Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods

References

• Al-Saleh , I. , Arif , J. , Ei-Doush , I. , Al-Sanea , N. , Abdul Jabbar , A. , Billedo , G. , Shinwari , N. , Mashhour , A. and Mohamed , G. 2008 . Carcinogen DNA adducts and the risk of colon cancer: Case–control study . Biomarkers , 13 : 201 – 216 .
   PubMed Web of Science ®Google Scholar
• Phillips , D.H. 2005 . DNA adducts as markers of exposure and risk . Mutation Res. , 577 : 284 – 292 .
   PubMed Web of Science ®Google Scholar
• Wang , D. and Lippard , S.J. 2005 . Cellular processing of platinum anticancer drugs . Nat. Rev. Drug Discov. , 4 : 307 – 320 .
   PubMed Web of Science ®Google Scholar
• Peltonen , K. and Dipple , A. 1995 . Polycyclic aromatic hydrocarbons: Chemistry of DNA adducts formation . J. Environ. Med. , 37 : 52 – 58 .
   Google Scholar
• Kriek , E. , Rojas , M. , Alexandrov , K. and Bartsch , H. 1998 . Polycyclic aromatic hydrocarbon-DNA adducts in humans: Relevance as biomarkers for exposure and cancer risk . Mutation Res. , 400 : 215 – 231 .
   PubMed Web of Science ®Google Scholar
• Golding , B.T. and Watson , W.P. 1999 . Possible mechanisms of carcinogenesis after exposure to benzene . IARC Sci. Publ. , 150 : 75 – 88 .
   PubMedGoogle Scholar
• Wu , M. , Yan , S. , Patel , D.J. , Geacintov , N.E. and Broyde , S. 2002 . Relating repair susceptibility of carcinogen-damaged DNA with structural distortion and thermodynamic stability . Nucleic Acids Res. , 30 : 3422 – 3432 .
   PubMedGoogle Scholar
• Kulkarni , S.A. , Moir , D. and Zhu , J. 2007 . Influence of structural and functional modifications of selected genotoxic carcinogens on the metabolism and mutagenicity . SAR QSAR Environ. Res. , 18 : 459 – 514 .
   PubMed Web of Science ®Google Scholar
• Marnett , L.J. 1999 . Lipid peroxidation-DNA damage by malondialdehyde . Mutation Res. , 424 : 83 – 95 .
   PubMed Web of Science ®Google Scholar
• Carrier , E.J. , Amarnath , V. , Oates , J.A. and Boutaud , O. 2009 . Characterization of covalent adducts of nucleosides and DNA formed by reaction with levuglandin . Biochemistry , 48 : 10775 – 10781 .
   PubMedGoogle Scholar
• Benigni , R. , Conti , L. , Crebelli , R. , Rodomonte , A. and Vari , M.R. 2005 . Simple and α,β-unsaturated aldehydes: Correct prediction of genotoxic activity through structure–activity relationship models . Environ. Mol. Mutagen. , 19 : 338 – 345 .
   Google Scholar
• Vogel , E.W. and Nivard , M.J.M. 1994 . The subtlety of alkylating agents in reactions with biological macromolecules . Mutation Res. , 395 : 13 – 32 .
   Google Scholar
• Coluci , V.R. , Vendrame , R. , Braga , R.S. and Galvao , D.S. 2002 . Identifying relevant molecular descriptors related to carcinogenic activity of polycyclic aromatic hydrocarbons (PAHs) using pattern recognition methods . J. Chem. Inf. Comput. Sci. , 42 : 1479 – 1489 .
   PubMedGoogle Scholar
• Dash , M. , Choi , K. , Scheuermann , P. and Liu , H. 2000 . Feature selection for clustering – a filter solution , Proceedings of the Second International Conference on Data Mining 115 – 122 .
   Google Scholar
• Hall , M.A. 2000 . “ Correlation-based feature selection for discrete and numeric class machine learning ” . Proceedings of the 17th International Conference on Machine Learning 359 – 366 .
