A Hierarchical Clustering Methodology for the Estimation of Toxicity

REFERENCES

• Basak S. C., Mills D. R., Balaban A. T., Gute B. D. Prediction of mutagenicity of aromatic and heteroaromatic amines from structure: a hierarchical QSAR approach. J. Chem. Inf. Comput. Sci. 2001; 41: 671–678
   PubMedGoogle Scholar
• Belsley D. A., Kuh E., Welsch R. E. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, New York 1980; 157
   Google Scholar
• Cash G. G. Prediction of the genotoxicity of aromatic and heteroaromatic amines using electrotopological state indices. Mutat. Res. 2001; 491: 31–37
   PubMed Web of Science ®Google Scholar
• Cherkasov A. Inductive QSAR descriptors Distinguishing compounds with antibacterial activity by artificial neural networks. Int. J. Mol. Sci. 2005; 6: 63–86
   Web of Science ®Google Scholar
• Contrera J. F., Matthews E. J., Benz R. D. Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices. Reg. Toxicol. Pharmacol. 2003; 38: 243–259
   PubMed Web of Science ®Google Scholar
• Cronin M. T. D., Aptula A. O., Duffy J. C., Netzeva T. I., Rowe P. H., Valkova I. V., Schultz T. W. Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis. Chemosphere 2002; 49: 1201–1221
   PubMed Web of Science ®Google Scholar
• Cronin M. T. D., Schultz T. W. Structure-toxicity relationships for phenols to Tetrahymena pyriformis. Chemosphere 1996; 32(8)1453–1468
   PubMed Web of Science ®Google Scholar
• Damborski J., Schultz T. W. Comparison of the QSAR models for toxicity and biodegradability of anilines and phenols. Chemosphere 1997; 34(2)429–446
   PubMed Web of Science ®Google Scholar
• Daylight Chemical Information Systems, Inc. Simplified Molecular Input Line Entry System. 2006, http://www.daylight.com/smiles/index.html. Accessed January 24, 2008.
   Google Scholar
• Edusoft-LC. Molconn-z Version 4.0. 2006, http://www.edusoft-lc.com/molconn./ Accessed January 24, 2008
   Google Scholar
• Eldred D. V., Weikel C. L., Jurs P. C., Kaiser K. L. E. Prediction of fathead minnow acute toxicity of organic compounds from molecular structure. Chem. Res. Toxicol. 1999; 12: 670–678
   PubMed Web of Science ®Google Scholar
• Elsevier MDL. CTFile Formats. 2005, http://www.mdl.com/solutions/white_papers/ctfile_formats.jsp. Accessed January 24, 2008.
   Google Scholar
• Elsevier MDL. MDL QSAR Version 2.2. 2006, http://www.mdl.com/products/predictive/qsar/index.jsp. Accessed January 24, 2008.
   Google Scholar
• Eriksson L., Jaworska J. S., Worth A. P., Cronin M. T. D., McDowell R. M., Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ. Health Perspect. 2003; 111(10)1361–1375
   PubMed Web of Science ®Google Scholar
• Fatemi M. H., Jalali-Heravi M., Konuze E. Prediction of bioconcentration factor using genetic algorithm and artificial neural network. Anal. Chim. Acta 2003; 486: 101–108
   Web of Science ®Google Scholar
• Faucon J. C., Bureau R., Faisant J., Briens F., Rault S. Prediction of the fish acute toxicity from heterogeneous data coming from notification files. Chemosphere 1999; 38: 3261–3276
   PubMed Web of Science ®Google Scholar
• Ghose A. K., Viswanadhan V. N., Wendoloski J. J. Prediction of hydrophilic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of ALOGP and CLOGP Methods. J. Phys. Chem. 1998; 102: 3762–3772
   Web of Science ®Google Scholar
• Gough J. D., Hall L. H. Modeling the toxicity of amide herbicides using the electrotopological state. Environ. Toxicol. Chem. 1999; 18: 1069–1075
   Web of Science ®Google Scholar
• Gramatica P., Corradi M., Consonni V. Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors. Chemosphere 2000; 41: 763–777
   PubMed Web of Science ®Google Scholar
• Gramatica P., Pilutti P., Papa E. Predicting the NO3 radical tropospheric degradability of organic pollutants by theoretical molecular descriptors. Atmospheric Environ. 2003; 37: 3115–3124
   Web of Science ®Google Scholar
