摘要
本论文通过对多变量图像解析和定量结构与活性相关性研究这两个领域中的一些难点问题的研究,提出了几种新型的化学计量学算法。
1.提出了一种结合空间相关性的多变量图像分割新方法——空间导向凝聚法。该方法核心思想在于将光谱相似性较大且空间最相近的点分割为一个聚类,同时引进了一个F统计量作为空间导向凝聚聚类的终止准则,从而可逐步实现全图像的分割。而且,该算法可以自动鉴定多变量图像中的聚类数及异常点。运用该方法成功处理了棕榈氯霉素I、II晶型混合物药片激光共聚焦拉曼成像分析数据。结果表明,即使在棕榈氯霉素I、II晶型拉曼光谱差别极为细微的情况下,该多变量图像分割方法仍可以有效鉴定棕榈氯霉素I、II晶型的分布与含量。这表明该方法可望为药剂的同质多晶分析、配方分析提供一个新的有效工具。
2.基于纯变量分辨算法的多变量图像分割与拉曼成像技术相结合,对磺胺类药物的实验室制药片与成品药片进行了分析。实验结果表明,所提出的技术可以明确辨别药片中的每种有效成分,并能定量可视化这些有效成分的空间分布。这项技术有望为药品的生产过程提供快速的无损分析,如提高药品质量,进行更好的配方设计和更好的了解药品的制造过程。
3.将基于空间导向凝聚法的多变量图像分割新方法与拉曼成像分析相结合用于分析各种不同组成比例的可互容和不可互容高密度聚乙烯(HDPE)和聚邻苯二甲酸乙二醇酯(PET)共混聚合物的两组分空间非均相性分布情况,并通过扫描电镜验证了分析结果。分析结果说明了马来酸酐能够显著改善HDPE/PET的共混物组分间的分散度和粘度。加入仅仅0.5 wt%的马来酸酐,就能获得可互容的HDPE/PET共混物,其亚相比不互容共混物要小得多,而相界面变得更加模糊。这都表明马来酸酐的加入产生了可互容共混物,其化学组成上的均相性程度大大提高了。
4.提出了变量分区组合算法,该算法是目前化学计量学研究中处于主导地位的隐变量方法的进一步推广。它利用模型的优良性,结合离散粒子群算法进行变量分区,在多个优化的变量子集中分别提取PLS成分,同区变量所提取的隐变量进一步建立相关的隐变量模型,再通过组合各子集的模型获得稳定的QSAR模型。而且,建立了一个新的F统计量用于确定偏最小二乘模型的维数。该算法可有效改善传统隐变量算法欠拟合问题,同时避免模型的过拟合,为复杂数据解析提供了新工具。与逐步回归相比,实验结果显示所提出方法的良好的性能。
5.QSAR中,由于存在多个先导化合物模板,无法运用单一模型解释构效关
The research work in this thesis focuses on the multivariate image analysis and QSAR studies and the design of some new chemometric algorithms used in these two fields.
Chemical imaging analysis holds great potential in probing the chemical heterogeneity of samples with high spatial resolution and molecular specificity. This thesis demonstrates the implementation of Raman mapping for microscopic characterization of tablets containing chloramphenicol palmitate polymorphs with the aid of a new multivariate image segmentation approach based on spatial directed agglomeration clustering. This approach performs the agglomeration clustering by stepwise merging the pixels possessing both spatial closeness and spectral similarity into clusters that define the image segmentation. Additionally, the stepwise merging of clusters offers an F-statistics based procedure to automatically ascertain the number of image segments. Raman mapping analysis of tablets containing two polymorphs of chloramphenicol palmitate followed by multivariate image segmentation reveals that the proposed technique offers the identification of each polymorph and a quantitative visualization of the spatial distribution of the polymorphs identified. This technique holds promise in rapid, noninvasive and quantitative polymorph analysis for pharmaceutical production processes.
A pure variable resolution algorithm coupled with Raman mapping technique is applied to identify pure spectral profiles of the ingredients in sulfa drug tablets. With the spectral data matrix obtained by Raman mapping and the identified pure spectral variables, one can obtain the matrix of the concentration profiles. Then concentration distribution maps are constructed using the concentration profiles at all pixels. The results obtained with laboratory-manufactured mixtures and commercial pharmaceutical formulations reveal that the proposed technique offers the identification of the two effective ingredients and a qualitative visualization of the spatial distribution of the ingredient identified. This approach was expected to be a promising tool in rapid noninvasive analysis for pharmaceutical production processes such as monitoring the distribution of effective ingredients as well as visualizing the pharmaceutical formulations.
Morphology, chemical distribution and domain size in high-density polyethylene/ polyethylene terephthalate (HDPE/PET) polymer blends of various ratios prepared
引文
[1] Latinen H A. Analytical chemistry in a changing world. Analytical Chemistry, 1980, 52: 606A-609A
[2] 高鸿. 分析化学前沿. 北京: 科学出版社,1991
[3] 汪尔康. 21 世纪的分析化学. 北京: 科学出版社,1999
[4] 俞汝勤. 化学计量学导论. 长沙:湖南教育出版社,1991
[5] 梁逸曾. 白灰黑复杂多组分分析体系及其化学计量学算法.长沙:湖南科学技术出版社,1996
[6] 梁逸曾,俞汝勤. 分析化学手册(第十分册),化学计量学. 北京:化学工业出版社,2000
[7] 许禄. 化学计量学方法. 北京:科学出版社,1995
[8] Yu R Q. Chemometrics in China. Chemometrics and Intelligent Laboratory Systems, 1992, 14: 23-29
[9] 梁逸曾,俞汝勤. 化学计量学在我国的发展. 化学通报,1999, 10: 14-19
[10] Kowalski B R. Chemometrics. Analytical Chemistry, 1980, 52: 112R-122R
[11] Frank I E, Kowalski B R. Chemometrics. Analytical Chemistry, 1982, 54: 232 R-243R
[12] Delany M F. Chemometrics. Analytical Chemistry, 1984, 56: 261R-277R
[13] Ramos L S, Beebe K R, Carey W P, et al. Chemometrics. Analytical Chemistry, 1986, 58: 294R-314R
[14] Brown S D, Barker T Q, Larivee R A, et al. Chemometrics. Analytical Chemistry, 1988, 60: 252R-274R
[15] Brown S D. Chemometrics. Analytical Chemistry, 1990, 62: 84R-101R
[16] Brown S D, Bear Jr R S, Blank T B. Chemometrics. Analytical Chemistry, 1992, 64: 22R-49R
[17] Brown S D, Blank T B, Sum S T, et al. Chemometrics. Analytical Chemistry, 1994, 66: 315R-359R
[18] Brown S D, Sum S T, Despagne F, et al. Chemometrics. Analytical Chemistry, 1996, 68: 21R-62R
[19] Lavine B K. Chemometrics. Analytical Chemistry, 1998, 70: 209R-228R
[20] Lavine B K. Chemometrics. Analytical Chemistry, 2000, 72: 91R-97R
[21] Lavine B K, Workman Jr J. Chemometrics. Analytical Chemistry, 2002, 74:2763R-2769R
[22] KoehlerIV F W, Lee E, Kidder L H, et al. Near infrared spectroscopy: the practical chemical imaging solution. Spectroscopy Europe, 2002, 14: 12-19
[23] Lewis E N, Treado P J, Reeder R C, et al. Fourier transform spectroscopic imaging using an infrared focal-plane array detector. Analytical Chemistry, 1995, 67: 3377-3381
[24] Bhargava R, Levin I W. Fourier transform infrared imaging: theory and practice. Analytical Chemistry, 2001, 73: 5157-5167
[25] Geladi P, Grahn H. Multivariate image analysis. Wiley, England, 1996.
