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研究生:呂侑宸
研究生(外文):You-Chen Lu
論文名稱:使用Hierarchical Support Vector Regression發展Quantiative Structure Activity Relationship模型預測芳香族硝基化合物的致突變
論文名稱(外文):Development of a Quantiative Structure Activity Relationship Model to Predict Mutagenicity of Atomatic Nitro Based on Hierarchical Support Vector Regression
指導教授:梁剛荐
指導教授(外文):Max K. Leong
學位類別:碩士
校院名稱:國立東華大學
系所名稱:化學系
學門:自然科學學門
學類:化學學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
論文頁數:84
中文關鍵詞:
外文關鍵詞:Aromatic nitrosmutagenicitySalmonella typhimurium TA98Hierarchical support vector regression
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Aromatic nitro compounds 常見於工業、食物與環境中,並且具有mutagenicity和carcinogenicity的特性。基於此,需要發展in silico模型去預測Aromatic nitros的mutagenicity。本實驗的目標是利用來自不同文獻中的Salmonella typhimurium TA98‒S9 mutagenicity去發展hierarchical support vector regression (HSVR) 模型。各樣統計分析皆確保accuracy 和 predictivity。在結果方面,HSVR 模型對於Aromatic nitros擁有很好的預測能力:training set (n = 226, r2= 0.909, = 0.901, RMSE = 0.627, s = 0.375), test set (n = 56, r2= 0.827, RMSE = 0.732, s = 0.441), and outlier set (n = 8, r2 = 0.845, RMSE = 0.315, s = 0.200)。根據統計數據證明,HSVR 模型優於已過文獻中所提出的模型。所以本篇發展的HSVR 模型是一個很好的預測工具,可以用來預測空氣、食物或藥物中的化學毒性。
Aromatic nitro compounds are commonly seen in industries, food, and environment, and they are closely related to mutagenicity or even carcinogenicity in some cases. As such, it is of necessity to develop an in silico model to predict their mutagenicity. The objective of this study was to construct a predictive model using 15 descriptors and the hierarchical support vector regression (HSVR) scheme based on Salmonella typhimurium TA98‒S9 mutagenicity data compiled from the literature. Various statistical analyses were employed to ensure its accuracy and predictivity. The predictions by the HSVR model are in good agreement with the observed values for those molecules in the training set (n = 226, r2= 0.909, = 0.901, RMSE = 0.627, s = 0.375), test set (n = 56, r2= 0.827, RMSE = 0.732, s = 0.441), and outlier set (n = 8, r2 = 0.845, RMSE = 0.315, s = 0.200). The results indicate that the HSVR model performed better than published models statistically and can be employed as a tool to predict mutagenicity of new aromatic nitro compounds.
摘要 ……….………………………………………………..………..………………………..i
ABSTRACT ..………………………………………………..………..………………………..i
KEYWORDS ......………………………………………...…………................................…….i
TABLE OF CONTENTS ...…………..……………………………..……………………....…ii
1. Introduction …...…………………………………………………………………………….1
2. Materials and Methods ...……………………………………………………………….…...5
2.1 Data Compilation ....……………………………………………………...……………...5
2.2 Molecular descriptors ..………………………………………………………..……..…..5
2.3 Descriptor preprocessing and selection ...…..…………………………….…………..….6
2.4 Set Selection ...……………...……………………………………………………….…...7
2.5 Hierarchical Support Vector Regression ...………………………………….…………...8
2.6 Model Evaluation ...…………..……………………………………………..…….....…..9
3. Results ..……….………………………………..……….………..……….……………….13
3.1 Dataset selection ...….….………………………...….…….….……….………...……..13
3.2 SVR models ...………………………………………………………………………….13
3.2 HSVR ...……………………………………………………………….…………..…....14
4. Discussion ...………………………………………………………………………….……19
5. Conclusion ...…………………………………………………………….…………...……23
6. References ...………………………………………………………………….……………25

Kielhorn, J. (2003). Selected nitro - and nitro-oxy-polycyclic aromatic hydrocarbons / first draft prepared by Drs J. Kielhorn, U. Wahnschaffe and I. Mangelsdorf. World Health Organization, Geneva.