   Google Scholar
• Liu , H. and Setiono , R. 1996 . “ A probabilistic approach to feature selection – a filter solution ” . Proceedings of the 13th International Conference on Machine Learning (ICML'96) 319 – 327 . Italy : Bari .
   Google Scholar
• Caruana , R. and Freitag , D. 1994 . Greedy attribute selection , Proceedings of the 11th International Conference on Machine Learning 28 – 36 .
   Google Scholar
• Dy , J.G. and Brodley , C.E. 2000 . “ Feature subset selection and order identification for unsupervised learning ” . Proceedings of the 17th International Conference on Machine Learning 247 – 254 . San Francisco, CA : Morgan Kaufmann .
   Google Scholar
• Kim , Y. , Street , W. and Menczer , F. 2000 . “ Feature selection in unsupervised learning via evolutionary search ” . AAAI: Proceedings of the Sixth ACM SIGKDD Conference on Knowledge Discovery and Data Mining 365 – 369 .
   Google Scholar
• Leung , Y. and Hung , Y. 2010 . A multiple-filter-multiple-wrapper approach to gene selection and microarray data classification . IEEE/ACM Trans. Comput. Biol. Bioinform. , 7 : 108 – 117 .
   PubMedGoogle Scholar
• Li , H. , Ung , C.Y. , Yap , C.W. , Xue , Y. , Li , Z.R. , Cao , Z.W. and Chen , Y.Z. 2005 . Prediction of genotoxicity of chemical compounds by statistical learning . Chem. Res. Toxicol. , 18 : 1071 – 1080 .
   PubMed Web of Science ®Google Scholar
• Chen , S.W. , Li , Z.R. and Li , X.Y. 2005 . Prediction of antifungal activity by support vector machine approach . J. Mol. Struct. (THEOCHEM) , 731 : 73 – 81 .
   Web of Science ®Google Scholar
• Ajmani , S. , Jadhav , K. and Kulkarni , S.A. 2006 . Three-dimensional QSAR using the k-nearest neighbor method and its interpretation . J. Chem. Inf. Model. , 46 : 24 – 31 .
   PubMed Web of Science ®Google Scholar
• Kohavi , R. and John , G.H. 1997 . Wrappers for feature subset selection . Artif. Intell. , 97 : 273 – 324 .
   Web of Science ®Google Scholar
• Matsuda , T. , Terashima , I. , Matsumoto , Y. , Yabushita , H. , Matsui , S. and Shibutani , S. 1999 . Effective utilization of N2-ethyl-2′-deoxyguanosine triphosphate during DNA synthesis catalyzed by mammalian replicative DNA polymerases . Biochemistry , 38 : 929 – 935 .
   PubMed Web of Science ®Google Scholar
• Chang , H.F. , Huffer , D.M. , Chiarelli , M.P. , Blankenship , L.R. , Culp , S.J. and Cho , B.P. 2002 . Characterization of DNA adducts derived from syn-benzo[ghi]fluoranthene-3,4-dihydrodiol-5,5a-epoxide and comparative DNA binding studies with structurally-related anti-diolepoxides of benzo[ghi]fluoranthene and benzo[c]phenanthrene . Chem. Res. Toxicol. , 15 : 198 – 208 .
   PubMed Web of Science ®Google Scholar
• Eberhard , S. , Karen , I. , Hirsch , E. , Ekkehard , S. , Georg , F.K. and Heidi , F. 2000 . Metabolism of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) in primary cultures of rat alveolar type II cells . Drug Metab. Dispos. , 28 : 180 – 184 .
   PubMedGoogle Scholar
• Hecht , S.S. 2001 . Preparation of pyridine-N-glucuronides of tobacco-specific nitrosamines . Chem. Res. Toxicol. , 14 : 555 – 561 .