[26] Esbensen K H, Wold S, Geladi P. Relationships between higher-order data array configurations and problem formulations in multivariate data analysis. Journal of Chemometrics, 1988, 3: 33-48
[27] Esbensen K H, Geladi P. Strategy of multivariate image analysis (MIA). Chemometrics and Intelligent Laboratory Systems, 1989, 7: 67-86
[28] Esbensen K H, Geladi P, Grahn H. Strategies for multivariate image regression. Chemometrics and Intelligent Laboratory Systems, 1992, 14: 357-374
[29] Geladi P, Isaksson H, Lindqvist L, et al. Principal component analysis of multivariate images. Chemometrics and Intelligent Laboratory Systems, 1989, 5: 209-220
[30] Geladi P, Esbensen K H. Regression on multivariate images: principal component regression for modeling, prediction and visual diagnostical tools. Journal of Chemometrics, 1991, 5: 97-111
[31] de Juan A, Tauler R. Chemometrics applied to unravel multicomponent processes and mixtures: revisiting latest trends in multivariate resolution. Analytica Chimica Acta, 2003, 500: 195-210
[32] Jiang J H, Ozaki Y. Self-modeling curve resolution (SMCR): principles, techniques, and applications. Applied Spectroscopy Review, 2002, 37: 321-345
[33] Mansfield J R, Sowa M G, Scarth G B, et al. Analysis of spectroscopic imaging data by fuzzy C-means clustering. Analytical Chemistry, 1997, 69: 3370-3374
[34] Hall L, Bensaid A M, Clarke L, et al. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Transactions on Neural Networks, 1992, 3: 672-682
[35] Bezdek J, Hall L, Clarke L. Review of MR image segmentation techniques using pattern recognition. Medical Physics, 1993, 20: 1033-1048
[36] Bro R. PARAFAC. Tutorial and applications. Chemometrics and IntelligentLaboratory Systems, 1997, 38: 149-171
[37] Bro R. Multi-way calibration. Multilinear PLS. Journal of Chemometrics, 1996, 10: 47-61
[38] Smilde A K. Comments on multilinear PLS. Journal of Chemometrics, 1997, 11: 367-377
[39] Duponchel L, Elmi-Rayaleh W, Ruckebusch C, et al. Multivariate curve resolution methods in imaging spectroscopy: influence of extraction methods and instrumental perturbations. Journal of Chemical Information and Computer Sciences, 2003, 43: 2057-2067
[40] Willse A, Tyler B. Poisson and multinomial mixture models for multivariate SIMS image segmentation. Analytical Chemistry, 2002, 74: 6314-6322
[41] Yu P. Application of cluster analysis (CLA) in feed chemical imaging to accurately reveal structural-chemical features of feeds and plants within cellular dimension. Journal of Agricultural and Food Chemistry, 2005, 53: 2872-2880
[42] Zuzak K J, Schaeberle M D, Lewis E N, et al. Visible reflectance hyperspectral imaging: characterization of a noninvasive, in vivo System for determining tissue perfusion. Analytical Chemistry, 2002, 74: 2021-2028
[43] Chan K L A, Hammond S V, Kazarian S G. Applications of attenuated total reflection infrared spectroscopic imaging to pharmaceutical formulations. Analytical Chemistry, 2003, 75: 2140-2146
[44] Schaeberle M D, Karakatsanis C G, Lau C J, et al. Raman chemical imaging: noninvasive visualization of polymer blend architecture. Analytical Chemistry, 1995, 67: 4316-4321
[45] Schaeberle M D, Kalasinsky V F, Luke J L, et al. Raman chemical imaging: histopathology of inclusions in human breast tissue. Analytical Chemistry, 1996, 68: 1829-1833
[46] de Juan A, Tauler R, Dyson R, et al. Spectroscopic imaging and chemometrics: a powerful combination for global and local sample analysis. TRAC Trends in Analytical Chemistry, 2004, 23: 70-79
[47] Jiang C, Zhao J, Therese H A, et al. Raman imaging and spectroscopy of heterogeneous individual carbon nanotubes. The Journal of Physical Chemistry B, 2003, 107: 8742-8745
[48] RayIII K, McCreery R L. Spatially resolved Raman spectroscopy of carbon electrode surfaces: observations of structural and chemical heterogeneity. Analytical Chemistry, 1997, 69: 4680-4687
[49] Balss K M, Kuo T C, Bohn P W. Direct chemical mapping of electrochemically generated spatial composition gradients on thin gold films with surface-enhanced Raman spectroscopy. The Journal of Physical Chemistry B, 2003, 107: 994-1000
[50] Lee C J, Pezzotti G, Okui Y, et al. Raman microprobe mapping of residual microstresses in 3C-SiC film epitaxial lateral grown on patterned Si(111). Applied Surface Science, 2004, 228: 10-16
[51] 庞怡诺, 殷恭宽.药物多晶型. 华西药学杂志, 2000, 15: 197-199
[52] 阿基业, 刘云. 片剂加工工艺对药物多晶型的影响. 江苏药学与临床研究. 1998, 6: 1-4
[53] Otsuka M, Otsuka K, Kaneniwa N. Relation between polymorphic transformation pathway during grinding and the physico-chemical properties of bulk powders for pharmaceutical preparations. Drug Development and Industrial Pharmacy, 1994, 20: 1649-1660
[54] Otsuka M, Kaneniwa N. Effect of seed crystals on solid-state transformation of polymorphs of chloramphenicol palmitate during grinding. Journal of Pharmaceutical Sciences, 1986, 75: 506-511
[55] Gubskaya A V, Chishko K A, Lisnyak Y V, et al. Effect of cryogrinding on physico-chemical properties of drugs. II. Cortisone acetate: particles sizes and polymorphic transition. Drug Development and Industrial Pharmacy, 1995, 21: 1965-1974
[56] Otsuka M, Matsumoto T, Kaneniwa N. Effects of the mechanical energy of multi-tableting compression on the polymorphic transformations of chlorpropamide. Journal of Pharmacy and Pharmacology, 1989, 41: 665-669
[57] Threlfall T L. Analysis of organic polymorphs. Analyst, 1995, 120: 2435-2460
[58] De villiers, Van der Wat J G, Lotter A P. The interconversion of the polymorphic forms of chloramphenicol palmitate (CAP) as a function of environmental temperature. Drug Development and Industrial Pharmacy, 1991, 17: 1295-1303
[59] Yoshihisa M, Kawaguchi S Y, Kobayashi H, et al. Physico-chemical characterization of spray-dried phenylbutazone polymorphs. Journal of Pharmaceutical Sciences, 1984, 73: 173-179
[60] Olives A I, Martin M A, del Castillo B, et al. Influence of the presence of trace amounts of metals on the polymorphism of tolbutamide. Journal of Pharmaceutical and Biomedical Analysis, 1996, 14: 1069-1076
[61] 王少剑, 赵玉华, 李涛. 高分子合金-聚合物共混改性的研究与应用. 齐齐哈尔师范学院学报 (自然科学版), 1995, 15: 26-28
[62] 宋国君, 舒文艺. 聚合物合金的相容性与增容. 青岛大学学报., 1995, 10: 90-96
[63] 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, 141-164
[64] Kier L B, Hall L H. Molecular connectivity in structure-activity analysis. Wiley: New York, 1986.
[65] Trinajstic N. Chemical graph theory. 2nd revised ed.; CRC Press: Boca Raton, FL, 1992, Chapter 10.