Möller, L., Lax, I. and Eriksson, L. (1993). Nitrated polycyclic aromatic hydrocarbons: a risk assessment for the urban citizen. Environ. Health Perspect. 101, 309-315.
Purohit, V. and Basu, A. K. (2000). Mutagenicity of nitroaromatic compounds. Chem. Res. Toxicol. 13, 673-92.
Tokiwa, H., Ohnishi, Y. and Rosenkranz, H. S. (1986). Mutagenicity and Carcinogenicity of Nitroarenes and Their Sources in the Environment. Crit. Rev. Toxicol. 17, 23-58.
International Agency for Research on Cancer (1989). Diesel and gasoline engine exhausts and some nitroarenes (I. W. G. o. t. E. o. C. R. t. Humans, Ed.^ Eds.). World Health Organization.
Rosenkranz, H. S., McCoy, E. C., Sanders, D. R., Butler, M., Kiriazides, D. K. and Mermelstein, R. (1980). Nitropyrenes: Isolation, Identification, and Reduction of Mutagenic Impurities in Carbon Black and Toners. Science 209, 1039-1043.
Rosenkranz, H. S. and Mermelstein, R. (1983). Mutagenicity and genotoxicity of nitroarenes: All nitro-containing chemicals were not created equal. Mutat. Res.-Rev. Genet. Toxicol. 114, 217-267.
Hakimelahi, G. H. and Khodarahmi, G. A. (2005). The identification of toxicophores for the prediction of mutagenicity, hepatotoxicity and cardiotoxicity. J. Iran. Chem. Soc. 2, 244-267.
Sabbioni, G. (1994). Hemoglobin Binding of Nitroarenes and Quantitative Structure-Activity Relationships. Chem. Res. Toxicol. 7, 267-274.
Beland, F. A. (1990). Metabolic activation and DNA adducts of aromatic amines and nitroaromatic hydrocarbons (C. S. Cooper and P. L. Grover, Ed.^ Eds.), pp. 267-325, Springer-Verlag, Berlin.
Beland, F. A. and Marques, M. M. (1994). DNA adducts of nitropolycyclic aromatic hydrocarbons. IARC Sci Publ, 229-44.
Benigni, R. and Bossa, C. (2011). Mechanisms of Chemical Carcinogenicity and Mutagenicity: A Review with Implications for Predictive Toxicology. Chem. Rev. 111, 2507-2536.
Fu, P. P. (1990). Metabolism of Nitro-Polycyclic Aromatic Hydrocarbons. Drug Metab. Rev. 22, 209-268.
Rosenkranz, H. S. and Howard, P. C. (1986). Structural basis of the activity of nitrated polycyclic aromatic hydrocarbons. Dev. Toxicol. Environ. Sci. 13, 141-68.
Vogt, R. A., Rahman, S. and Crespo-Hernández, C. E. (2010). Structure–Activity Relationships in Nitro-Aromatic Compounds (J. Leszczynski and M. K. Shukla, eds.), pp. 217-240. Springer, New York.
Atkinson, R. and Arey, J. (1994). Atmospheric chemistry of gas-phase polycyclic aromatic hydrocarbons: formation of atmospheric mutagens. Environ. Health Perspect. 102 (Suppl 4), 117-26.
Castells, P., Santos, F. J. and Galceran, M. T. (2003). Development of a sequential supercritical fluid extraction method for the analysis of nitrated and oxygenated derivatives of polycyclic aromatic hydrocarbons in urban aerosols. J Chromatogr A 1010, 141-51.
Leníček, J., Sekyra, M., Bednárková, K., Beneš, I. and Šípek, F. (2000). Fractionation and Chemical Analysis of Urban Air Particulate Extracts. Int. J. Environ. Anal. Chem. 77, 269-288.
Söderström, H., Hajšlová, J., Kocourek, V., Siegmund, B., Kocan, A., Obiedzinski, M. W., Tysklind, M. and Bergqvist, P.-A. (2005). PAHs and nitrated PAHs in air of five European countries determined using SPMDs as passive samplers. Atmos. Environ. 39, 1627-1640.
Schauer, C., Niessner, R. and Pöschl, U. (2004). Analysis of nitrated polycyclic aromatic hydrocarbons by liquid chromatography with fluorescence and mass spectrometry detection: air particulate matter, soot, and reaction product studies. Anal. Bioanal. Chem. 378, 725-736.
