: 241-253.
[135] Winer, H. Structural determination of paraffin boiling point[J]. J. Am. Chem. Soc. 1947, 69: 2636-2641.
[136] Randic, M. On characterization of molecular Branching[J]. J. Am. Chem. Soc. 1975, 97: 6609-6615.
[137] Hosoya, H. Topological index. A new proposed quantity characterizing the topological nature of structural isomers of saturated hydrocarbons[J]. Bull. Chem. Soc. 1971, 44: 2332-2339.
[138] Balaban, A. T. High discrimination distance-based topological index[J]. Chem. Phys. Lett. 1982, 89: 399-404.
[139] Kier, L. B.; Hall, L. H. An electrotopological state index for atoms in molecules[J]. Pharm. Res. 1990, 7: 801-807.
[140] Wild, D. J.; Blankley, C. J. Comparison of 2D fingerprint types and hierarchy level selection methods for structural groupingusing Ward’s clustering[J]. J. Chem. Inf. Comput.Sci. 2000, 40: 155-162.
[141] Flower, D. R. On the properties of bit string-based measures of chemical similarity[J]. J.Chem. Inf. Comput. Sci. 1998, 38: 379-386.
[142] Xue, L.; Godden, J. W.; Bajorath, J. Database searching for compounds with similar biological activities using short binary bit string representations of molecules[J]. J. Chem. Inf.Comput. Sci. 1999, 39: 881-886.
[143] Mcgregor, M. J.; Pallai, P. V. Clustering of large databases of compounds: Using MDL“Keys”as structural descriptors[J]. J. Chem. Inf. Comput. Sci. 1997, 37: 1-9.
[144] Silverman, B. D.; Platt, D. E. Comparative molecular moment analysis (CoMMA): 3D-QSAR without molecular superposition[J]. J. Med. Chem. 1996, 39: 2129-2140.
[145] Goodford, P. J. A computational precedure for determining energetically favorable binding sites on biologically important molecules[J]. J. Med. Chem. 1985, 28: 849-857.
[146] Goodford, P. J. Multivariate characterization of molecules for QSAR analysis[J]. J. Chemom. 1996, 10: 107-117.
[147] Silverman, B. D.; Platt, D. E.; Pitman, M. C.; Rigotsos, I. Comparative molecular moment analysis[J]. Perspect. Drug Discov. Des. 1998, 12/13/14: 183-196.
[148] Ghuloum, A. M.; Sage, C. R.; Jain, A. N. Molecular Hashkeys: A novel method for molecular characterization and its application forpredicting important pharmaceutical properties of molecules[J]. J. Med. Chem. 1999, 42: 1739-1748.
[149] Todeschini, R.; Lasagni, M.; Marengo, E. New molecular descriptors for 2D and 3D structures. Theory[J]. J. Chemom. 1994, 8: 263-272.
[150] Wiener, H. Correlation of heats of isomerization and differences in heats of vaporiza- tion of isomers, among the paraffin hydrocarbons[J]. J. Phys. Chem. 1948, 52: 2636? 2638.
[151] Kier, L. B.; Murray, W. J.; Hall, L. H. Molecular connectivity 4: Relationship to biologicalactivity[J]. J. Med. Chem. 1975, 18: 1272-1274.
[152] Hall, L. H.; Kier, L. B. The relation of molecular connectivity to molecular volume and biological activity[J]. Eur. J. Med. Chem. 1981, 16: 399-407.
[153] Nirmalakhandan, N. N.; Speece, R. E. Structure-activity relationships. Quantitative techniques for predicting the behavior of chemicals in the ecosystem[J]. Environ. Sci. Technol. 1988, 22: 606-615.
[154] McHughes, M. C.; Poshusta, R. D. Graph-theoretical cluster expansion. Thermo-chemical properties for alkanes[J]. J. Math. Chem. 1990, 4: 227-249.
[155] Randic, M. Novel molecular description for structure-property studies[J]. Chem. Phys. Lett.1993, 211: 478-483.
[156] Cash, G. G.; Breen, J. J. Correlation of graph-theoretical parameters with biological activity[J]. J. Chem. Inf. Comput. Sci. 1993, 33: 275-279.
[157] Wessel, M. D.; Jurs, P. C. Prediction of normal boiling points for a diverse set of industrially important organic compounds from molecular structure[J]. J. Chem. Inf. Comput.Sci. 1995, 35: 841-850.