[66] Katritzky A R, Lobanov V S, Karelson M. QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chemical Society Reviews, 1995, 24: 279-287
[67] Baumann K, Albert H, Korff M V. A systematic evaluation of the benefits and hazards of variable selection in latent variable regression. Part I. Search algorithm, theory and simulations. Journal of Chemometrics, 2002, 16: 339-350
[68] Martens H, Naes T. Multivariate calibration. John Wiley & Sons: New York, 1989.
[69] Vapnik V. The nature of statistical learning theory. New York: Springer-Verlag, 1995.
[70] 陈希孺, 王松桂. 近代回归分析-原理方法及应用. 安徽: 安徽教育出版社. 1987, 151-212
[71] Mallows C L. Some comments on Cp. Technometrics, 1973, 15: 661-675
[72] Allen D M. Prediction as a criterion for selecting variables. Technometrics, 1971, 13: 469-475
[73] Allen D M. The relationship between variable selection and data agumentation and a method for prediction. Technometrics, 1974, 16: 125-127
[74] Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 1974, 19: 716-723
[75] Rassokhin D N, Agrafiotis D K. Kolmogorov-Smirnov statistic and its application in library design. Journal of Molecular Graphics and Modelling, 2000, 18: 368-382
[76] Lin T-H, Li H-T, Tsai K-C. Implementing the Fisher's discriminant ratio in a k-means clustering algorithm for feature selection and data set trimming. Journal of Chemical Information and Computer Sciences, 2004, 44: 76-87
[77] Rajko R, Heberger K. Conditional Fisher's exact test as a selection criterion for pair-correlation method. Type I and Type II errors. Chemometrics and IntelligentLaboratory Systems, 2001, 57: 1-14
[78] Godden J W, Bajorath J. Differential Shannon entropy as a sensitive measure of differences in database variability of molecular descriptors differential Shannon entropy as a sensitive measure of differences in database variability of molecular descriptors. Journal of Chemical Information and Computer Sciences, 2001, 41: 1060-1066
[79] Matsui K, Suganami Y, Kosugi Y. Feature selection by genetic algorithm for MRI segmentation. Systems and Computers in Japan, 1999, 30: 69-78
[80] Draper N R, Smith H. Applied regression analysis. 3rd ed.; Wiley: New York, 1998.
[81] Roy K, Leonard J T. QSAR modeling of HIV-1 reverse transcriptase inhibitor 2-amino-6-arylsulfonylbenzonitriles and congeners using molecular connectivity and E-state parameters. Bioorganic and Medicinal Chemistry, 2004, 12: 745-754
[82] Kalivas J H, Roberts N, Sutter J M. Global optimization by simulated annealing with wavelength selection for ultraviolet-visible spectrophotometry. Analytical Chemistry, 1989, 61: 2024-2030
[83] Kirkpatrick S, Gerlatt C D Jr, Vecchi M P. Optimization by simulated annealing. Science, 1983, 220: 671-680
[84] Aarts E, Korst J. Simulated annealing and Boltzmann machines. Wiley, Chichester, 1989, 13-31
[85] Kalivas J H. (ed.), Adaptation of simulated annealing to chemical optimization problems, Elsevier, Amsterdam, 1995.
[86] Bohachevsky I O, Johnson M E, Stein M L. Generalized simulated annealing for function optimization. Technometrics, 1986, 28: 209-217
[87] Hasegawa K, Miyashita Y, Funatsu K. GA strategy for variable selection in QSAR studies: GA-based PLS analysis of calcium channel antagonists. Journal of Chemical Information and Computer Sciences, 1997, 37: 306-310
[88] Lucasius C B, Kateman G. Understanding and using genetic algorithms. Part 1. Concepts, properties and context. Chemometrics and Intelligent Laboratory Systems, 1993, 19: 1-33
[89] Lucasius C B, Kateman G. Understanding and using genetic algorithms. Part 2. Representation, configuration and hybridization. Chemometrics and Intelligent Laboratory Systems, 1994, 25: 99-145
[90] Goldberg D E. Genetic algorithms in search, optimization & machine learning. Addison-Wesley: NewYork, 1989.
[91] Holland J H. Genetic algorithms. Scientific American, 1992, 66-72
[92] Han Y, Steinbeck C. Evolutionary-algorithm-based strategy for computer- assisted structure elucidation. Journal of Chemical Information and Computer Sciences, 2004, 44: 489-498
[93] Kubinyi H. Variable selection in QSAR studies. II. A highly efficient combination of systematic search and evolution. Quantitative Structure-Activity Relationships, 1994, 13: 393-401
[94] Shen Q, Jiang J-H, Shen G-L, et al. Variable selection by an evolution algorithm using modified Cp based on MLR and PLS modeling: QSAR studies of carcinogenicity of aromatic amines. Analytical and Bioanalytical Chemistry, 2003, 375: 248-254
[95] Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Australia, Perth, 1995, 1942-1948
[96] Shen Q, Jiang J-H, Jiao C-X, et al. Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists. European Journal of Pharmaceutical Sciences, 2004, 22: 145-152
[97] Wang Z, Durst G L, Eberhart R C, et al. Particle swarm optimization and neural network application for QSAR. IEEE: Parallel and Distributed Processing Symposium, 2004, 194-201
[98] Agrafiotis D K, Cedeno W. Feature selection for structure-activity correlation using binary particle swarms. Journal of Medicinal Chemistry, 2002, 45: 1098-1107.
[99] Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. Proceedings of the 1st European Conference on Artificial Life; Varela, F., Bourgine, P., Eds.; Elsevier: Paris, France, 1991, 134-142
[100] Bonabeau E, Dorigo M, Theraulaz G. Inspiration for optimization from social insect behaviour. Nature, 2000, 406: 39-42
[101] Krieger M J B, Billeter J-B, Keller L. Ant-like task allocation and recruitment in cooperative robots. Nature, 2000, 406: 992-995
[102] Izrailev S, Agrafiotis D K. Variable selection for QSAR by artificial ant colony systems. SAR and QSAR in Environmental Research, 2002, 13: 417-423
[103] Shen Q, Jiang J-H, Tao J-C, et al. Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors. Journal of Chemical Information and Modeling, 2005, 45: 1024-1029
[104] Grover M, Singh B, Bakshi M, et al. Quantitative structure–property relationships in pharmaceutical research. Pharmaceutical Science & Technology Today, 2000, 3: 28-35
[105] Topliss J G, Costello R J. Chance correlations in structure-activity studies using multiple regression analysis. Journal of Medicinal Chemistry, 1972, 15: 1066-1068
[106] Kowalski B R. Partial least-squares path modeling with latent variables. Analytica Chimica Acta, 1979, 112: 417-430
[107] K?vesdi I, Dominguez-Rodriguez M F, ?rfi L. Application of neural networks in structure-activity relationships. Medicinal Research Reviews, 1999, 19: 249-269
[108] Shao J. Linear model selection by cross-validation. Journal of the American Statistical Association, 1993, 88: 486-494
[109] Wehrens R, Putter H, Buydens L M C. The bootstrap: a tutorial. Chemometrics and Intelligent Laboratory Systems, 2000, 54: 35-52
[110] Aoyama T, Suzuki Y, Ichikawa H. Neural networks applied to quantitative structure-relationships. Journal of Medicinal Chemistry, 1990, 33: 905-908
[111] Schneider G, Wrede P. Artificial neural networks for computer-based molecular design. Progress in Biophysics and Molecular Biology, 1998, 70: 175-222.