Yaffe, D., Cohen, Y., Arey, J. and Grosovsky, A. J. (2001). Multimedia analysis of PAHs and nitro-PAH daughter products in the Los Angeles Basin. Risk Anal. 21, 275-94.
Casalegno, M., Sello, G. and Benfenati, E. (2008). Definition and Detection of Outliers in Chemical Space. J. Chem. Inf. Model. 48, 1592-1601.
Ames, B. N., McCann, J. and Yamasaki, E. (1975). Methods for detecting carcinogens and mutagens with the Salmonella/mammalian-microsome mutagenicity test. Mutat. Res. 31, 347-64.
Mortelmans, K. and Zeiger, E. (2000). The Ames Salmonella/microsome mutagenicity assay. Mutat. Res. 455, 29-60.
Soderman, J. V. (1982). CRC Handbook of Identified Carcinogens and Noncarcinogens: Carcinogenicity and Mutagenicity Database. CRC Press, Boca Raton, Florida.
Benigni, R., Giuliani, A., Franke, R. and Gruska, A. (2000). Quantitative Structure-Activity Relationships of Mutagenic and Carcinogenic Aromatic Amines. Chem. Rev. 100, 3697-3714.
Benigni, R. (2005). Structure-activity relationship studies of chemical mutagens and carcinogens: Mechanistic investigations and prediction approaches. Chem. Rev. 105, 1767-1800.
Boelsterli, U. A., Ho, H. K., Zhou, S. and Yeow Leow, K. (2006). Bioactivation and Hepatotoxicity of Nitroaromatic Drugs. Curr. Drug Metab. 7, 715-727.
Maynard, A. T., Pedersen, L. G., Posner, H. S. and McKinney, J. D. (1986). An Ab initio study of the relationship between nitroarene mutagenicity and electron affinity. Mol. Pharmacol. 29, 629-636.
Hansch, C., Leo, A. and Hoekman, D. H. (1995). Exploring QSAR: Hydrophobic, electronic, and steric constants. American Chemical Society, Washington, DC.
Schultz, T. W., Cronin, M. T. D., Walker, J. D. and Aptula, A. O. (2003). Quantitative structure-activity relationships (QSARs) in toxicology: a historical perspective. Theochem-J. Mol. Struct. 622, 1-22.
Verma, R. P., Mekapati, S. B., Kurup, A. and Hansch, C. (2005). A QSAR review on melanoma toxicity. Bioorg. Med. Chem. 13, 5508-5526.
Miertuš, S., Scrocco, E. and Tomasi, J. (1981). Electrostatic interaction of a solute with a continuum. A direct utilizaion of AB initio molecular potentials for the prevision of solvent effects. Chem. Phys. 55, 117-129.
Perkins, R., Fang, H., Tong, W. and Welsh, W. J. (2003). Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicology. Environ. Toxicol. Chem. 22, 1666-1679.
Debnath, A. K., Lopez de Compadre, R., Debnath, G., Shusterman, A. J. and Hansch, C. (1991). Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity. J. Med. Chem. 34, 786-797.
Caliendo, G., Fattorusso, C., Greco, G., Novellinor, E., Perissutti, E. and Santagada, V. (1995). Shape-Dependent Effects in a Series of Aromatic Nitro Compounds Acting as Mutagenic Agents on S. Typhimurium TA98. SAR QSAR Environ. Res. 4, 21 - 27.
Fan, M., Byrd, C., Compadre, C. M. and Compadre, R. L. (1998). Comparison of CoMFA models for Salmonella typhimurium TA98, TA100, TA98 + S9 and TA100 + S9 mutagenicity of nitroaromatics. SAR QSAR Environ. Res. 9, 187 - 215.
King, R., Muggleton, S., Srinivasan, A. and Sternberg, M. (1996). Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proc. Natl. Acad. Sci. USA 93, 438-442.
Klein, M., Voigtmann, U., Haack, T., Erdinger, L. and Boche, G. (2000a). From mutagenic to non-mutagenic nitroarenes: effect of bulky alkyl substituents on the mutagenic activity of 4-nitrobiphenyl in Salmonella typhimurium: Part I. Substituents ortho to the nitro group and in 2'-position. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 467, 55-68.