[158] Kier, L. B.; Hall, L. H. An electrotopological state index for atoms in molecules[J]. Pharm.Res. 1990, 7: 801-807.
[159] Hall, L. H.; Kier, L. B. The electrotopological state: Structure information at the atomic level for molecular graph[J]. J. Chem. Inf. Comput. Sci. 1991, 31: 76-83.
[160] Kier, L. B.; Hall, L. H. An index of electrotopological state for atoms in molecules[J]. J.Math. Chem. 1991, 7: 229-241.
[161] Kier, L. B.; Hall, L. H. Inhibition of salicylamide binding: a electrotopological stateanalysis[J]. Med. Chem. Res. 1992, 2: 497-502.
[162] Hall, L. H.; Mohney, B.; Kier, L. B. Comparison of electrotopological state indexes with molecular orbital parameters: inhibition of MAO by hydrazides[J]. Quant. Struct. -Act. Relat. 1993, 12: 44-48.
[163] AbouShaaban, R. R. A.; AlKhamees, H. A.; AbouAuda, H. S. et al. Atom level electrotopological state indexes in QSAR: Designing and testing antithyroid agents[J]. Pharmaceut. Res. 1996, 13(1): 129-136.
[164] Wild, D. J.; Blankley, C. J. Comparison of 2D fingerprint types and hierarchy level selection methods for structural groupingusing Ward’s clustering[J]. J. Chem. Inf. Comput.Sci. 2000, 40: 155-162.
[165] Ferguson, A. M.; Heritage, T.; Jonathon, P.; et al. EVA: a new theoritically based molecular descriptor for use in QSAR/QSPR analysis[J]. J. Comput. -Aided Mol. Des. 1997,11: 143-147.
[166] Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity[J]. J.Med. Chem. 1994, 37: 4130-4146.
[167] Todeschini, R.; Gramatice, P.; Provenzani, R. Weighted holistic invariant molecular descriptors. Part 2. Theory devolopment and applications on modeling physicochemical properties of polycyclic aromatic hydrocarbons[J]. Chemom. Intell. Lab. Syst. 1995, 27: 221-229
[168] 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]. J. Comput. -Aided Mol. Des. 1997, 11: 79-92.
[169] Gordon, M.; Crippen, G. M. Distance geometry approach to rationalizing binding data[J]. J.Med. Chem. 1979, 22(8): 988-997.
[170] Gordon, M.; Crippen, G. M. Quantitative structure-activity relationship by distance geometry-systematic analysis of dibydrofolate-re-ductase inhibitors[J]. J. Med. Chem. 1980, 23(6): 599-606.
[171] Gordon, M.; Crippen, G. M. Quantitative structure-activity relationship by distance geometry: thyroxine binding-site[J]. J. Med. Chem. 1981, 24(2): 198-203.
[172] Crippen, G. M. Distance geometry analysis of the benzodiazepine binding-site[J]. Mol. Pharmacol. 1982, 22: 11-19.
[173] Crippen, G. M. Voronoi binding site models[J]. J. Comput. Chem. 1987, 8: 43-49.
[174] Hopfinger, A. J. A QSAR investigation of dihydrofolate-reductase inhibition by bakertriazines based upon molecular shape analysis[J]. J. Am. Chem. Soc. 1980, 102(24): 7196-7206.
[175] Raevsky, O. A.; Sapegin, A.; Zefirov, N. The QSAR discriminantregression model[J]. Quant.Struct.-Act. Relat. 1994, 13: 412-418.
[176] Raevsky, O. A. Quantification of noncovalent interactions on the basis of the thermodynamic hydrogen bond parameters[J]. J. Phys. Org. Chem. 1997, 10: 405-413.
[177] Jain, A. N.; Koil, K.; Chapman, D. COMPASS-prediction biological-activities from molecular-surface properties-performance comparisons on a steroid benchmark[J]. J. Med.Chem. 1994, 37(15): 2315-2327.
[178] Jain, A. N.; Dietterich, T. G.; Lathrop, R. H.; Chapman, D.; Critchlow, R. E.; Webster, T. A. Lozaoperez, T. COMPASS-a shape-based machine learning tool for drug design[J]. J.Comput. -Aided Mol. Des. 1994, 8: 635-652.