   PubMedGoogle Scholar
• Newman , M.J. , Light , B.A. , Weston , A. , Tollurud , D. , Clark , J.L. , Mann , D.L. , Blackmon , J.P. and Harris , C.C. 1988 . Detection and characterization of human serum antibodies to polycyclic aromatic hydrocarbon diol-epoxide DNA adducts . J. Clin. Invest. , 82 : 145 – 153 .
   PubMed Web of Science ®Google Scholar
• Lacy , C.F. , Armstrong , L.L. , Goldman , M.P. and Lance , L.L. 2004 . Drug Information Handbook , 10th Anniversary ed. , Vol. 2003 , Hudson, Cleveland : Lexicomp .
   Google Scholar
• Todeschini , R. and Consonni , V. 2000 . Handbook of Molecular Descriptors , 50 – 70 . Weinheim : Wiley-VCH .
   Google Scholar
• Katritzky , A.R. and Gordeeva , E.V. 1993 . Electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research . J. Chem. Inf. Comput. Sci. , 33 : 835 – 857 .
   PubMedGoogle Scholar
• Kier , L.B. and Hall , L.H. 1999 . Molecular Structure Description: The Electrotopological State , San Diego : Academic Press .
   Google Scholar
• Karelson , M. , Lobanov , V.S. and Katritzky , A.R. 1996 . Quantum-chemical descriptors in QSAR/QSPR studies . Chem. Rev. , 96 : 1027 – 1043 .
   PubMed Web of Science ®Google Scholar
• Li , Z.R. , Han , L.Y. , Xue , Y. , Yap , C.W. , Li , H. , Jiang , L. and Chen , Y.Z. 2007 . Model-molecular descriptor lab: A web-based sever for computing structural and physicochemical feature of compounds . Biotechnol. Bioeng. , 97 : 389 – 396 .
   PubMed Web of Science ®Google Scholar
• Schultz , H.P. 1989 . Topological organic chemistry. 1. Graph theory and topological indices of alkanes . J. Chem. Inf. Comput. Sci. , 29 : 227 – 228 .
   Google Scholar
• Hall , L.H. and Kier , L.B. 1995 . Electrotopological state indices for atom types: A novel combination of electronic, topological and valence state information . J. Chem. Inf. Comput. Sci. , 35 : 1039 – 1045 .
   Google Scholar
• Pearlman , R.S. and Smith , K.M. 1998 . Novel software tools for chemical diversity . Persp. Drug Disc. Des. , 9–11 : 339 – 353 .
   Google Scholar
• Caballero , J. and González-Nilo , F.D.F.M. 2008 . Structural requirements of pyrido[2,3-d]pyrimidin-7-one as CDK4/D inhibitors: 2D autocorrelation, CoMFA and CoMSIA analyses . Bioorg. Med. Chem. , 16 : 6103 – 6115 .
   PubMedGoogle Scholar
• Golbraikh , A. and Tropsha , A. 2002 . Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection . J. Comput. Aid. Mol. Des. , 16 : 357 – 369 .
   PubMed Web of Science ®Google Scholar
• Wu , W. , Walczak , B. , Massart , D.L. , Heuerding , S. , Erni , F. , Last , I.R. and Pebble , K.A. 1996 . Artificial neural networks in classification of NIR spectral data: Design of the training set . Chemometr. Intell. Lab. Syst. , 33 : 35 – 46 .
   Web of Science ®Google Scholar
• Kennard , R.W. and Stone , L.A. 1969 . Computer aided designs of experiments . Technometrics , 11 : 137 – 148 .
   Web of Science ®Google Scholar
• Bourguignon , B. , De Aguiar , P.F. , Thorre , K. and Massart , D.L. 1994 . Optimization in irregularly shaped regions: pH and solvent strength in reversed-phase high-performance liquid chromatography separations . J. Chromatogr. Sci. , 32 : 144 – 152 .
   Web of Science ®Google Scholar
• Claeys , D.D. , Verstraelen , T. , Pauwels , E. , Stevens , C.V. , Waroquier , M. and Van Speybroeck , V. 2010 . Conformational sampling of macrocyclic alkenes using a Kennard–Stone-based algorithm . J. Phys. Chem. A. , 114 : 6879 – 6887 .