[112] Niculescu S P. Artificial neural networks and genetic algorithms in QSAR. Journal of Molecular Structure, 2003, 622: 71-83
[113] Winkler D A, Burden F R. Bayesian neural nets for modeling in drug discovery. Drug Discovery Today, 2004, 2: 104-111
[114] Patankar S J, Jurs P C. Prediction of glycine/NMDA receptor antagonist inhibition from molecular structure. Journal of Chemical Information and Computer Sciences, 2002, 42: 1053-1068
[115] Zhang L, Jiang J-H, Liu P, et al. Multivariate nonlinear modelling of fluorescence data by neural network with hidden node pruning algorithm. Analytica Chimica Acta, 1997, 344: 29-39
[116] Jiang J-H, Wang J-H, Song X-H, et al. Network training and architecture optimization by a recursive approach and a modified genetic algorithm. Journal of Chemometrics, 1996, 10: 253-267
[117] Mosier P D, Jurs P C. QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks. Journal of Chemical Information and Computer Sciences, 2002, 42: 1460-1470
[118] Chiu T-L, So S-S. Development of neural network QSPR models for Hanschsubstituent constants. 2. Applications in QSAR studies of HIV-1 reverse transcriptase and dihydrofolate reductase inhibitors. Journal of Chemical Information and Computer Sciences, 2004, 44: 154-160
[119] Gini G., Craciun M V, Konig C. Combining unsupervised and supervised artificial neural networks to predict aquatic toxicity. Journal of Chemical Information and Computer Sciences, 2004, 44: 1897-1902
[120] Hemmateenejad B, Akhond M, Miri R, et al. Neural networks: Application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous). Journal of Chemical Information and Computer Sciences, 2003, 43: 1328-1334
[121] Hemmateenejad B, Safarpour M A, Miri R, et al. Toward an optimal procedure for PC-ANN model building: prediction of the carcinogenic activity of a large set of drugs. Journal of Chemical Information and Modeling, 2005, 45: 190-199
[122] Sperduti A, Starita A. Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks, 1997, 8: 714-735
[123] Micheli A, Sperduti A, Starita A, et al. Analysis of the internal representations developed by neural networks for structures applied to quantitative structure-activity relationship studies of benzodiazepines. Journal of Chemical Information and Computer Sciences, 2001, 41: 202-218
[124] Shen Q, Jiang J-H, Jiao C-X, et al. Hybridized particle swarm algorithm for adaptive structure training of multilayer feed-forward neural network: QSAR studies of bioactivity of organic compounds. Journal of Computational Chemistry, 2004, 25: 1726-1735
[125] Lu Q-Z, Shen G-L, Yu R-Q. Genetic training of network using chaos concept: Application to QSAR studies of vibration modes of tetrahedral halides. Journal of Computational Chemistry, 2002, 23: 1357-1365
[126] Lu Q-Z, Jiang J-H, Yu R-Q, et al. A genetic algorithm based on prepotency evolution using chaotic initiation used for network training. Journal of Chemical Information and Computer Sciences, 2003, 43: 1132-1137
[127] Tsutsumi A, Chen W, Hasegawa T, et al. Neural networks for prediction of the dynamic heat-transfer rate in bubble columns. Industrial and Engineering Chemistry Research, 2001, 40: 5358-5361
[128] English N J, Carroll D G. Prediction of Henry's law constants by a quantitative structure property relationship and neural networks. Journal of Chemical Information and Computer Sciences, 2001, 41: 1150-1161
[129] Wailzer B, Klocker J, Buchbauer G, et al. Prediction of the aroma quality and the threshold values of some pyrazines using artificial neural networks. Journal of Medicinal Chemistry, 2001, 44: 2805-2813
[130] Pigram G M, MacDonald T R. Use of neural network models to predict industrial bioreactor effluent quality. Environmental Science and Technology, 2001, 35: 157-162
[131] Polanski J, Zouhiri F, Jeanson L, et al. Use of the Kohonen neural network for rapid screening of ex vivo anti-HIV activity of styrylquinolines. Journal of Medicinal Chemistry, 2002, 45: 4647-4654
[132] Rughooputh S D D V, Rughooputh H C S. Neural network based chemical structure indexing. Journal of Chemical Information and Computer Sciences, 2001, 41: 713-717
[133] Manallack D T, Pitt W R, Gancia E, et al. Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks. Journal of Chemical Information and Computer Sciences, 2002, 42: 1256-1262
[134] Braunheim B B, Miles R W, Schramm V L, et al. Prediction of inhibitor binding free energies by quantum neural networks. Nucleoside analogues binding to trypanosomal nucleoside hydrolase. Biochemistry, 1999, 38: 16076-16083
[135] Selzer P, Gasteiger J, Thomas H, et al. Rapid access to infrared reference spectra of arbitrary organic compounds: Scope and limitations of an approach to the simulation of infrared spectra by neural networks. Chemistry - A European Journal, 2000, 6: 920-927
[136] Vaananen T, Koskela H, Hiltunen Y, et al. Application of quantitative artificial neural network analysis to 2D NMR spectra of hydrocarbon mixtures. Journal of Chemical Information and Computer Sciences, 2002, 42: 1343-1346
[137] Aires-de-Sousa J, Hemmer M C, Gasteiger J. Prediction of H NMR chemical shifts using neural networks. Analytical Chemistry, 2002, 74: 80-90
[138] Sarabia L A, Ortiz M C, Todeschini R, et al. Application of the Kohonen artificial neural network in the identification of proteinaceous binders in samples of panel painting using gas chromatography-mass spectrometry. Analyst, 2003, 128: 281-286
[139] Olivier E, Eldridge R B. Prediction of the trayed distillation column mass-transfer performance by neural networks. Industrial and Engineering Chemistry Research, 2002, 41: 3436-3446
[140] Mattioni B E, Jurs P C. Prediction of glass transition temperatures frommonomer and repeat unit structure using computational neural networks. Journal of Chemical Information and Computer Sciences, 2002, 42: 232-240
[141] Gao L, Loney N W. New hybrid neural network model for prediction of phase equilibrium in a two-phase extraction system. Industrial and Engineering Chemistry Research, 2002, 41: 112-119
[142] Corma A, Serra J M, Argente E, et al. Application of artificial neural networks to combinatorial catalysis: Modeling and predicting ODHE catalysts. ChemPhysChem, 2002, 3: 939-945
[143] Serra J R, Jurs P C, Kaiser K L E. Linear regression and computational neural network prediction of tetrahymena acute toxicity for aromatic compounds from molecular structure. Chemical Research in Toxicology, 2001, 14: 1535-1545
[144] Shaffer R E, Rose-Pehrsson S L. Improved probabilistic neural network algorithm for chemical sensor array pattern recognition. Analytical Chemistry, 1999, 71: 4263-4271
[145] Viterbo V D, Belchior J C. Artificial neural networks applied for studying metallic complexes. Journal of Computational Chemistry, 2001, 22: 1691-1701
[146] Acioli P H, Silva G M. Investigating charge transport in molecular switches with neural networks. Journal of Computational Chemistry, 1999, 20: 1060-1066
[147] Harrington P B, Urbas A, Wan C. Evaluation of neural network models with generalized sensitivity analysis. Analytical Chemistry, 2000, 72: 5004-5013
[148] McCulloch W S, Pitts W. A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 1943, 5: 115-133
[149] Hopfield J J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 1982, 79: 2254-2558
[150] Hopfield J J. Neurons with graded response have collective computational properties like those of two state neurons. Proceedings of the National Academy of Sciences of the United States of America, 1984, 81: 3088-3092