Martin, T. M., Harten, P., Young, D. M., Muratov, E. N., Golbraikh, A., Zhu, H. and Tropsha, A. (2012). Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling? J. Chem. Inf. Model. 52, 2570-2578.
Nair, P. C. and Sobhia, M. E. (2008). Comparative QSTR studies for predicting mutagenicity of nitro compounds. J. Mol. Graph. Model. 26, 916-934.
Takamura-Enya, T., Suzuki, H. and Hisamatsu, Y. (2006). Mutagenic activities and physicochemical properties of selected nitrobenzanthrones. Mutagenesis 21, 399-404.
Wang, X. D., Lin, Z. F., Yin, D. Q., Liu, S. S. and Wang, L. S. (2005). 2D/3D-QSAR comparative study on mutagenicity of nitroaromatics. Sci. China Ser. B. 48, 246-252.
Hou, T., Li, Y., Zhang, W. and Wang, J. (2009). Recent developments of in silico predictions of intestinal absorption and oral bioavailability. Comb. Chem. High Throughput Screen. 12, 497-506.
Benigni, R., Andreoli, C. and Giuliani, A. (1994). QSAR models for both mutagenic potency and activity: application to nitroarenes and aromatic amines. Environ. Mol. Mutagen. 24, 208-219.
Leong, M. K., Lin, S.-W., Chen, H.-B. and Tsai, F.-Y. (2010). Predicting Mutagenicity of Aromatic Amines by Various Machine Learning Approaches. Toxicol. Sci. 116, 498-513.
André, V., Boissart, C., Sichel, F., Gauduchon, P., Le Talaër, J. Y., Lancelot, J. C., Mercier, C., Chemtob, S., Raoult, E. and Tallec, A. (1997). Mutagenicity of nitro- and amino-substituted carbazoles in Salmonella typhimurium. III. Methylated derivatives of 9H-carbazole. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 389, 247-260.
André, V., Boissart, C., Sichel, F., Gauduchon, P., Le Talaër, J. Y., Lancelot, J. C., Robba, M., Mercier, C., Chemtob, S., Raoult, E. and Tallec, A. (1995). Mutagenicity of nitro- and amino-substituted carbazoles in Salmonella typhimurium. II. Ortho-aminonitro derivatives of 9H-carbazole. Mutat. Res.-Genet. Toxicol. 345, 11-25.
El-Bayoumy, K., Lavoie, E. J., Hecht, S. S., Fow, E. A. and Hoffmann, D. (1981). The influence of methyl substitution on the mutagenicity of nitronaphthalenes and nitrobiphenyls. Mutat. Res.-Fund. Mol. M. 81, 143-153.
Hooberman, B. H., Brezzell, M. D., Das, S. K., You, Z. and Sinsheimer, J. E. (1994). Substituent effects on the genotoxicity of 4-nitrostilbene derivatives. Mutat. Res. 341, 57-69.
Juneja, T. R., Kaur, B. and Gupta, R. L. (1991). Mutagenicity of 4-nitrodiphenyl thioether-derived products and their potential metabolites. Mutat. Res. Lett. 263, 13-19.
Juneja, T. R., Talukdar, A., Mehta, N. and Gupta, R. L. (2001). Effect of various alkyl and unsaturated substituents on the mutagenicity of some nitrophenyl thioethers. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 495, 97-102.
Jung, H., Heflich, R. H., Fu, P. P., Shaikh, A. U. and P. E. Hartman (1991). Nitro group orientation, reduction potential, and direct-acting mutagenicity of nitro-polycyclic aromatic hydrocarbons. Environ. Mol. Mutagen. 17, 169-180.
Klein, M., Erdinger, L. and Boche, G. (2000b). From mutagenic to non-mutagenic nitroarenes: effect of bulky alkyl substituents on the mutagenic activity of nitroaromatics in Salmonella typhimurium: Part II. Substituents far away from the nitro group. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 467, 69-82.
LaVoie, E. J., Govil, A., Briggs, G. and Hoffmann, D. (1981). Mutagenicity of aminocarbazoles and nitrocarbazoles. Mutat. Res. Genet. Toxicol. 90, 337-344.