[179] Walters, D. E.; Hinds, R. M. Genetically evolved receptor models-a computational approach to construction of receptor models[J]. J. Med. Chem. 1994, 37(16): 2527-2536.
[180] Liu, S.; Yin, C.; Cai, S.; Li, Z. A novel MHDV descriptor for dipeptide QSAR studies[J]. J.Chin. Chem. Soc. 2001, 48: 253-260.
[181] Liu, S.; Cao, C.; Li, Z. Approach to estimation and prediction for normal boiling point (NBP) of alkanes based on a novel Molecular Distance Edge (MDE) vectorλ[J]. J. Chem. Inf. Comput. Sci. 1998, 38: 387-394.
[182] Liu, S.; Cai, S.; Cao, C.; Li, Z. Molecular Electronegative Distance Vector (MEDV) relating to 15 properties of alkanes[J]. J. Chem. Inf. Comput. Sci. 2000, 40: 1337-1348.
[183] Liu, S.; Yin, C.; Cai, S.; Li, Z. QSAR study of steroid benchmark and dipeptides based on MEDV-13[J]. J. Chem. Inf. Comput. Sci. 2001, 41: 321-329.
[184]杨善彬,周鹏,田菲菲,李志良.一种基于三维原子场相互作用矢量的新型结构描述子[C],中国科协2005年学术年会论文集(上).北京:中国科学与技术出版社,133-139,2005.
[185] Levitt, M.; Perutz, M. F.; Aromatic Rings Act as Hydrogen Bond Acceptors[J]. J. Mol. Biol. 1988, 201: 751-754.
[186] Hahn, M.; Receptor Surface Models. 1. Definition and Construction[J]. J. Med. Chem. 1995, 38: 2080-2090.
[187] Kellogg, G. E.; Semus, S. F.; Abraham, D. J. HINT-a New Method of Empirical Hydrophobic Field Calculation for CoMFA[J]. J. Comput. Aided Mol. Des. 1991, 5: 545-552.
[188] Wireko, F. C.; Kellogg, G. E.; Abraham, D. J. Allosteric Modifiers of Hemoglobin. 2. Crystallographically Determined Binding Sites and Hydrophobic Binding/InteractionAnalysis of Novel Hemoglobin Oxygen Effectors[J]. J. Med. Chem. 1991, 34: 758-767.
[189] Kellogg, G. E.; Joshi, G. S.; Abraham, D. J. New Tools for Modeling and Understanding Hydrophobicity and Hydrophobic Interactions[J]. Med. Chem. Res. 1992, 1: 444-453.
[190] Kellogg, G. E.; Abraham, D. J. Key, Lock, and Locksmith. Complementary Hydrophobicity Map Predictions of Drug Structure from a Known Receptor/Receptor Structure from Known Drugs[J]. J. Mol. Graph. 1992, 10: 212-217.
[191] Nayak, V. R.; Kellogg, G. E. Cyclodextrin-Barbiturate Inclusion Complexes: a CoMFA/HINT 3-D QSAR Study[J]. Med. Chem. Res. 1994, 3: 491-502.
[192] Hasel, W.; Hendrikson, T. F.; Still, W. C. A Rapid Approximation to the Solvent Accessible Surface Areas of Atoms[J]. Tetrahed. Comp. Method. 1988, 1: 103-116.
[193] Pei, J.; Wang, Q.; Zhou, J.; Lai, L. Estimating Protein-Ligand Binding Free Energy: Atomic Solvation Parameters for Partition Coefficient and Solvation Free Energy Calculation[J]. Proteins, 2004, 57: 651-664.
[194] Chu ,D.T.W.; Fernandes , P.B. In: Testa B.(Ed), Recent Developments in the Field of Quinolone Antibacterial Agents[J] in Advances in Drug Research, Academic Press San Diego, 1991, 21: 39-144.
[195] Hooper, D.C. Mode of action of fluoroquinolones[J] Drugs.1999, 58(2): 6-10.
[196] Berger,J.M.;Gamblin,S.J.;Harrison,S.C.;Wang, J.C.Structure and mechanism of DNA topoisomeraseⅡ[J]. Nature , 1996, 379: 225-232.
[197] Malonne,H.;Atassi,G. DNA topoisomerase targeting drugs:Mechanism of action and perspectives[J] Anticancer Drug, 1997, 8: 811-822.