   PubMedGoogle Scholar
• Liu , H. , Papa , E. and Gramatica , P. 2006 . QSAR prediction of estrogen activity for a large set of diverse chemicals . Chem. Res. Toxicol. , 19 : 1540 – 1548 .
   PubMed Web of Science ®Google Scholar
• Tropsha , A. , Gramatica , P. and Gombar , V.K. 2003 . The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models . QSAR Comb. Sci. , 22 : 69 – 77 .
   Google Scholar
• Liu , H. and Gramatica , P. 2007 . QSAR study of selective ligands for the thyroid hormone receptor β . Bioorgan. Med. Chem. , 15 : 5251 – 5261 .
   PubMed Web of Science ®Google Scholar
• Gramatica , P. 2007 . Principles of QSAR models validation: Internal and external . QSAR Comb. Sci. , 26 : 694 – 701 .
   Google Scholar
• Bourguignon , B. , De Aguiar , P.F. , Khots , M.S. and Massart , D.L. 1994 . Optimization in irregularly shaped regions: pH and solvent strength in reversed-phase HPLC separations . Anal. Chem. , 66 : 893 – 904 .
   Web of Science ®Google Scholar
• Perez , J.J. 2005 . Managing molecular diversity . Chem. Soc. Rev. , 34 : 143 – 152 .
   PubMedGoogle Scholar
• Willett , P. , Barnard , J.M. and Downs , G.M. 1998 . Chemical similarity searching . J. Chem. Inf. Comput. Sci. , 38 : 983 – 996 .
   Google Scholar
• Willett , P. and Winterman , V.A. 1986 . Comparison of some measures for the determination of intermolecular structural similarity . Quant. Struct. Act. Relat. , 5 : 18 – 25 .
   Google Scholar
• The NCI Diversity Set II, Available at http://dtp.nci.nih.gov/branches/dscb/div2_explanation.html (accessed on January 9, 2010)
   Google Scholar
• Li , C. , Xu , L. , Wolan , D.W. , Wilson , I.A. and Olson , A.J. 2004 . Virtual screening of human-aminoimidazole-4-carboxamide ribonucleotide transformylase against the NCI diversity set by use of AutoDock to identify novel nonfolate inhibitors . J. Med. Chem. , 47 : 6681 – 6690 .
   PubMed Web of Science ®Google Scholar
• Y.W. Chen and C.J. Lin, Combining SVMs with various feature selection strategies, (2005), Available at http://www.csie.ntu.edu.tw/∼cjlin/papers/features.pdf
   Google Scholar
• Huang , C.L. , Chen , M.C. and Wang , C.J. 2007 . Credit scoring with a data mining approach based on support vector machines . J. Expert. Syst. Appl. , 4 : 2870 – 2878 .
   Google Scholar
• Trotter , M.W.B. , Buxton , B.F. and Holden , S.B. 2001 . Support vector machines in combinatorial chemistry . Meas. Contr. , 34 : 235 – 239 .
   Web of Science ®Google Scholar
• Xue , Y. , Yap , C.W. , Sun , L.Z. , Cao , Z.W. , Wang , J.F. and Chen , Y.Z. 2004 . Prediction of p-glycoprotein substrates by support vector machine approach . J. Chem. Inf. Comput. Sci. , 44 : 1497 – 1505 .
   PubMedGoogle Scholar
• Burges , C.J.C. 1998 . A tutorial on support vector machines for pattern recognition . Data Min. Knowl. Disc. , 2 : 127 – 167 .
   Web of Science ®Google Scholar
• Katritzky , A.R. and Gordeeva , E.V. 1993 . Raditional topological indices vs electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research . J. Chem. Inf. Comput. Sci. , 33 : 835 – 857 .
   PubMedGoogle Scholar
• Ajmani , S. , Jadhav , K. and Kulkarni , S.A. 2006 . Three-dimensional QSAR using the k-nearest neighbor method and its interpretation . J. Chem. Inf. Model. , 46 : 24 – 31 .
