In-silico predictive mutagenicity model generation using supervised learning approaches

Journal of Cheminformatics

Volumn 4, Issue 5, 2012, Pages

  Seal, Abhik ^a   Passi, Anurag ^b   Abdul Jaleel, U C ^c   Wild, David J ^a   Consortium, O S D D ^b  

^a INDIANA UNIVERSITY   (United States)

^b NATIONAL CHEMICAL LABORATORY   (India)

^c Malabar Christian College   (India)

Author keywords

Machine learning; Molecular descriptors; Mutagenicity; Random Forest; Screening; Toxicophores

Indexed keywords

EID: 84862842130     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/1758-2946-4-10     Document Type: Article

References (36)

1. van Ravenzwaay B, Herold M, Kamp H, Kapp MD, Fabian E, Looser R, Krennrich G, Mellert W, Prokoudine A, Strauss V, Walk T, Wiemer J: Metabolomics: A tool for early detection of toxicological effects and an opportunity for biology based grouping of chemicals-From QSAR to QBAR. Mutat Res 2012, [In Press].
2. Ames B: The detection of environmental mutagens and potential. Cancer 1984, 53:2030-2040.
3. Mortelmans K, Zeiger E: The ames salmonella/microsome mutagenicity assay. Mutat Res 2000, 455(1-2):29-60.
4. Kazius J, McGuire J, Bursi R: Derivation and validation of toxicophores for mutagenicity prediction. J Med Chem 2005, 48(1):312-320. (Pubitemid 40105270)
5. Helma C, Cramer T, Kramer S, Raedt L: Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. J Chem Inf Comput Sci 2004, 44:1402-1411.
6. Hansen K, Mika S, Schroeter T, Sutter A, Laak A, Hartmann ST, Heinrich N, Muller K P: Benchmark data set for in-silico prediction of ames mutagenicity. J Chem Inf Model 2009, 49:2077-2081.
7. Zhang QZ, Aires-de-Sousa J: Random forest prediction of mutagenicity from empirical physicochemical descriptors. J Chem Inf Model 2007, 47:1-8.
8. Feng J, Lurati L, Ouyang H, Robinson T, Wang Y, Yuan S, Young SS: Predictive toxicology: benchmarking molecular descriptors and statistical methods. J Chem Inf Comput Sci 2003, 43:1463-1470.
9. King RD, Muggletont SH, Srinivasani A, Sternberg MJE: 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 1996, 93:438-442. (Pubitemid 26038172)
10. Judson R, Elloumi F, Setzer RW, Li Z, Shah I: A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinf 2008,:9-241.
11. Ferrari T, Gini G: An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts. Chem Cent J 2010, 4(Suppl 1):S2.
12. Benfenati E: The CAESAR project for in silico models for the REACH legislation. Chem Central J 2010, 4(Suppl 1):I1.
13. Votano JR, Parham M, Hall LH, Kier LB, Oloff S, Tropsha A, Xie QA, Tong W: Three new consensus QSAR models for the prediction of ames genotoxicity. Mutagenesis 2004, 19:365-377. (Pubitemid 39405973)
14. Ashby J, Tennant RW: Chemical structure, salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP. Mutat Res 1988, 204(1):17-115. (Pubitemid 18057809)
15. Hakimelahi GH, Khodarahmi GA: The Identification of Toxicophores for the Prediction of Mutagenicity Hepatotoxicity and Cardiotoxicity. J Iran Chem Soc 2005, 2:244-267.
16. Blagg J: Structure activity relationships for in vitro and in vivo toxicity. Annu R Med Chem 2006, 41:353-358.
17. Bongsup PC, Beland FA, Marques FM: NMR structural studies of a 15-mer DNA sequence from a rasprotooncogene modified at the first base of codon 61 with the carcinogen 4 -aminobiphenyl. Biochemistry 1992, 31(40):9587-9602.
18. th Joint Sheffield Conference on Chemoinformatics: 2007.
19. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Guo AC, Wishart DS: DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 2011, 39:D1035-D1041.
20. Irwin J, Shoichet B: Zinc - a free database of commercially available compounds for virtual screening. J Chem Inf Model 2005, 45(1):177-182. (Pubitemid 40736970)
21. Accelrys, Inc., 10188 Telesis Court, Suite 100, San Diego, CA. URL: [http://accelrys.com/products/pipeline-pilot/]
22. Gold LS, Slone TH, Ames BN, Manley NB, Garfinkel GB, Rohrbach L: Carcinogenic Potency Database. In Handbook of Carcinogenic Potency and Genotoxicity Databases. Boca Raton: CRC Press; 1997:1-605.
23. Liu K, Feng J, Young SS, Power MV: A Software Environment for Molecular Viewing, Descriptor Generation, Data Analysis and Hit Evaluation. J Chem Inf Model 2005, 45(2):515-522. (Pubitemid 40635359)
24. Burden FR: Molecular identification number for substructure searches. J Chem Inf Comput Sci 1989, 29:225-227.
25. Schierz AC: Virtual screening of bioassay data. J Cheminformatics 2009, 1:21.
26. [http://www.cs.waikato.ac.nz/ml/Weka/index.html]
27. Friedman N, Geiger D, Goldszmidt M: Bayesian network classifiers. Mach Learn 1997, 29:131-163. (Pubitemid 127510036)
28. Keerthi S, Gilbert E: Convergence of a generalized SMO algorithm for SVM classifier design. Mach Learn 2002, 46:351-360. (Pubitemid 34129975)
29. Murthy A: Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowledge Discovery 1998, 2:345-389.
30. Dietterich TG: An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 2000, 40:139-157.
31. Ehrman TM, Barlow DJ, Hylands J, 2: Virtual Screening of Chinese Herbs with Random Forest. J ChemInf Model 2007, 47:264-278. (Pubitemid 46615932)
32. Singla D, Anurag M, Dash D, Raghava G: A web server for predicting inhibitors against bacterial target GlmU protein. BMCPharmacol 2011, 11:5.
33. Menze BH, Kelm BM, Masuch R, Himmelreich U, Bachert P, Petrich W: A comparison of Random Forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinforma 2009, 10:213.
34. Lagadic D, Rissel M, Le Bot MA: Guillouzo toxic effects of tacrine on primary hepatocytes and liver epithelial cells in culture. Cell Biol Toxicol 1998, 14:5361-5373.
35. Fuchs S, Simon Z, Brezis M: Fatal hepatic failure associated with ciprofloxacin. Lancet 1994, 242:738-739. (Pubitemid 24087878)
36. Ashby J: Fundamental structural alerts to potential carcinogenicity or non carcinogenicity. Environ Mutagen 1985, 7:919-921. (Pubitemid 16180586)