[198] Tomita, K.; Tsuzuki,Y.; Shibamori,K.; Tashima,M.; Kajikawa,F.; Sato,Y.; Kashimoto, S.; Chiba, K.; Hino,K. Synthesis and Structure-Activity Relationships of Novel 7-Substituted 1,4-Dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridine-3-carboxylic Acids as Antitumor Agents. Part 1 [J] . J Med. Chem. 2002, 45(25): 5564-5575.
[199] Tsuzuki,Y.; Tomita, K.; Shibamori, K.; Sato, Y.; Kashimoto, S.; Chiba,K. Synthesis and Structure-Activity Relationships of Novel 7-Substituted 1,4-Dihydro-4-oxo-1-(2-thiazolyl) -1,8-naphthyridine-3-carboxylic Acids as Antitumor Agents. Part 2 [J]. J.Med. Chem. 2004,47(8): 2097-2109.
[200] Tsuzuki, Y.; Tomita, K.; Sato, Y.; Shibamori, K.; Kashimoto, S.; Chiba, K Synthesis and structure–activity relationships of 3-substituted 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8 -naphthyridines as novel antitumor agents [J] Bioorg. Med. Chem. Lett. 2004,14(12): 3189-3193.
[201] Atanasova,M.; Ilieva,S.; Galabov,B.QSAR analysis of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines with anticancer activity[J]. European J. Med. Chem. 2007, 42: 1184-1192.
[202] Rouhi, A. Maureen. Tuberculosis: A Tough Adversary[J]. Chem. Eng.News.,1999,52-69.
[203] Chambhare, R.V.; Khadse, B.G.; Bobde, A.S.; Bahekar,R. H.Synthesis and preliminary evaluation of some N-5-(2-furanyl)-2-methyl-4-oxo-4H-thieno[2,3-d]pyrimidin-3-yl] -carboxamide and 3- substituted-5-(2-furanyl)-2- methyl-3H-thieno[2,3-d]pyrimidin-4-ones as antimicrobial agents[J]. Eur.J. Med.Chem. 2003, 38: 89.
[204]粱桂兆,梅虎,周原,计算机辅助药物设计中的多维定量构效关系模型化方法[J].化学进展, 2006, 18(1): l20-l27.
[205]朱杰,盛春泉,张珉,宋云龙,陈军,余建鑫,姚建忠,缪震元,张万年.苯并呋喃类N-肉豆蔻酰基转移酶抑制剂的三维定量构效关系研究[J],高等学校化学学报, 2006, 27(2): 287-29l.
[206]周鹏,梅虎,田菲菲,李志良,新型三维原子场全息相互作用矢量用于几类典型药物体系的QSAR研究[J],高等学校化学学报, 2007, 28(6): 1027-1030.
[207]孔德信,江涛,阎作伟,管华诗.使用本征值方法研究抗氧化剂的构效关系[J],高等学校化学学报, 2004, 25(4): 713-716.
[208]刘树深,刘堰,李志良,蔡绍皙.一个新的分子电性距离矢量(MEDV)[J].化学学报, 2000, 58(11): 1353-1357
[209] Balasubramanian Narasimhan, Meena Kumari,Nitin Jain,Avinash Dhake and Chandrasekaran Sundaravelan. Correlation of antibacterial activity of some N-[5-(2-furanyl)-2-methyl-4-oxo-4H-thieno[2,3-d]pyrimidin-3-yl]- carboxamide and 3- substituted-5-(2-furanyl)-2-methyl-3H-thieno[2,3-d]pyrimidin-4-ones with topological indices using Hansch analysis[J]. Bioorg. Med. Chem. Lett. 2006, 16: 4951-4958.
[210] Yoshihara, H.A.I.; Apriletti, J.W.; Baxter, J.D.et al. Structural Determinants of Selective Thyromimetics[J], J. Med. Chem.2003, 46: 3152-3161.
[211] Chiellini, G.; Nguyen,N.H.; Apriletti,J.W.et al. Synthesis and biological activity of novel thyroid hormone analogues: 5'-aryl substituted GC-1 derivatives.[J]. Bioorg. Med. Chem. 2002,10: 333-346.