   PubMed Web of Science ®Google Scholar
• 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 .
   PubMedGoogle Scholar
• Czerminski , R. , Yasri , A. and Hartsough , D. 2001 . Use of support vector machine in pattern application to QSAR studies . Quant. Struct. Act. Relat. , 20 : 227 – 240 .
   Google Scholar
• Huberty , C.J. 1994 . Applied Discriminant Analysis , New York : John Wiley & Sons .
   Google Scholar
• Fix , E. and Hodges , J.L. 1951 . Discriminatory analysis: Nonparametric discrimination: Consistency properties , 261 – 270 . Randolph Field, TX : USAF School of Aviation Medicine .
   Google Scholar
• Johnson , R.A. and Wichern , D.W. 1982 . Applied Multivariate Statistical Analysis , Englewood Cliffs, NJ : Prentice Hall .
   Google Scholar
• Yuan , J. and Chen , W. 2010 . A gamma dose distribution evaluation technique using the k-d tree for nearest neighbor searching . Med. Phys. , 37 : 4868 – 4873 .
   PubMedGoogle Scholar
• Wang , H. 2006 . Anal Mach Intell Nearest neighbors by neighborhood counting . IEEE Trans. Patt. , 28 : 942 – 953 .
   Google Scholar
• Givehchi , A. and Schneider , G. 2004 . Impact of descriptor vector scaling on the classification and nondrugs with artificial neural networks . J. Mol. Model. , 10 : 204 – 211 .
   PubMed Web of Science ®Google Scholar
• Vach , W. , Robner , R. and Schumacher , M. 1996 . Neural networks and logistic regression: Part I . Comput. Stat. Data Anal. , 21 : 683 – 701 .
   Web of Science ®Google Scholar
• Hosmer , D.W. and Lemeshow , S. 1989 . Applied Logistic Regression , New York : Wiley .
   Google Scholar
• Bruck , H.A. , McNeill , S.R. , Sutton , M.A. and Peters , W.H. 1989 . Digital-image-correlation using Newton-Raphson method for partial differential correction . Exp. Mech. , 29 : 261 – 267 .
   Web of Science ®Google Scholar
• Chen , B. , Harrison , R.F. , Papadatos , G. , Willett , P. , Wood , D.J. , Lewell , X.Q. , Greenidge , P. and Stiefl , N. 2007 . Evaluation of machine-learning methods for ligand-based virtual screening . J. Comput. Aided Mol. Des. , 21 : 53 – 62 .
   PubMed Web of Science ®Google Scholar
• Hert , J. , Willett , P. and Wilton , D.J. 2006 . New methods for ligand-based virtual screening: Use of data fusion and machine learning to enhance the effectiveness of similarity searching . J. Chem. Inf. Model. , 46 : 462 – 470 .
   PubMed Web of Science ®Google Scholar
• Willett , P. and Wilton , D. 2006 . Virtual screening using binary kernel discrimination: Analysis of pesticide data . J. Chem. Inf. Model. , 46 : 471 – 477 .
   PubMedGoogle Scholar
• Chen , B. , Harrison , R.F. , Pasupa , K. , Willett , P. , Wilton , D.J. , Wood , D.J. and Lewell , X.Q. 2006 . Virtual screening using binary Kernel discrimination: Effect of noisy training data and the optimization of performance . J. Chem. Inf. Model. , 46 : 478 – 486 .
   PubMed Web of Science ®Google Scholar
• Willett , P. and Wilton , D. 2007 . Prediction of ion channel activity using binary Kernel discrimination . J. Chem. Inf. Model. , 47 : 1961 – 1966 .
   PubMedGoogle Scholar
• Aitchison , J. and Aitken , C.G.G. 1976 . Multivariate binary discrimination by the kernel method . Biometrika , 63 : 413 – 420 .