[151] Hopfield J J. Tank D W. Neural computation of decisions in optimization problems. Biological Cybernetics, 1985, 52: 141-152
[152] Rumelhart D E, Hinton G E Williams R J. Parallel distributed processing: exploration in the microstructures of cognition. Cambridge: MIT Press, 1986
[153] Kim K-J, Han I. Application of a hybrid genetic algorithm and neural network approach in activity-based costing. Expert Systems With Applications, 2003, 24:73-77
[154] Huang K, Zhan X-L, Chen F-Q, et al. Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm. Chemical Engineering Science, 2003, 58: 81-87
[155] Burden F R, Rosewarne B S, Winkler D A. Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms. Chemometrics and Intelligent Laboratory Systems, 1997, 38: 127-137
[156] Hunger J, Huttner G. Optimization and analysis of force field parameters by combination of genetic algorithms and neural networks. Journal of Computational Chemistry, 1999, 20: 455-471
[157] Cundari T, Deng J, Zhao Y. Design of a propane ammoxidation catalyst using artificial neural networks and genetic algorithms. Industrial and Engineering Chemistry Research, 2001, 40: 5475-5480
[158] Tarca L A, Grandjean B P A, Larachi F. Integrated genetic algorithm-artificial neural network strategy for modeling important multiphase-flow characteristics. Industrial and Engineering Chemistry Research, 2002, 41: 2543-2551
[159] Hervas C, Algar J A, Silva M. Correction of temperature variations in kinetic-based determinations by use of pruning computational neural networks in conjunction with genetic algorithms. Journal of Chemical Information and Computer Sciences, 2000, 40: 724-731
[160] Kumar A, Hand V C. Feasibility of using neural networks and genetic algorithms to predict and optimize coated paper and board brightness. Industrial and Engineering Chemistry Research, 2000, 39: 4956-4962
[161] Meiler J, Will M. Automated structure elucidation of organic molecules from C13 NMR spectra using genetic algorithms and neural networks. Journal of Chemical Information and Computer Sciences, 2001, 41: 1535-1546
[162] Nandi S, Mukherjee P, Tambe S S, et al. Reaction modeling and optimization using neural networks and genetic algorithms: Case study involving TS-1-catalyzed hydroxylation of benzene. Industrial and Engineering Chemistry Research, 2002, 41: 2159-2169
[163] Cortes C, Vapnik V. Support vector networks. Machine Learning, 1995, 20: 273-297
[164] Osuna E, Freund R. Girosi F. Training support vector machine: an application to face detection. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, New York: IEEE, 1997: 130-136
[165] Dumais S, Platt J, Heckerman D, et al. Inductive learning algorithms and representations for text categorization. In: Proceedings of the 7th International Conference on Information and Knowledge Management, 1998.
[166] Burbidge R, Trotter M, Buxton B, et al. Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Computer and Chemistry, 2001, 26: 5-14
[167] Joachims T. Making large-scale SVM learning practical. In: Scholkopf B, Burges C J C, Smola A eds., Advances in kernel methods-support vector learning, Cambridge, MA: MIT Press, 1998, 169-184
[168] Platt J C. Fast training of SVMs using sequential minimal optimization. In: Scholkopf B, Burges C J C, Smola A eds., Advances in kernel methods-support vector learning, Cambridge, MA: MIT Press, 1998, 185-208
[169] Platt J C. Using sparseness and analytic QP to speed training of support vector machine. In: Kearns M S, Solla S A, Cohn D A eds., Advances in neural information processing systems(Volume 11), Cambridge, MA: MIT Press,1999.
[170] Burges C J C. Building locally invariant kernels. In: Proceeding of the 1997 NIPS Workshop on Support Vector Machine, 1998
[171] Shi Y, Eberhart R. A modified particle swarm optimizer. In IEEE World Congress on Computational Intelligence, 1998, 69-73
[172] Clerc M, Kennedy J. The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE transactions on evolutionary computation, 2002, 6: 58-64
[173] Shi Y, Eberhart R. Fuzzy adaptive particle swarm optimization. In: Proc congress on evolutionary computation, 2001, 79-85
[174] Kennedy J, Eberhart R A. Discrete binary version of the particle swarm algorithm. IEEE Int’1 Conf on Computational Cybernetics and Simulation, 1997, 4104-4108
[175] Carlisle A, Dozier G. Adapting particle swarm optimization to dynamic environments. In: Proc of Int’1 Conf on Artificial Intelligence, 2000, 429-434
[176]Brandstatter B, Baumgartner U. Particle swarm optimization-mass -spring system analogon. IEEE Transactions on Magnetics, 2002, 38: 997-1000
[177] Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proc of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, 39-43
[178] Clerc M. The swarm and the queen: Towards a deterministic and adaptiveparticle swarm optimization. In: Proc of the Congress of Evolutionary Computation, 1999, 1951-1957
[179] Angeline P J. Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. In: Evolutionary Programming VII, 1998, 601-610
[180] Ciuprina G, Loan D, Munteanu I. Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans on Magnetics, 2002, 38: 1037-1040
[181] 李爱国,覃征,鲍复民。粒子群优化算法。计算机工程与应用, 2002, 38: 1-3
[182] Treado P J, Morris M D. In Spectroscopic and Microscopic Imaging of the Chemical State; Morris, M.D., Ed.; Marcel Dekker: New York. 1992, Chapter 3
[183] Kargacin M E, Kowalski B R. Ion intensity and image resolution in secondary ion mass spectrometry. Analytical Chemistry, 1986, 58: 2300-2306
[184] Lavine B, Workman J J Jr. Chemometrics. Analytical Chemistry, 2004, 76: 3365-3372
[185] Shafer-Peltier K E, Haka A S, Motz J T, et al. Model-based biological Raman spectral imaging. Journal of Cellular Biochemistry, 2002, 87: 125-137
[186] Noordam J C, van den Broek W H A M. Multivariate image segmentation based on geometrically guided fuzzy C-means clustering. Journal of Chemometrics, 2002, 16: 1-11
[187] Artyushkova K, Fulghum J E. Multivariate image analysis methods applied to XPS imaging data sets. Surface and Interface Analysis, 2002, 33: 185-195
[188] Artyushkova K, Fulghum J E. Mathematical topographical correction of XPS images using multivariate statistical methods. Surface and Interface Analysis, 2004, 36: 1304-1313
[189] Smentkowski V S, Keenan M R, Ohlhausen J A, et al. Time-of-flight secondary ion mass spectrometry spectral images. Complete description of the sample with one analysis. Analytical Chemistry, 2005, 77: 1530-1536.
[190] Helmy R, Zhou G X, Chen Y W, et al. Characterization and quantitation of aprepitant drug substance polymorphs by attenuated total reflectance fourier transform infrared spectroscopy. Analytical Chemistry, 2003, 75: 605-611.
[191] Wold S, Sj?str?m M. SIMCA: A method for analyzing chemical data in terms of similarity and analogy. In: Chemometrics: Theory and Applications, Kowalski, B.R., (Ed.), ACS Symposium Series, 1977, 52: 243-282
[192] Lawton W H, Sylvestre E A. Self modeling curve resolution. Technometrics, 1971, 13: 617-633
[193] Wold S. Pattern recognition by means of disjoint principal components models. Pattern Recognition, 1976, 8: 127-139
[194] Clark D. The analysis of pharmaceutical substances and formulated products by vibrational spectroscopy. In Handbook of Vibrational Spectroscopy. Chalmers, J.M., Griffiths, P.R., Eds.: John Wiley & Sons: Chichester, 2002.