[212] Borngraeber, S.; Budny, M.J.; Chiellini, G . et al. Ligand selectivity by seeking hydrophobicity in thyroid hormone receptor[J]. Proc. Natl. Acad. Sci. U. S. A. 2003,100: 15358-15363.
[213] Ye, L.;Li, Y.L.;Mellstrom, K. et al. Thyroid receptor ligands. 1. Agonist ligands selective for the thyroid receptor beta1[J]. J.Med.Chem. 2003,46: 1580-1588.
[214] Hangeland, J.J.; Doweyko, A.M.; Dejneka, T.et al. Thyroid receptor ligands. Part 2: Thyromimetics with improved selectivity for the thyroid hormone receptor beta.[J]. Bioorg.Med. Chem. Lett. 2004,14: 3549-3554.
[215] Hedfors,A.; Appelqvist, T.; Carlsson, B.et al. Thyroid receptor ligands. 3. Design and synthesis of 3,5-dihalo-4-alkoxyphenylalkanoic acids as indirect antagonists of the thyroid hormone receptor[J]. J. Med. Chem. 2005, 48: 3114.
[216] Li,Y.L.; Litten,C.; Koehler,K.F.et al. Thyroid receptor ligands. Part 4: 4'-amido bioisosteric ligands selective for the thyroid hormone receptor beta[J], Bioorg. Med. Chem.Lett., 2006, 16: 884-886.
[217] Hangeland, J.J.; Doweyko, A.M. Dejneka, T. Friends,T.J. Devasthale, P. Mellstr?m,M. Sandberg, Grynfarb,J. M.; Sack, J.S.; Einspahr,H.; F?rneg?rdh, M.; Husman, B.; Ljunggren, J.; Koehler, K.; Sheppard,C.; Malm,J.; and Ryono,D.E. Thyroid receptor ligands. Part 2: Thyromimetics with improved selectivity for the thyroid hormone receptor beta.[J]. Bioorg. Med. Chem. Lett. 2004,14: 3549-3554.
[218] Hedfors,A.; Appelqvist, T.; Carlsson, B.; Bladh, L.-B.; Litten, C.; Agback, P.; Grynfarb, M.; Koehler, K. F.;Malm, J. Thyroid receptor ligands. 3. Design and synthesis of 3,5-dihalo-4-alkoxyphenylalkanoic acids as indirect antagonists of the thyroid hormone receptor[J]. J. Med. Chem. 2005, 48: 3114.
[219] Li,Y.L.; Litten,C.; Koehler,K.F.; Mellstrom,K.; Garg,N,;Garcia Collazo,A.M.; Farnegard,M.; Grynfarb,M.;Husman,B.; Sandberg,J.; Malm,J. Thyroid receptor ligands. Part 4: 4'-amido bioisosteric ligands selective for the thyroid hormone receptor beta[J], Bioorg. Med. Chem.Lett., 2006, 16: 884-886.
[220] Garcia Collazo,A.M.; Koehler,K.F.; Garg,N.;Farnegardh,M.; Husman,B.; Ye,L.; Ljunggren,J.;Mellstrom,K.; Sandberg,J.; Grynfarb,M.; Ahola,H.;Malm,J.Thyroid receptor ligands. Part 5: novel bicyclic agonist ligands selective for the thyroid hormone receptor beta[J]. J. Bioorg. Med. Chem. Lett. 2006, 16: 1240.
[221] Valadares,N.F.; Castilho,M.S.; Polikarpov, I.; Garratt,R.C. 2D QSAR studies on Thyroid Hormone Receptor Ligands[J], Bioorg. Med. Chem.,2007,doi: 10.1016/j.bmc.2007.04.015.
[222] Oxfoxd J S, Novelli P. New millennium antivirals against pandemic and epidemic influenza: the neuraminidase inhibitors[J]. Antivir Chem Chemother, 2002, 13(4): 205-217.
[223] Colman PM, Varghese JN, Laver WG. Structure of the catalyticand antigenic sites in influenza virus euraminidase[J],Nature, 1983, 303 (5912): 41-44.
[224] McNicholl IR, McNicholl JJ. Neuraminidase inhibitors:zanamivir and oseltamivir[J]. Ann Pharmacother, 2001, 35(1): 57-70.
[225]龚祖埙.病毒的分子生物学及防治策略[M],上海:上海科学技术出版社,2005: 267-297.