Ludolph, B., Klein, M., Erdinger, L. and Boche, G. (2001). The effects of 4'-alkyl substituents on the mutagenic activity of 4-amino- and 4-nitrostilbenes in Salmonella typhimurium. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 491, 195-209.
Rosenkranz, H. S., McCoy, E. C., Frierson, M. and Klopman, G. (1985). The role of DNA sequence and structure of the electrophile on the mutagenicity of nitroarenes and arylamine derivatives. Environ. Mutagen. 7, 645-653.
Rosenkranz, H. S. and Mermelstein, R. (1985). The genotoxicity, metabolism and carcinogenicity of nitrated polycyclic aromatic hydrocarbons. J. Environ. Sci. Health C 3, 221-272.
Tokiwa, H., Nakagawa, R. and Ohnishi, Y. (1981). Mutagenic assay of aromatic nitro compounds with Salmonella typhimurium. Mutat. Res. Lett. 91, 321-325.
Tokiwa, H., Sera, N., Fukuhara, K., Utsumi, H., Sasaki, S. and Miyata, N. (2003). Structural activity relationship between Salmonella-mutagenicity and nitro-orientation of nitroazaphenanthrenes. Chem.-Biol. Interact. 146, 19-25.
Vance, W. A., Wang, Y. Y. and Okamoto, H. S. (1987). Disubstituted amino-, nitroso-, and nitrofluorenes: a physicochemical basis for structure-activity relationships in Salmonella typhimurium. Environ. Mutagen. 9, 123-141.
von Tungeln, L. S., Ewing, D. G., Weitkamp, R., Cheng, E., Herreno-Saenz, D., Evans, F. E. and Fu, P. P. (1994). Metabolic Activation of the Potent Mutagen and Tumorigen 2-Nitrobenzo[a]pyrene. Polycyclic Aromat. Compd. 7, 91-98.
Watanabe, T., Kaji, H., Kasai, T. and Hirayama, T. (1994). Metabolic activation of nitrodibenzofurans by rat liver in Salmonella/mutagenicity test. Mutat. Res. Lett. 325, 11-19.
Watanabe, T., Kaji, H., Takashima, M., Kasai, T., Lewtas, J. and Hirayama, T. (1997). Metabolic activation of 2- and 3-nitrodibenzopyranone isomers and related compounds by rat liver S9 and the effect of S9 on the mutational specificity of nitrodibenzopyranones. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 388, 67-78.
You, Z., Brezzell, M. D., Das, S. K., Espadas-Torre, M. C., Hooberman, B. H. and Sinsheimer, J. E. (1993). ortho-Substituent effects on the in vitro and in vivo genotoxicity of benzidine derivatives. Mutat. Res. Genet. Toxicol. 319, 19-30.
Yu, S., Herreno-Saenz, D., Miller, D. W., Kadlubar, F. F. and Fu, P. P. (1992). Mutagenicity of nitro-polycyclic aromatic hydrocarbons with the nitro substituent situated at the longest molecular axis. Mutat. Res. Lett. 283, 45-52.
Burden, F. R., Ford, M. G., Whitley, D. C. and Winkler, D. A. (2000). Use of automatic relevance determination in QSAR studies using Bayesian neural networks. J. Chem. Inf. Comput. Sci. 40, 1423-30.
Kettaneh, N., Berglund, A. and Wold, S. (2005). PCA and PLS with very large data sets. Comput. Stat. Da. An. 48, 69-85.
Leong, M. K., Chen, Y.-M., Chen, H.-B. and Chen, P.-H. (2009a). Development of a New Predictive Model for Interactions with Human Cytochrome P450 2A6 Using Pharmacophore Ensemble/Support Vector Machine (PhE/SVM) Approach. Pharm. Res. 26, 987-1000.
Golbraikh, A., Shen, M., Xiao, Z. Y., Xiao, Y. D., Lee, K. H. and Tropsha, A. (2003). Rational selection of training and test sets for the development of validated QSAR models. J. Comput.-Aid. Mol. Des. 17, 241-253.
Cortes, C. and Vapnik, V. (1995). Support-Vector Networks. Mach. Learn. 20, 273-297.
Schölkopf, B. and Smola, A. J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press.
Goldman, B. B. and Walters, W. P. (2006). Machine Learning in Computational Chemistry (C. S. David, ed., pp. 127-140. Elsevier.