• Gramatica P., Vighi M., Consolaro F., Todeschini R., Finizio A., Faust M. QSAR approach for the selection of congeneric compounds with a similar toxicological mode of action. Chemosphere 2001; 42: 873–883
   PubMed Web of Science ®Google Scholar
• Hall L. H., Kier L. B. The molecular connectivity chi indices and kappa shape indices in structure-property relations. Reviews of Computational Chemistry, D. Boyd, K. Lipkowitz. VCH Publishers, Inc., New York 1991; 367–422
   Google Scholar
• Hall L. H., Kier L. B. Molecular connectivity chi indices for database analysis and structure-property modeling. Topological indices and related descriptors in QSAR and QSPR, J. Devillers, A. T. Balaban, ReadingUK: Gordon & Breach 1999
   Google Scholar
• He L., Jurs P. C. Assessing the Reliability of QSAR model's predictions. J. Mol. Graphics Model. 2005; 23: 503–523
   PubMed Web of Science ®Google Scholar
• Hosoya H. Topological index. A newly proposed quantity characterizing the topological nature of structural isomers of saturated hydrocarbons. Bull. Chem. Soc. Jap. 1971; 44: 2332–2339
   Web of Science ®Google Scholar
• Itskowitz P., Tropsha A. k nearest neighbors QSAR modeling as a variational problem: theory and applications. J. Chem. Infor. Model. 2005; 45: 777–785
   PubMed Web of Science ®Google Scholar
• Kaiser K. L. E., Niculescu S. P. Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas): a study based on 865 chemicals. Chemosphere 1999; 38: 3237–3245
   PubMed Web of Science ®Google Scholar
• Katritzky A. R., Karelson M., Putrukhin R. The CODESSA PRO project: Comprehensive Descriptors for Structural and Statistical Analysis 2007, http://www.codessa-pro.com/. Accessed January 25, 2008
   Google Scholar
• Kier L. B., Hall L. H. Molecular Connectivity in Chemistry and Drug Research. Academic Press Inc, New York 1976
   Google Scholar
• Kier L. B., Hall L. H. Molecular Connectivity in Structure-Activity Analysis. John Wiley and Sons, New York 1986
   Google Scholar
• Kutner M. H., Nachtsheim C. J., Neter J., Li W. Applied Linear Statistical Models. McGraw-Hill, New York 2004
   Google Scholar
• 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) vector, λ. J. Chem. Inf. Comput. Sci. 1998; 38: 387–394
   Google Scholar
• Louis Pasteur University. ISIDA Project. 2007, http://infochim.u-strasbg. fr/recherche/isida/index.php. Accessed January 25, 2008.
   Google Scholar
• Martin T. M., Young D. M. Prediction of the acute toxicity (96-h LC50) of organic compounds ti the fathead minnow (Pimephales promelas) using a group contribution method. Chem. Res. Toxicol. 2001; 14: 1378–1385
   PubMed Web of Science ®Google Scholar
• Montgomery D. C. Introduction to Linear Regression Analysis. John Wiley and Sons, New York 1982; 141: 143
   Google Scholar
• Öberg T. A general structure-property relationship to predict the enthalpy of vaporisation at ambient temperatures. SAR QSAR Environ. Res. 2007; 18: 127–139
   PubMed Web of Science ®Google Scholar
• Papa E., Villa F., Gramatica P. Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow). J. Chem. Infor. Model. 2005; 45: 1256–1266
   PubMed Web of Science ®Google Scholar
• Pirselova K., Balaz S., Schultz T. W. Model-based QSAR for ionizable compounds: toxicity of phenols against Tetrahymena pyriformis. Arch. Environ. Contam. Toxicol. 1996; 30(2)170–177
   PubMed Web of Science ®Google Scholar
• Romesburg H. C. Cluster Analysis for Researchers. Lifetime Learning Publications, BelmontCA 1984
   Google Scholar
• Russom C. L., Bradbury S. P., Broderius S. J., Hammermeister D. E., Drummond R. A. Predicting modes of toxic action from chemical structure: acute toxicity in the Fathead minnow (Pimephales Promelas). Environ. Toxicol. Chem. 1997; 16: 948–967
   Web of Science ®Google Scholar
• Schultz T. W. TETRATOX: Tetrahymena pyriformis population growth impairment endpoint—a surrogate for fish lethality. Toxicol. Methods 1997; 7: 289–309
   Google Scholar
• Schultz T. W. Tetratox. 2007, http://www.vet.utk.edu/TETRATOX/. Accessed January 25, 2008.