[226]周正任,潘兴瑜.病原生物学[M].北京:科学出版社, 2001: 180-185.
[227]龚晓明,许激扬,丁玉林.抗流感病毒新药——神经氨酸酶抑制剂[J].药学进展, 2002, 26(5): 266-270.
[228] Matrosovich M, Klenk HD. Natural and synthetic sialic acidcontaining inhibitors of influenza virus receptor binding[J]. Rev Med Virol, 2003, 13(2): 85-97.
[229] Smith B, Colman P, von Itzstein M.Analysis of inhibitor binding in influenza virus neuraminidase [J]. Protein Sci, 2001, 10(4): 689-696.
[230] Andrews DM, Cherry PC, Humber DC, Synthesis and influenza virus sialidase inhibitory activity of analogues of 4-guanidino-Neu5Ac2en (Zanamivir) modified in the glycerolside- chain[J]. Eur J Med Chem, 1999, 34(7-8): 563-574.
[231] Wilson JC, von Itzstein M. Recent strategies in the search for new anti-influenza therapies[J]. Curr Drug Targets, 2003, 4(5): 389-408.
[232] von Itzstein M, Wu W Y, Kok GB , et al. Rational design of potentsialidase-based inhibitors of influenza virus replication,[J] Nature , 1993 , 363 (6428): 418-423.
[233] Singh S , Jedrzejas M J , Air GM, et al. Structure-based inhibitors of influenza virus sialidase. A benzoic acid lead withnovel interaction [J]. J Med Chem , 995 ,38: 3217-3225.
[234] Chand P , Babu Y S , Bantia S , et al. Design and synthesis ofbenzoic acid derivatives as influenza neuraminidase inhibitorsusing structure-based drug design [J ] . J Med Chem , 1997 ,40: 4030-4052.
[235] Kim C U , Lew W, WilliamsMA , et al. Influenza neuraminidase inhibitors prossessing a novel hydrophobic interaction inthe enzyme active site :design , synthesis , and structural analysis of carbocyclic sialic acid analogues with potent anti-influenza activity [J]. J Am Chem Soc , 1997 , 119(4): 6812-6901
[236] Kim C U , Lew W, Williams M A , et al. Structure-activity relationship studies of novel carbocyclic influenza neuramini dase inhibitors [J]. J Med Chem , 1998 , 41: 2451-2460
[237] Sorbera L A. RWJ2270201[J] . Drugs Fut , 2000 , 25 (3): 249-251.
[238] Wang G T,Chen Y,Wang S ,et al.Design ,synthesis ,and structural analysis of influenza neuraminidase inhibitors containing pyrrolidine cores[J]. J Med Chem ,2001 ,44: 1192-1201.
[239] Rajeshwar P, Verma and Corwin Hansch. A QSAR of neuraminidase inhibitors[J]. J. Bio Med Chem, 2006, 14: 982–996.
[240] Stell V, StewartK D, MaringC J. Influenza neuraminidase inhibitors: structure based design of a novel inhibitor series[J]. Biochem, 2003, 42(3): 718-727.
[241] Lew W, Chen X, KimC U. Discovery and development of GS4104(oseltamivir): an orally active influenza neuraminidase inhibitor[J]. Curr Med Chem, 2000, 7(6): 663-672 .
[242] Wyatt PG, Coomber BA, Evans DN. Sialidase inhibitors related to zanamivir. Further SAR studies of 4-amino-4H-pyran-2-carboxylic acid-6-propylamides[J]. Bioorg Med Chem Lett, 2001, 11(5): 669-673.
[243] Tiralongo J, Pegg M S, von Itzstein M. Effect of substrate aglycon on enzyme mechanism in the reaction of sialidase from influenza virus[J]. FEBS Lett, 1995, 372(2-3): 148-150.
[244] Chanlder M, Bamford M, Conroy R. Synthesis of the potent influenza neuraminidase inhibitor 4-guanidino -Neu5Ac2en[J].Chem Soc Perkin Trans 1995, 1: 1189-1197.
[245] Smith P W, Sollis S L, Howes P D, Dihydropyrancarboxamides related to zanamivir: A new series of inhibitors of influenza virus sialidase[J].Med Chem, 1998, 41(6): 787-797.