[195] Clarke F C, Jamieson M J, Clark D A, et al. Chemical image fusion. The synergy of FT-NIR and Raman mapping microscopy to enable a more complete visualization of pharmaceutical formulations. Analytical Chemistry, 2001, 73: 2213-2220
[196] Keen I, Rintoul L, Fredericks P M. Raman and infrared microspectroscopic mapping of plasma-treated and grafted polymer surfaces. Applied Spectroscopy, 2001, 55: 984-991
[197] Schaeberle M D, Morris H R, Turner J F, et al. Raman chemical imaging Spectroscopy. Analytical Chemistry, 1999, 71: 175A-181A
[198] ?a?i? S, Jiang J -H, Sato H, et al. Analyzing Raman images of polymer blends by sample–sample two-dimensional correlation spectroscopy. Analyst, 2003, 128: 1097-1103.
[199] Reese R E, Betts R F, Gumustop B. Trimethoprim–sulfamethoxazole. In: Reese R E, Betts R F, Gumustop B, editors. Handbook of antibiotics. PA, USA: Lippincott Williams and Wilkins; 2000, 449-461.
[200]Jiang J-H, Liang Y-Z, Ozaki Y. On simplex-based method for self-modeling curve resolution of two-way data. Chemometrics and Intelligent Laboratory Systems, 2003, 65: 51-65
[201] Eaton P J, Graham P, Smith J R, et al. Mapping the surface heterogeneity of a polymer blend: An adhesion-force-distribution study using the atomic force microscope. Langmuir, 2000, 16: 7887-7890
[202] Huang H L, Goh S H, Wee A T S. Miscibility and interactions in poly(2,2,3,3,3-pentafluoropropyl methacrylate-co-4-vinylpyridine)/poly(p-vinyl- -phenol) blends. Polymer, 2002, 43: 2861-2867
[203] Champion-Lapalu L, Wilson A, Fuchs G, et al. Cryo-scanning electron microscopy: A new tool for interpretation of fracture studies in bitumen/polymer blends. Energy Fuels, 2002, 16: 143-147
[204] Aoki H, Ito S. Two-dimensional polymers investigated by scanning near-field optical microscopy: Phase separation of polymer blend monolayer. The Journal of Physical Chemistry B, 2001, 105: 4558-4564
[205] Li Z M, Yang W, Xie B H, et al. Effects of compatibilization on the essential work of fracture parameters of in situ microfiber reinforced poly(ethylene terephtahalate)/polyethylene blend. Materials Research Bulletin, 2003, 38: 1867-1878
[206] Li Z M, Yang M B, Feng J M, et al. Morphology of in situ poly(ethylene terephthalate)/polyethylene microfiber reinforced composite formed via slit-die extrusion and hot-stretching. Materials Research Bulletin, 2002, 37: 2185-2197
[207] Burillo G, Herrera-Francob P, Vazquezc M, et al. Compatibilization of recycled and virgin PET with radiation-oxidized HDPE. Radiation Physics and Chemistry, 2002, 63: 241-244
[208] Dimitrova T L, La Mantia F P, Pilati F, et al. On the compatibilization of PET/HDPE blends through a new class of copolyesters. Polymer, 2000, 41: 4817-4824
[209] Gupper A, Wilhelm P, Schmied M, et al. Combined application of imaging methods for the characterization of a polymer blend. Applied Spectroscopy, 2002, 56: 1515-1523
[210] Snively C M, Koenig J L. Fast FTIR imaging: A new tool for the study of semicrystalline polymer morphology. Journal of Polymer Science Part B: Polymer Physics, 1999, 37: 2353-2359.
[211] Sutter J M, Dixon S L, Jurs P C. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. Journal of Chemical Information and Computer Sciences, 1995, 35: 77-84
[212] Steyerberg E W, Eijkemans M J C, Habbema J D F. Stepwise selection in small data sets: A simulation study of bias in logistic regression analysis. Journal of Clinical Epidemiology, 1999, 52: 935-942
[213] Agostinelli C. Robust stepwise regression. Journal of Applied Statistics, 2002, 29: 825-840
[214] Westfall P H, Young S S, Lin D K J. Forward selection error control in the analysis of supersaturated design. Statistica Sinica, 1998, 8: 101-117
[215] Kubinyi H. Evolutionary variable selection in regression and PLS analyses. Journal of Chemometrics, 1996, 10: 119-133
[216] Luke B T. Evolutionary programming applied to the development of quantitativestructure-activity relationships and quantitative structure-property relationships. Journal of Chemical Information and Computer Sciences, 1994, 34: 1279-1287
[217] Rogers D, Hopfinger A J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. Journal of Chemical Information and Computer Sciences, 1994, 34: 854-866
[218] Yasri A, Hartsough D. Toward an optimal procedure for variable selection and QSAR model building. Journal of Chemical Information and Computer Sciences, 2001, 41: 1218-1227
[219] Du Y P, Liang Y Z, Li B Y, et al. Orthogonalization of block variables by subspace-projection for quantitative structure property relationship (QSPR) research. Journal of Chemical Information and Computer Sciences, 2002, 42: 993-1003
[220] Benigni R, Giuliani A, Franke R, et al. Quantitative structure-activity relationships of mutagenic and carcinogenic aromatic amines. Chemical Reviews, 2000, 100: 3697-3714
[221] Kier L B, Hall L H. An electrotopological-state index for atoms in molecules. Pharmaceutical Research, 1990, 7: 801-807
[222] Hall LH, Kier L B. The electrotopological state: structure information at the atomic level for molecular graphs. Journal of Chemical Information and Computer Sciences, 1991, 31: 76-82
[223] Hall L H, Mohney B, Kier L B. The electrotopological state: An atom index for QSAR. Quantitative Structure-Activity Relationships, 1991,10: 43-48
[224] Kier L B, Hall L H, Frazer J W. An index of electrotopological state for atoms in molecules. Journal of Mathematical Chemistry, 1991, 7: 229-237
[225] Hall L H, Kier L B. Electrotopological state indices for atom types: A novel combination of electronic, topological, and valence state information. Journal of Chemical Information and Computer Sciences, 1995, 35: 1039-1045
[226] Viswanadhan V N, Ghose A K, Revankar G R, et al. 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. Journal of Chemical Information and Computer Sciences, 1989, 29: 163-172
[227] Dewar M J S, Thiel W. Ground states of molecules. 38. The MNDO method.Approximations and parameters. Journal of the American Chemical Society, 1977, 99: 4899-4907
[228] Hopfinger A J. Safe handling of chemical carcinogens, mutagens, teratogens and highly toxic substances. Ann Arbor Press, Ann Arbor, 1980, ML, 385-421
[229] Gasteiger J, Marsali M. Iterative partial equalization of orbital electronegativity––a rapid access to atomic charges. Tetrahedron 1980, 36: 3219-3226
[230] Stanton D T, Jurs P C. Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies. Analytical Chemistry, 1990, 62: 2323–2329
[231] Bonchev D. Information theoretic indices for characterization of chemical structures. Research Studies Press, Chichester, UK, 1983, 249-283
[232] Kurup A, Garg R, Carini D J, et al. Comparative QSAR: angiotensin II antagonists. Chemical Reviews, 2001, 101: 2727-2750
[233] Ashton W T, Cantone C L, Chang L L. Nonpeptide angiotensin II antagonists derived from 4H-1,2,4-triazoles and 3H-tmidazo triazoles. Journal of Medicinal Chemistry, 1993, 36: 591-609
[234] Rohrbaugh R H, Jurs P C. Descriptions of molecular shape applied in studies of structure/activity and structure/property relationships. Analytica Chimica Acta, 1987,199: 99-109
[235] Cerius2 QSAR+, 1997, Molecular Simulations Inc., San Diego.