Ivanciuc, O. (2007). Applications of Support Vector Machines in Chemistry. Rev. Comp. Ch. 23.
Noble, W. S. (2006). What is a support vector machine? Nat. Biotech. 24, 1565-1567.
Zhao, C. Y., Zhang, R. S., Liu, H. X., Xue, C. X., Zhao, S. G., Zhou, X. F., Liu, M. C. and Fan, B. T. (2004). Diagnosing Anorexia Based on Partial Least Squares, Back Propagation Neural Network, and Support Vector Machines. J. Chem. Inf. Comput. Sci. 44, 2040-2046.
Bao, L. and Sun, Z. (2002). Identifying genes related to drug anticancer mechanisms using support vector machine. FEBS Lett. 521, 109-114.
Cai, Y.-D., Liu, X.-J., Xu, X.-b. and Chou, K.-C. (2002). Prediction of protein structural classes by support vector machines. Comput. Chem. 26, 293-296.
Burbidge, R., Trotter, M., Buxton, B. and Holden, S. (2001). Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 26, 5-14.
Leong, M. K., Chen, Y.-M. and Chen, T.-H. (2009b). Prediction of Human Cytochrome P450 2B6-Substrate Interactions Using Hierarchical Support Vector Regression Approach. J. Comput. Chem. 30, 1899-1909.
Vapnik, V., Golowich, S. and Smola, A. (1997). Support vector method for function approximation, regression estimation, and signal processing (M. Mozer, M. I. Jordan and T. Petsche, Ed.^ Eds.), pp. 281-287. MIT Press, Cambridge, MA, USA.
Kecman, V. (2001). Learning and Soft Computing : Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press.
Breiman, L. and Spector, P. (1992). Submodel Selection and Evaluation in Regression. The X-Random Case. Int. Stat. Rev. 60, 291-319.
Netzeva, T. I., Worth, A., Aldenberg, T., Benigni, R., Cronin, M. T. D., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G., Perkins, R., Roberts, D. W., Schultz, T. W., Stanton, D. W., van de Sandt, J. J., Tong, W., Veith, G. and Yang, C. (2005). Current status of methods for defining the applicability domain of (Quantitative) structure-activity relationships : The report and recommendations of ECVAM workshop 52. Altern. Lab. Anim. 33, 19.
Benfenati, E., Chrétien, J. R., Gini, G., Piclin, N., Pintore, M. and Roncaglioni, A. (2007). Validation of the models (B. Emilio and B. Emilio, eds.), pp. 185-199. Elsevier, Amsterdam.
Roy, P. P. and Roy, K. (2008). On Some Aspects of Variable Selection for Partial Least Squares Regression Models. QSAR Comb. Sci. 27, 302-313.
Chirico, N. and Gramatica, P. (2012). Real External Predictivity of QSAR Models. Part 2. New Intercomparable Thresholds for Different Validation Criteria and the Need for Scatter Plot Inspection. J. Chem. Inf. Model. 52, 2044-2058.
Walker, J. D., Jaworska, J., Comber, M. H. I., Schultz, T. W. and Dearden, J. C. (2003). Guidelines for developing and using quantitative structure-activity relationships. Environ. Toxicol. Chem. 22, 1653-1665.
Gnanadesikan, R. and Kettenring, J. R. (1972). Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics 28, 81-124.
Pederson, T. C. and Siak, J. S. (1981). The role of nitroaromatic compounds in the direct-acting mutagenicity of diesel particle extracts. J. Appl. Toxicol. 1, 54-60.
Consonni, V., Ballabio, D. and Todeschini, R. (2009). Comments on the Definition of the Q2 Parameter for QSAR Validation. J. Chem. Inf. Model. 49, 1669-1678.
Schüürmann, G., Ebert, R.-U., Chen, J., Wang, B. and Kühne, R. (2008). External Validation and Prediction Employing the Predictive Squared Correlation Coefficient-Test Set Activity Mean vs Training Set Activity Mean. J. Chem. Inf. Model. 48, 2140-2145.
Ojha, P. K., Mitra, I., Das, R. N. and Roy, K. (2011). Further exploring rm2 metrics for validation of QSPR models. Chemometrics Intell. Lab. Syst. 107, 194-205.

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