   Web of Science ®Google Scholar
• Roulston , J.E. 2002 . Screening with tumor markers: Critical issues . Mol. Biotechnol. , 20 : 153 – 162 .
   PubMedGoogle Scholar
• Matthews , B.W. 1975 . Comparison of the predicted and observed secondary structure of T4 phage lysozyme . Biochim. Biophys. Acta. , 405 : 442 – 451 .
   PubMed Web of Science ®Google Scholar
• Leach , A.R. and Gillet , V.J. 2007 . An Introduction to Chemoinformatics , 2nd ed. , Netherlands : Springer .
   Google Scholar
• Pearlman , R.S. , Smith , K.M. , Kubingi , H. , Martin , T. and Folkers , G. , eds. 1997 . 3D-QSAR and Drug Design: Recent Advances , Dordrecht, Netherlands : Kluwer Academic .
   Google Scholar
• Burden , F.R. 1989 . Molecular identification number for substructure searches . J. Chem. Inf. Comput. Sci. , 29 : 225 – 227 .
   Google Scholar
• Hall , L.H. , Mohney , B.K. and Kier , L.B. 1991 . The electrotopological state: Structure information at the atomic level for molecular graphs . J. Chem. Inf. Comput. Sci. , 31 : 76 – 82 .
   Google Scholar
• Kier , L.B. and Hall , L.H. 1986 . Molecular Connectivity in Structure–Activity Analysis , Letchworth, Hertfordshire; New York : Research Studies Press, Wiley .
   Google Scholar
• Hall , L.H. and Kier , L.B. 1991 . “ The molecular connectivity chi indices and kappa shape indices in structure-property modeling ” . In Reviews of Computational Chemistry , Edited by: Lipkowitz , K.B. and Boyd , D.B. Vol. 2 , 367 – 412 . New York : VCH Publishers .
   Google Scholar
• Kogej , T. , Engkvist , O. , Blomberg , N. and Muresan , S. 2006 . Multifingerprint based similarity searches for targeted class compound selection . J. Chem. Inf. Model. , 46 : 1201 – 1213 .
   PubMedGoogle Scholar
• Bakken , G.A. and Jurs , P.C. 2000 . Classification of multidrug-resistance reversal agents using structure-based descriptors and linear discriminant analysis . J. Med. Chem. , 43 : 4534 – 4541 .
   PubMed Web of Science ®Google Scholar
• Wang , F. , Elmquist , C.E. , Stover , J.S. , Rizzo , C.J. and Stone , M.P. 2007 . DNA sequence modulates the conformation of the food mutagen 2-amino-3-methylimidazo[4,5-f]quinoline in the recognition sequence of the NarI restriction enzyme . Biochemistry , 24 : 8498 – 8516 .
   Google Scholar
• Baik , M.H. , Friesner , R.A. and Lippard , S.J. 2003 . Theoretical study of cisplatin binding to purine bases: Why does cisplatin prefer guanine over adenine as substrate? . J. Am. Chem. Soc. , 125 : 14082 – 14092 .
   PubMedGoogle Scholar
• Luch , A. 2009 . On the impact of the molecule structure in chemical carcinogenesis . EXS , 99 : 151 – 179 .
   PubMedGoogle Scholar
• Nikolova-Jeliazkova , N. and Jaworska , J. 2005 . An approach to determining applicability domains for QSAR group contribution models: An analysis of SRC KOWWIN . Altern. Lab. Anim. , 33 : 461 – 470 .
   PubMed Web of Science ®Google Scholar
• Tropsha , A. and Golbraikh , A. 2007 . Predictive QSAR modeling workflow, model applicability domains, and virtual screening . Curr. Pharm. Des. , 13 : 3494 – 3504 .
   PubMed Web of Science ®Google Scholar
• Eriksson , L. , Jaworska , J. , Worth , A. , Cronin , M.T.D. , McDowell , R.M. and Gramatica , P. 2003 . Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression-based QSARs . Environ. Health Persp. , 111 : 1351 – 1375 .
   Google Scholar