[236] Tetko I V, Villa A E P, Livingstone D J. Neural network studies. 2. Variable selection. Journal of Chemical Information and Computer Sciences, 1996, 36: 794-803
[237] Hasegawa K, Kimura T, Funatsu K. GA strategy for variable selection in QSAR studies: Application of GA-based region selection to a 3D-QSAR study of acetylcholinesterase inhibitors. Journal of Chemical Information and Computer Sciences, 1999, 39: 112-120
[238] Zheng W, Tropsha A. Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. Journal of Chemical Information and Computer Sciences, 2000, 40: 185-194
[239] Hoskuldsson A. Variable and subset selection in PLS regression. Chemometrics and Intelligent Laborary Systems, 2001, 55: 23-38
[240]Xiao Z, Xiao Y-D, Feng J, et al. Antitumor agents. 213. Modeling of epipodophyllotoxin derivatives using variable selection k nearest neighborQSAR method. Journal of Medicinal Chemistry, 2002, 45: 2294-2309
[241]Liu S-S, Liu H-L, Yin C-S, et al. VSMP: A novel variable selection and modeling method based on the prediction. Journal of Chemical Information and Computer Sciences, 2003, 43: 964-969
[242] Cho S J, Hermsmeier M A. Genetic algorithm guided selection: Variable selection and subset selection. Journal of Chemical Information and Computer Sciences, 2002, 42: 927-936
[243] Nasser A M B, Jiang J H, Liang Y Z, et al. Piece-wise quasi-linear modeling in QSAR and analytical calibration based on linear substructures detected by genetic algorithm. Chemometrics and Intelligent Laborary Systems, 2004, 72: 73-82
[244] Du Y P, Liang Y Z, Yun D. Data mining for seeking an accurate quantitative relationship between molecular structure and GC retention indices of alkenes by projection pursuit. Journal of Chemical Information and Computer Sciences, 2002, 42: 1283-1292
[245] Tormod N, Tomas I. Splitting of calibration data by cluster analysis. Journal of Chemometrics, 1991, 5: 49-65
[246] Tormod N. Multivariate calibration when data are split into subsets. Journal of Chemometrics, 1991, 5: 487-501
[247] Leyla O, Mayuresh V K, Christos G. Model predictive control of nonlinear systems using piecewise linear models. Computers and Chemical Engineering, 2002, 24: 793-799
[248] Lin J N, Rolf U. Explicit piecewise-linear models. IEEE Transactions on Circuit and Systems, 1994, 912-931
[249] Morimoto Y, Madarame H. Piecewise linear model for water column oscillator simulating reactor safety system. International Journal of Non-linear Mechanics, 2003, 38: 213-223
[250]Shi Y, Eberhart R. Fuzzy adaptive particle swarm optimization. In: Proc congress on evolutionary computation. 2001, 1: 101-106
[251] Shen Q, Jiang J H, Jiao C X, et al. Optimized partition of minimum spanning tree for piecewise modeling by particle swarm algorithm. QSAR studies of antagonism of angiotensin II antagonists. Journal of Chemical Information and Computer Sciences, 2004, 44: 2027-2031
[252] Hansch C, Silipo C, Steller E E. Formulation of de novo substituent constants in correlation analysis: inhibition of dihydrofolate reductase by2,4-diamino-5-(3,4-dichlorophenyl)-6-substituted pyrimidines. Journal of Pharmaceutical Sciences, 1975, 64: 1186-1191
[253] Alka K, Rajni G, Corwin H. Comparative QSAR study of tyrosine kinase inhibitors. Chemical Reviews, 2001, 101: 2573-2600
[254] Gasteiger J, Zupan J. Neural networks in chemistry. Angewandte Chemie International Edition in English, 1993, 32: 503-527
[255] Svozil D, Kvasni?ka V, Pospíchal J. Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, 1997, 39: 43-62
[256] Hervas C, Toledo R, Silva M. Use of pruned computational neural networks for processing the response of oscillating chemical reactions with a view to analyzing nonlinear multicomponent mixtures. Journal of Chemical Information and Computer Sciences, 2001, 41: 1083-1092
[257] Liu H X, Zhang R S, Yao X J, et al. Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs. Journal of Chemical Information and Computer Sciences, 2004, 44: 161-167
[258] Lin W-Q, Jiang J-H, Shen Q, et al. Optimized block-wise variable combination by particle swarm optimization for partial least squares modeling in quantitative structure-activity relationship studies. Journal of Chemical Information and Modeling, 2005, 45: 486-493
[259] Hornik K, Stichcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Networks, 1989, 2: 359-366
[260] Khanna I K, Weier R M, Yu Y, et al. 1,2-Diarylimidazoles as potent, cyclooxygenase-2 selective, and orally active antiinflammatory agents. Journal of Medicinal Chemistry, 1997, 40: 1634-1647
[261] Garg R, Kurup A, Mekapati S B, et al. Cyclooxygenase (COX) inhibitors: A comparative QSAR study. Chemical Reviews, 2003, 103: 703-731
[262] Amic D, Davidovic-Amic D, Beslo D, et al. The use of the ordered orthogonalized multivariate linear regression in a structure-activity study of coumarin and flavonoid derivatives as inhibitors of aldose reductase. Journal of Chemical Information and Computer Sciences, 1997, 37: 581– 586
[263] Krzanowski W J. Selection of variables to preserve multivariate data structure, using principal components. Applied Statistics, 1987, 36: 22-33
[264] Shen M, LeTiran A, Xiao Y, et al. Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearestneighbor and simulated annealing PLS methods. Journal of Medicinal Chemistry, 2002, 45: 2811-2823
[265] Leardi R, Boggia R, Terrile M. Genetic algorithms as a strategy for feature selection. Journal of Chemometrics, 1992, 6: 267- 281
[266] Wikel J, Dow E R. The use of neural networks for variable selection in QSAR. Bioorganic and Medicinal Chemistry Letters, 1993, 3: 645- 651
[267] Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines[A]. In: Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing[C], IEEE Press, 1997, 276-285
[268] Suykens J A K. Least squares support vector machines [M]. World Scientific Pub Co, 2002.
