Comparative study to predict toxic modes of action of phenols from molecular structures

References

• Garg , R. , Kurup , A. and Hansch , C. 2001 . Comparative QSAR: On the toxicology of the phenolic OH moiety . Crit. Rev. Toxicol , 31 : 223 – 245 .
   PubMed Web of Science ®Google Scholar
• Cronin , M.T. , Aptula , A.O. , Duffy , J.C. , Netzeva , T.I. , Rowe , P.H. , Valkova , I.V. and Schultz , T.W. 2002 . Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis . Chemosphere , 49 : 1201 – 1221 .
   PubMed Web of Science ®Google Scholar
• Caza , N. , Bewtra , J.K. , Biswas , N. and Taylor , K.E. 1999 . Removal of phenolic compounds from synthetic wastewater using soybean peroxidase . Water Res , 33 : 3012 – 3018 .
   Web of Science ®Google Scholar
• Auer , C.M. , Nabholz , J.V. and Baetcke , K.P. 1990 . Mode of action and the assessment of chemical hazards in the presence of limited data: Use of structure-activity relationships (SAR) under TSCA, Section 5 . Environ. Health Perspect , 87 : 183 – 197 .
   PubMed Web of Science ®Google Scholar
• Ivanciuc , T. , Ivanciuc , O. and Klein , D.J. 2005 . Posetic quantitative superstructure/activity relationships (QSSARs) for chlorobenzenes . J. Chem. Inf. Model. , 45 : 870 – 879 .
   PubMed Web of Science ®Google Scholar
• Spycher , S. , Escher , B.I. and Gasteiger , J. 2005 . A quantitative structure–activity relationship model for the intrinsic activity of uncouplers of oxidative phosphorylation . Chem. Res. Toxicol. , 18 : 1858 – 1867 .
   PubMed Web of Science ®Google Scholar
• Bradbury , S.P. 1994 . Predicting modes of toxic action from chemical structure: An overview . SAR QSAR Environ. Res. , 2 : 89 – 104 .
   PubMedGoogle Scholar
• Russom , C.L. , Bradbury , S.P. , Broderius , S.J. , Hammermeister , D. and Drummond , R.A. 1997 . Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas) . Environ. Toxicol. Chem. , 16 : 948 – 967 .
   Web of Science ®Google Scholar
• Aptula , A.O. , Netzeva , T.I. , Valkova , I.V. , Cronin , M.T.D. and Schultz , T.W. 2002 . Multivariate discrimination between modes of toxic action of phenols . Quant. Struct. Act. Relat. , 21 : 12 – 22 .
   Google Scholar
• Ren , S. 2002 . Determining the mechanisms of toxic action of phenols to Tetrahymena pyriformis . Environ. Toxicol. , 17 : 119 – 127 .
   PubMed Web of Science ®Google Scholar
• Ren , S. 2002 . Use of molecular descriptors in separating phenols by three mechanisms of toxic action . Quant. Struct. Act.-Relat. , 21 : 486 – 492 .
   Google Scholar
• Spycher , S. , Pellegrini , E. and Gasteiger , J. 2005 . Use of structure descriptors to discriminate between modes of toxic action of phenols . J. Chem. Inf. Model. , 45 : 200 – 208 .
   PubMed Web of Science ®Google Scholar
• Schuurmann , G. , Aptula , A.O. , Kuhne , R. and Ebert , R.U. 2003 . Stepwise discrimination between four modes of toxic action of phenols in the Tetrahymena pyriformis assay . Chem. Res. Toxicol. , 16 : 974 – 987 .
   PubMed Web of Science ®Google Scholar
• Bradbury , S.P. , Russom , C.L. , Ankley , G.T. , Schultz , T.W. and Walker , J.D. 2003 . Overview of data and conceptual approaches for derivation of quantitative structure-activity relationships for ecotoxicological effects of organic chemicals . Environ. Toxicol. Chem. , 22 : 1789 – 1798 .
   PubMed Web of Science ®Google Scholar
• Ren , S. , Frymier , P.D. and Schultz , T.W. 2003 . An exploratory study of the use of multivariate techniques to determine mechanisms of toxic action . Ecotoxicol. Environ. Saf. , 55 : 86 – 97 .
   PubMed Web of Science ®Google Scholar
• Ren , S. and Kim , H. 2003 . Comparative assessment of multiresponse regression methods for predicting the mechanisms of toxic action of phenols . J. Chem. Inf. Comput. Sci , 43 : 2106 – 2110 .
   PubMedGoogle Scholar
• Norinder , U. , Liden , P. and Bostrom , H. 2006 . Discrimination between modes of toxic action of phenols using rule based methods . Mol. Divers. , 10 : 207 – 212 .
   PubMed Web of Science ®Google Scholar
• Guo , J. , Chen , H. , Sun , Z. and Lin , Y. 2004 . A novel method for protein secondary structure prediction using dual-layer SVM and profiles . Proteins , 54 : 738 – 743 .