[246] Howard H R, Lowe J A, Seeger TF.3-Benzisothiazolylpiperazine derivatives as potential atypical antipsychotic agents[J]. Med Chem, 1996, 39(1): 143-148.
[247] Bamford MJ, Pichel JC, Husman W, Synthesis of 6-,7-and 8-carbon sugar analogues of potent anti-influenza 2,3-dideoxy-N-acetylneuraminic acid derivatives[J]. J Chem SocPerkin Trans 1995,1: 1181-1188.
[248] Smith PW, Robinson JE, Evans DN, Sialidase inhibitors related to zanamivir: synthesis and biological evaluation of 4H-pyran 6-ether and ketone[J]. Bioorg Med Chem Lett,1999, 9(4): 601-604.
[249] Andrews DM, Cherry PC, Humber DC, et al. Synthesis and influenza virus sialidase inhibitory activity of analogues of 4-guanidino-Neu5Ac2en (Zanamivir) modified in the glycerol side-chain [J]. Eur J Med Chem, 1999, 34(7-8): 563～574.
[250] Honda T, Masuda T, Yoshida S, Synthesis and antiinfluenza virus activity of 4-guanidino-7-substituted Neu5Ac2en derivatives[J]. Bioorg Med Chem Lett. 2002, 12 (15): 1921-1924.
[251] Honda T, Masuda T, Yoshida S. Synthesis and antiinfluenza virus activity of 7-O-alkylated derivatives related to zanamivir[J]. Bioorg Med Chem Lett. 2002, 12(15): 1925-1928.
[252] Masuda T, Yoshida S, Arai Ml. Synthesis and anti-influenza evaluation of polyvalent sialidase inhibitors bearing 4-guanidino-Neu5Ac2en derivatives [J]. Chem Pharm Bull, 2003, 51(12): 1386-1398.
[253] Masuda T, Shibuya S, Aria M. Synthesis and anti-influenza evaluation of orally active bicyclic ether derivatives related to zanamivir[J].Bioorg Med Chem Lett. 2003, 13(3): 669-673.
[254] Mineno T, Miller M J. Stereoselective total synthesis of racemic BCX-1812(RWJ-270201) for the development of neuraminidase inhibitors as anti-influenza agents [J].Org Chem, 2003, 68(17): 6591-6596.
[255] Humphrey AJ., Fremann C, Critchley P,et al, Biological Properties of N-Acyl and N-Haloacetyl Neuraminic Acids: Processing by Enzymes of Sialic Acid Metabolism, and Interaction with Influenza Virus [J], Bioorg. Med.Chem. 2002,10:3175–3185;
[256] Atigadda VR.,Brouillette WJ, Duarte F. Hydrophobic Benzoic Acids as Inhibitors of Influenza Neuraminidase, [J].Bioorg.Med.Chem. 1999, 7: 2487-2497.
[257] Kati, W M, Montgomery D, Carrick R, et al. Novelα- andβ-amino acid inhibitors of influenza virus neuraminidase. Antimicrob. [J].Agents Chemother. 2001, 45: 2563-257.
[258] Hanessian S,Wang J,Montgomery D. Design, Synthesis, and Neuraminidase Inhibitory Activity of GS-4071 Analogues that Utilize a Novel Hydrophobic Paradigm, [J].Bioorg. Med. Chem. Lett. 2002, 12: 3425-3429.
[259] Wang GT,Wang S, Gentles R. Design, synthesis, and structural analysis of inhibitors of influenza neuraminidase containing a 2,3-disubstituted tetrahydrofuran-5-carboxylic acid core, [J].Bioorg. Med. Chem. Lett. 2005, 15: 125-128.
[260] Furuta, Y. In vitro and in vivo activities of anti-influenza virus compound T-705 Antimicrob[J]. Agents Chemother. 2002, 46: 977-981.
[261] Zhang L, Williams MA,Mendel DB, Synthesis and evoluation of 1,4,5,6-tetrahydropyrida -zine derivatives as lnfluenza neuraminidase inhibitors.[J].Bioorg.Med.Chem.Lett,1999, 9: 1751 -1756.
[262] Zhang L, Williams M A, Mendel DB, Escarpe P A and Kim C U. Synthesis and activity of C2-substituted analogs of influenza neuraminidase inhibitor GS 4071[J], Bioorg. & Med. Chem. Lett., 1997, 7(14): 1847-1850.