[269] Keerthi S S. A fast iterative nearest point algorithm for support vector machine classifier design [J].IEEE Trans. on Neural Networks, 2000, 11: 124 -136
[270] Amit S, Kalgutkar K R, Kozak B C, et al. Covalent modification of cyclooxygenase-2 (COX-2) by 2-acetoxyphenyl alkyl sulfides, a new class of selective COX-2 inactivatiors. Journal of Medicinal Chemistry, 1998, 41: 4800-4818
[271] Netzeva T I, Dearden J C, Edwards R, et al. QSAR analysis of the toxicity of aromatic compounds to Chlorella vulgaris in a novel short-term assay. Journal of Chemical Information and Computer Sciences, 2004, 44: 258-265
[272] Le Pecq J-B, Dat-Xuong N, Gosse C, et al. A new antitumoral agent: 9-hydroxyellipticine. Possibility of a rational design of anticancerous drugs in the series of DNA intercalating drugs. Proceedings of the National Academy of Sciences of the United States of America, 1974, 71: 5078-5082
[273] Baguley B C, Denny W A, Atwell G J, et al. Potential antitumor agents. Part 35. Quantitative relationships between antitumor (L1210) potency and DNA binding for 4-(9-acridinylamino)methanesulfon-m-anisidide analogues. Journal of Medicinal Chemistry, 1981, 24: 520-525
[274] Hartley J A, Reszka K, Zuo E T, et al, Characteristics of the interaction of anthrapyrazole anticancer agents with deoxyribonucleic acids: structural requirements for DNA binding, intercalation and photosensitization. Molecular Pharmacology, 1988, 33: 265-271
[275] Denny W A. DNA-Intercalating agents as antitumour drugs: prospects for future design. Anti-Cancer Drug Design, 1989, 4: 241-263
[276] Esnault C, Roques P B, Jacquemin-Sablon A, et al. Effects of new antitumor bifunctional intercalators derived from 7H-pyridocarbazole on sensitive andresistant L110 cell lines. Cancer Research, 1984, 44: 4355-4360
[277] Denny W A, Atwell G J, Baguley B C, et al, Potential antitumor agents. Part 44. Synthesis and antitumor activity of new classes of diacridines. The importance of linker chain rigidity for DNA binding kinetics and biological activity. Journal of Medicinal Chemistry, 1985, 28: 1568-1574
[278] Wakelin L P G. Polyfunctional DNA intercalating agents. Medicinal Research Review, 1986, 6: 276-340
[279] Muss H B, Blessing J A, Eddy G L, et al. Echinomycin (NSC-526417) in recurrent and metastatic squamous cell carcinoma of the cervix - A phase II trial of the gynecologic oncology group (GOG). Investigational New Drugs, 1992, 10: 25-26
[280] Wadler S, Tenteromano L, Cazenave L, et al. Phase II trial of echinomycin in patients with advanced or recurrent colorectal cancer. Cancer Chemother Pharmacol, 1994, 34: 266-269
[281] Brana M F, Castellano J M, Moran M, et al. Bis-naphthalimides 3: Synthesis and antitumor activity of N,N -bis[2-(1,8-naphthalimido)ethyl]alkane-diamines. Anti-Cancer Drug Design, 1996, 11: 297-309
[282] Cholody W M, Hernandez L, Hassner L, et al. Bisimidazoacridones and related compounds: new antineoplastic agents with high selectivity against colon tumours. Journal of Medicinal Chemistry, 1995, 38: 3043-3052
[283] Gamage S A, Spicer J A, Atwell G J, et al. Structure-activity relationships for substituted bis(acridine-4-carboxamides): A new class of anticancer agents. Journal of Medicinal Chemistry, 1999, 42: 2383-2393
[284] Spicer J A, Gamage S A, Rewcastle G W, et al. Tyrosine kinase inhibitors. 17. Irreversible inhibitors of the epidermal growth factor receptor: 4-(Phenylamino)quinazoline- and 4-(Phenylamino)pyrido-[3,2-d]pyrimidine-6- acryla-mides Bearing Additional Solubilizing Functions. Journal of Medicinal Chemistry, 2000, 43: 1380-1397
[285] Baguley B C, Zhuang L, Marshall E M. Experimental solid tumour activity of N-[2-(dimethylamino)ethyl] acridine-4-carboxamide. Cancer Chemother Pharmacol, 1995, 36: 244-248
[286] Finlay G J, Riou J-F, Baguley B C. From amsacrine to DACA (N-[2-(dimethylamino)ethyl]acridine-4-carboxamide): selectivity for topoisomerases I and II among acridine derivatives. European Journal of Cancer, 1996, 32: 708-714
[287] Finlay G J, Marshall E, Matthews J H L, et al. In vitro assessment of N-[2-(dimethylamino)ethyl]- acridine-4-carboxamide (DACA), a DNAintercalating antitumor drug with reduced sensitivity to multidrug resistance. Cancer Chemother Pharmacol, 1993, 31: 401-406
[288] Atwell G J, Rewcastle G W, Baguley B C, et al. Potential antitumor agents. 50. In vivo solid tumor activity of derivatives of N-[2-(dimethylamino)ethyl] acridine-4-carboxamide. Journal of Medicinal Chemistry, 1987, 30: 664-669
[289] Garg R, Denny W A, Hansch C. Comparative QSAR studies on substituted bis-(acridines) and bis-(phenazines)-carboxamides: A new class of anticancer agents. Bioorganic & Medicinal Chemistry, 2000, 8: 1835-1839
[290] Lindgren F, Geladi P, Rannar S, et al. Interacwe variable selection (IVS) for PLS, Part I: Theory and algorithins. Journal of Chemometrics, 1994, 8: 349-363
[291] Lindgren F, Geladi P, Berglunel A et al. Interactive variable selection (IVS) for PLS, Part II: Chemical applications. Journal of Chemometrics, 1995, 9: 331-342
[292] Cruciani G, Watson K A. Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. Journal of Medicinal Chemistry, 1994, 37: 2589-2601
[293] Pastor M, Cruciani G, Clementi S. Smart region definition: A new way to improve the predictive ability and interpretability of three-dimensional quantitative structure-activity relationships. Journal of Medicinal Chemistry, 1997, 40: 1455-1464
[294] Pastor M, Cruciani G, Watson K A. A strategy for the incorporation of water molecules present in a ligand binding site into a three-dimensional quantitative structure-activity relationship analysis. Journal of Medicinal Chemistry, 1997, 40: 4089-4102
[295] Wold S. Hierarchical multiblock PLS and PC models for easier model interpretation as an alternative to variable selection. Journal of Chemometrics, 1996, 10: 463-482
[296] Hansch C. In exploring QSAR: Fundamentals and applications in chemistry and biology; Leo, A., Ed.; American Chemical Society, Washington, DC. 1995
[297] Braestrup C, Honore T, Nielsen M, et al. Ligands for benzodiazepine receptors with positive and negative efficacy. Biochemical Pharmacology, 1984, 33: 859-862
[298] Haefely W, Kyburz E, Gerecke M, et al. Recent advances in the molecular pharmacology of benzodiazepine receptors and in the structure-activity relationships of their agonists and antagonists. In: Advances in Drug Research, Testa, B., Ed.; Academic: Orlando, 1985, 14: 165-322
[299] Yokoyama N, Ritter B, Neubert A D. 2-Arylpyrazolo[4,3-c]quinolin-3-ones: a novel agonist, a partial agonist and an antagonist of benzodiazepines. Journal of Medicinal Chemistry, 1982, 25: 337-339
[300] Takada S, Shindo H, Sasatani T, et al. Thienylpyrazoloquinolines: potent agonists and inverse agonists to benzodiazepine receptors. Journal of Medicinal Chemistry, 1988, 31: 1738-1745
[301] Shindo H, Takada S, Murata S, et al. Thienylpyrazoloquinolines with high affinity to benzodiazepine receptors: continuous shift from inverse agonist to agonist properties depending on the size of the alkyl substituent. Journal of Medicinal Chemistry, 1989, 32: 1213-1217
[302] Fryer R I, Zhang P, Rios R, et al. Structure-activity relationship studies at benzodiazepine receptor (BZR): a comparison of the substituent effects of pyrazoloquinolinone analogs. Journal of Medicinal Chemistry, 1993, 36: 1669-1673
[303] Hadjipavlou-Litina D, Garg R, Hansch C. Comparative quantitative structure-activity relationship studies (QSAR) on non-benzodiazepine compounds binding to benzodiazepine receptor (BzR). Chemical Reviews, 2004, 104: 3751-3794
[304] Savini L, Massarelli P, Nencini C, et al. High affinity central benzodiazepine receptor ligands: synthesis and structure–activity relationship studies of a new series of pyrazolo[4,3-c]quinolin-3-ones. Bioorganic and Medicinal Chemistry, 1998, 6: 389-399