   PubMed Web of Science ®Google Scholar
• Klopman , G. 1984 . Artificial intelligence approach to stucture-activities studies. Computer automated structure evaluation of biological activity of organic molecules . J. Am. Chem. Soc. , 106 : 7315 – 7321 .
   Web of Science ®Google Scholar
• Zupan , J. and Gasteiger , J. 1999 . Neural Networks for Chemistry and Drug Design , Weinheim (Germany) : Wiley-VCH Publishers .
   Google Scholar
• Quinlan , J.R. 1986 . Induction of decision trees . Machine Learn. , 1 : 81 – 106 .
   Google Scholar
• Cover , T. and Hart , P. 1967 . Nearest neighbor pattern classification . IEEE Trans. Inform. Theory , 13 : 21 – 27 .
   Web of Science ®Google Scholar
• Aha , D. and Kibler , D. 1991 . Instance-based learning algorithms . Machine Learn. , 6 : 37 – 66 .
   Web of Science ®Google Scholar
• R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, 1st ed., Vol. 11, WILEY-VCH Verlag GmbH, D-69469 Weinheim, Federal Republic of Germany, 2000, p. 667.
   Google Scholar
• Larrañaga , P. , Calvo , B. , Santana , R. , Bielza , C. , Galdiano , J. , Inza , I. , Lozano , J.A. , Armañanzas , R. , Santafé , G. , Pérez , A. and Robles , V. 2006 . Machine learning in bioinformatics . Brief. Bioinform. , 7 : 86 – 112 .
   PubMed Web of Science ®Google Scholar
• Y. Marrero-Ponce and V. Romero, TOMOCOMD software (TOpological MOlecular COMputer Design) for Windows, 1.0 ed., Central University of Las Villas. Villa Clara. Cuba, 2002. A preliminary experimental version; in future a professional version can be obtained upon request to Y. Marrero: [email protected] or [email protected]
   Google Scholar
• Casanola-Martin , G.M. , Marrero-Ponce , Y. , Khan , M.T. , Ather , A. , Khan , K.M. , Torrens , F. and Rotondo , R. 2007 . Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays . Eur. J. Med. Chem. , 42 : 1370 – 1381 .
   PubMed Web of Science ®Google Scholar
• Castillo-Garit , J.A. , Marrero-Ponce , Y. , Torrens , F. and García-Domenech , R. 2008 . Estimation of ADME properties in drug discovery: Predicting Caco-2 cell permeability using atom-based stochastic and non-stochastic linear indices . J. Pharm. Sci. , 97 : 1946 – 1976 .
   PubMedGoogle Scholar
• Castillo-Garit , J.A. , Vega , M.C. , Rolón , M. , Marrero-Ponce , Y. , Gómez-Barrio , A. , Escario , J.A. , Bello , A.A. , Montero , A. , Torrens , F. , Pérez-Giménez , F. , Arán , V.J. and Abad , C. 2011 . Ligand-based discovery of novel trypanosomicidal drug-like compounds: In silico identification and experimental support . Europ. J. Med. Chem. , 46 : 3324 – 3330 .
   PubMedGoogle Scholar
• Le-Thi-Thu , H. , Cardoso , G.C. , Casañola-Martín , G.M. , Marrero-Ponce , Y. , Puris , A. , Torrens , F. , Rescigno , A. and Abad , C. 2010 . QSAR models for tyrosinase inhibitory activity description applying modern statistical classification techniques: A comparative study . Chemometr. Intell. Lab. Syst. , 104 : 249 – 259 .
   Google Scholar
• Castillo-Garit , J.A. , Vega , M.C. , Rolon , M. , Marrero-Ponce , Y. , Kouznetsov , V. , Torres , D.F. , Gómez-Barrio , A. , Alvarez , A. , Montero , A. , Torrens , F. and Pérez-Giménez , F. 2010 . Computational discovery of novel trypanosomicidal drug-like chemicals by using bond-based non-stochastic and stochastic quadratic maps and linear discriminant analysis . Eur. J. Pharm. Sci. , 39 : 30 – 36 .
   PubMedGoogle Scholar
• Castillo-Garit , J.A. , Marrero-Ponce , Y. , Escobar , J. , Torrens , F. and Rotondo , R. 2008 . A novel approach to predict aquatic toxicity from molecular structure . Chemosphere , 73 : 415 – 427 .
   PubMed Web of Science ®Google Scholar
• Castillo-Garit , J.A. , Abad , C. , Rodriguez-Borges , J.E. , Marrero-Ponce , Y. and Torrens , F. 2012 . A review of QSAR studies to discover new drug-like compounds active against Leishmaniasis and Trypanosomiasis . Curr. Top. Med. Chem. , 12 : 852 – 865 .
   PubMedGoogle Scholar
• Castillo-Garit , J.A. , Marrero-Ponce , Y. , Torrens , F. and Rotondo , R. 2007 . Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification . J. Mol. Graphics Model. , 26 : 32 – 47 .