   Google Scholar
• Schultz T. W., Bearden A. P., Jaworska J. S. A novel approach for estimating the toxicity of phenols. SAR QSAR Environ. Res. 1996; 5(2)99–112
   PubMedGoogle Scholar
• Schultz T. W., Netzeva T. I. Development and evaluation of QSARs for ecotoxic endpoints: the benzene response-surface model for tetrahymena toxicity. Modeling Environmental Fate and Toxicity, M. T. D. Cronin, D. J. Livingstone. CRC Press, Boca RatonFL 2004
   Google Scholar
• Steinbeck C., Han Y., Kuhn S., Horlacher O., Luttmann E., Willighagen E. The Chemistry Development Kit (CDK): an open-source Java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci. 2003; 43: 493–500
   PubMedGoogle Scholar
• Sutter J. M., Dixon S. L., Jurs P. C. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. J. Chem. Inf. Comput. Sci. 1995; 35: 77–84
   Google Scholar
• Talete. Dragon Version 5.4. 2006, http://www.talete.mi.it/dragon_net.htm. Accessed January 25, 2008.
   Google Scholar
• Todeschini R., Consonni V. Handbook of Molecular Descriptors. Wiley-VCH, WeinheimGermany 2000
   Google Scholar
• Todeschini R., Vighi M., Finizio A., Gramatica P. 3D modelling and prediction by WHIM descriptors. Part 8. Toxicity and physicochemical properties of environmental priority chemicals by 2D-TI and 3D WHIM descriptors. SAR QSAR Environ. Res. 1997; 7: 173–193
   PubMedGoogle Scholar
• U.S. Environmental Protection Agency. ECOSAR, version 0.99 g. 2000, http://www.epa.gov/oppt/newchems/tools/21ecosar.htm. Accessed January 25, 2008.
   Google Scholar
• U.S. Environmental Protection Agency. TSCA §5 New Chemicals Program. 2007, http://www.epa.gov/oppt/newchems/index.htm. Accessed January 25, 2008.
   Google Scholar
• Verhaar H. J. M., van Leeuwen C. J., Hermens J. L. M. 1: Structure-activity relationships for prediction of aquatic toxicity. Chemosphere 1992; 25: 471–491
   Web of Science ®Google Scholar
• Virtual Computational Chemistry Laboratory. Associative Neural Network. 2007, http://www.vcclab.org/lab/asnn/. Accessed January 25, 2008.
   Google Scholar
• Viswanadhan 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. Chem. Inf. Comput. Sci. 1989; 29: 163–172
   Google Scholar
• Waikato Environment for Knowledge Analysis WEKA. 2007, http://www.cs.waikato.ac.nz/~ml/weka/ Accessed January 25, 2008.
   Google Scholar
• Wang R., Fu Y., Lai L. A new atom-additive method for calculating partition coefficients. J. Chem. Inf. Comput. Sci. 1997; 37: 615–621
   Google Scholar
• Witten I. H. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco 2005
   Google Scholar
• Xue C. X., Zhang R. S., Liu H. X., Yao X. J., Liu M. C., Hu Z. D., Fan B. T. An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines. J. Chem. Inf. Comput. Sci. 2004; 44: 669–677
   PubMedGoogle Scholar
• Zhang S., Golbraikh A., Oloff S., Kohn H., Tropsha A. A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J. Chem. Inf. Model 2006; 46: 1984–1995
   PubMed Web of Science ®Google Scholar
• Zheng W. F., Tropsha A. Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. J. Chem. Inf. Comput. Sci. 2000; 40: 185–194
   PubMedGoogle Scholar
• Zhu H., Tropsha A., Fourches D., Varnek A., Papa E., Gramatica P., Öberg T., Dao P., Cherkasov A., Tetko I. G. Combinational QSAR model of chemical toxicants tested against. Tetrahymena pyriformis. J. Chem. Inf. Model. 2008
   Google Scholar