   PubMed Web of Science ®Google Scholar
• Castillo-Garit , J.A. , Marrero-Ponce , Y. , Torrens , F. , García-Domenech , R. and Romero-Zaldivar , V. 2008 . Bond-based 3D-chiral linear indices: Theory and QSAR applications to central chirality codification . J. Comput. Chem. , 29 : 2500 – 2512 .
   PubMed Web of Science ®Google Scholar
• Castillo-Garit , J.A. , Martinez-Santiago , O. , Marrero Ponce , Y. , Casañola-Martin , G.M. and Torrens , F. 2008 . Atom-based non-stochastic and stochastic bilinear indices: Application to QSPR/QSAR studies of organic compounds . Chem. Phys. Lett. , 464 : 107 – 112 .
   Web of Science ®Google Scholar
• Schultz , T.W. 1997 . TERATOX: Tetrahymena pyriformis population grow impairment endpoint- A surrogate for fish lethality . Toxicol. Meth. , 7 : 289 – 309 .
   Google Scholar
• Wolpert , D.H. and Macready , W.G. 1997 . No free lunch theorems for optimization . IEEE Trans. Evol. Comput. , 1 : 67 – 82 .
   Google Scholar
• C. Platt. J, Fast training of support vector machines using sequential minimal optimization, in Advances in Kernel Methods, MIT Press, 1999, pp. 185–208.
   Google Scholar
• R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, 1993.
   Google Scholar
• R. Bisquerra Alzina, Introduccion conceptual al análisis multivariable. Un enfoque informatico con los paquetes SPSS-X, BMDP, LISREL Y SPAD, Vol. 1, PPU, SA, Barcelona, 1989, pp. 202–264.
   Google Scholar
• van de Waterbeemd , H. 1995 . Chemometric Methods in Molecular Design (Methods and Principles in Medicinal Chemistry) , New York : John Wiley & Sons .
   Google Scholar
• Witten , I.H. and Frank , E. 2005 . Data Mining: Practical Machine Learning Tools and Techniques, Second ed. , Morgan Kaufmann Publishers .
   Google Scholar
• STATISTICA version. 6.0, StatSoft, (2001), pp. Tulsa.
   Google Scholar
• Pierre , B. , Brunak , S. , Chauvin , Y. , Andersen , C.A.F. and Henrick , N. 2000 . Assessing the accuracy of prediction algorithms for classifcation: An overview . Bioinformatics , 16 : 412 – 424 .
   PubMed Web of Science ®Google Scholar
• F. Provost and T. Fawcett, Analysis and visualization of classifier performance: Comparison under imprecise class and cost distribution, Third International Conference on Knowledge Discovery and Data Mining (KDD-97), J.W. Shavlik, ed., Newport Beach, California, August 14-17 1997, AAAI Press, Menlo Park, CA, pp. 43–48.
   Google Scholar
• F. Provost, T. Fawcett, and R. Kohavi, The case against accuracy estimation for comparing induction algorithms, Fifteenth International Conference on Machine Learning, J.W. Shavlik, ed., 1998, Morgan Kaufmann, pp. 445–453.
   Google Scholar
• T. Fawcett, ROC graphs: Notes and practical considerations for researchers, Machine Learn. (2004), pp. 1–38.
   Google Scholar
• C.X. Ling, J. Huang, H. Zhang, AUC: A statistically consistent and more discriminating measure than accuracy, Proceedings of 18th International Conference on, Artificial Intelligence (IJCAI-2003) (2003), pp. 519–524.
   Google Scholar
• A.P. Bradley., The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognit. 30 (1997), pp. 1145–1159.
   Google Scholar
• Hanley , J.A. and Mcneil , B.J. 1982 . The meaning and use of the area under a receiver operating characteristic (ROC) curve . Radiology , 143 : 29 – 36 .
   PubMed Web of Science ®Google Scholar
• Toxtree, version 1.60 software available at http://ihcp.jrc.ec.europa.eu/our_labs/predictive_toxicology/qsar_tools/toxtree
   Google Scholar
• Verhaar , H.J.M. , van Leeuwen , C.J. and Hermens , J.L.M. 1992 . Classifying environmental pollutants . Chemosphere , 25 : 471 – 491 .
   Web of Science ®Google Scholar
• L.I. Kuncheva, Combining pattern classifiers, in Methods and Algorithms, Wiley Interscience, 2004.
   Google Scholar
• Dietterrich , T.G. 1857 . Ensemble methods in machine learning . Lect. Not. Comput. Sci. , 2000 : 1 – 15 .
   Google Scholar
• Jaworska , J. , Nikolova-Jeliazkova , N. and Aldenberg , T. 2005 . QSAR applicabilty domain estimation by projection of the training set descriptor space. A review . Altern. Lab. Anim. , 33 : 445 – 459 .
   PubMed Web of Science ®Google Scholar