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Meta-heuristics on quantitative structure-activity relationships: study on polychlorinated biphenyls

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Abstract

A genetic algorithm was developed and assessed in order to select pairs of proper structural descriptors able to estimate and predict octanol-water partition coefficients of polychlorinated biphenyls (PCBs). The molecular descriptors family was calculated for a sample of 206 PCBs. The problem of searching for the proper descriptors in order to identify structure-activity relationships was translated in genetic terms. The following parameters were imposed in the genetic algorithm (GA) search: sample size − 12, number of variables in multivariate linear regression − 4, imposed adaptation requirements − 3 criteria, maximum number of generations − 50,000, selection strategy − tournament, probability of parent/child mutation − 0.05, number of genes implied in the mutation − 2, optimization parameter - determination coefficient, optimization score - minimum in the sample, and optimization objective - maximum. The highest determination coefficient was obtained in the generation 17,277. Twenty-one evolutions were studied until the optimum solution was obtained. The model identified by the implemented genetic algorithm proved not to be statistically different from the model identified through complete search (ZSteiger = 1.37, p = 0.0861). According to this GA model, the relationship between the structure of PCBs and octanol-water partition coefficients was of geometric and topological nature as previously revealed by the complete search. The genetic algorithm proved its ability to identify two pairs of molecular descriptors able to characterize the relationship between the structure of PCBs and the octanol-water partition coefficient.

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References

  1. Hansch C, Leo A (1979) Substituent constants for correlation analysis in chemistry and biology. Wiley, New York

    Google Scholar 

  2. Kaminski JJ (1994) Computer-assisted drug design and selection. Adv Drug Deliver Rev 14(2–3):331–337. doi:10.1016/0169-409X(94)90049-3

    Article  CAS  Google Scholar 

  3. Barbosa F, Horvath D (2004) Molecular similarity and property similarity. Curr Top Med Chem 4(6):589–600. doi:10.2174/1568026043451186

    Article  CAS  Google Scholar 

  4. Baumann K (1999) Uniform-length molecular descriptors for quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR): classification studies and similarity searching. Trends Analyt Chem 18(1):36–46. doi:10.1016/S0165-9936(98)00075-2

    Article  CAS  Google Scholar 

  5. Bravi G, Gancia E, Mascagni P, Pegna M, Todeschini R, Zaliani A (1997) MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: A comparative 3D QSAR study in a series of steroids. J Comput Aid Mol Des 11(1):79–92. doi:10.1023/A:1008079512289

    Article  CAS  Google Scholar 

  6. Ciubotariu D, Deretey E, Oprea TI, Sulea T, Simon Z, Kurunczi L, Chiriac A (2006) Multiconformational minimal steric difference. Structure-acetylcholinesterase hydrolysis rates relations for acetic acid Esters. QSAR Comb Sci 12(4):367–372. doi:10.1002/qsar.19930120404

    Google Scholar 

  7. Jäntschi L (2005) Molecular descriptors family on structure activity relationships 1. Review of the methodology. Leonardo Electron J Pract Technol 6:76–98 Available via: http://lejpt.academicdirect.org/A06/76_98.htm. Accessed 15 April 2009

    Google Scholar 

  8. Jäntschi L, Bolboacă SD (2007) Results from the use of molecular descriptors family on structure property/activity relationships. Int J Mol Sci 8(3):189–203. doi:10.3390/i8030189

    Article  Google Scholar 

  9. Putz MV, Lacrămă AM (2007) Introducing Spectral Structure Activity Relationship (S-SAR) analysis. Application to ecotoxicology. Int J Mol Sci 8(5):363–469. doi:10.3390/i8050363

    Article  CAS  Google Scholar 

  10. Du QS, Huang RB, Wei YT, Pang ZW, Du LQ, Chou KC (2009) Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design. J Comput Chem 30(2):295–304. doi:10.1002/jcc.21056

    Article  CAS  Google Scholar 

  11. Du QS, Huang RB, Wei YT, Du LQ, Chou KC (2008) Multiple field three dimensional quantitative structure-activity relationship (MF-3D-QSAR). J Comput Chem 29(2):211–219. doi:10.1002/jcc.20776

    Article  CAS  Google Scholar 

  12. Jing JH, Xiao SY, Li ZL (2008) Quantitative structure-activity relationship studies of fatty acids in ranunculus ternatus thunb using three-dimensional holographic vector of atomic interaction field. Fenxi Huaxue/ Chinese J Anal Chem 36(7):971–974

    CAS  Google Scholar 

  13. Vedani A, McMasters DR, Dobler M (2000) Multi-conformational ligand representation in 4D-QSAR: Reducing the bias associated with ligand alignment. Quant Struct-Act Relatsh 19(2):149–161. doi:10.1002/1521-3838(200004)19:2<149::AID-QSAR149>3.0.CO;2-9

    Article  CAS  Google Scholar 

  14. Vedani A, Dobler M (2002) 5D-QSAR: The key for simulating induced fit? J Med Chem 45(11):2139–2149. doi:10.1021/jm011005p

    Article  CAS  Google Scholar 

  15. Eriksson L, Johansson E, Lindgren F, Sjöström M, Wold S (2002) Megavariate analysis of hierarchial QSAR data. J Comput Aided Mol Des 16(10):711–726. doi:10.1023/A:1022450725545

    Article  CAS  Google Scholar 

  16. Liang G, Chen G, Niu W, Li Z (2008) Factor analysis scales of generalized amino acid information as applied in predicting interactions between the human amphiphysin-1 SH3 domains and their peptide ligands. Chem Biol Drug Des 71(4):345–351. doi:10.1111/j.1747-0285.2008.00641.x

    Article  CAS  Google Scholar 

  17. Tsygankova IG (2008) Variable selection in QSAR models for drug design. Curr Comput Aided Drug Des 4(2):132–142. doi:10.2174/157340908784533238

    Article  CAS  Google Scholar 

  18. Khan MTH, Sylte I (2007) Predictive QSAR modeling for the successful predictions of the ADMET properties of candidate drug molecules. Curr Drug Discov Technol 4(3):141–149

    Article  CAS  Google Scholar 

  19. Vighi M, Migliorati S, Monti GS (2009) Toxicity on the luminescent bacterium Vibrio fischeri (Beijerinck). I: QSAR equation for narcotics and polar narcotics. Ecotoxicol Environ Saf 72(1):154–161. doi:10.1016/j.ecoenv.2008.05.008

    Article  CAS  Google Scholar 

  20. Lin W-Q, Jiang J-H, Wu H-L, Shen G-L, Yu R-Q (2006) Recent advances in chemometric methodologies for QSAR studies. Curr Comput Aided Drug Des 2(3):255–266. doi:10.2174/157340906778226418

    Article  CAS  Google Scholar 

  21. Riahi S, Pourbasheer E, Dinarvand R, Ganjali MR, Norouzi P (2008) QSAR Study of 2-(1-Propylpiperidin-4-yl)-1H-Benzimidazole-4-Carboxamide as PARP inhibitors for treatment of cancer. Chem Biol Drug Des 72(6):575–584. doi:10.1111/j.1747-0285.2008.00739.x

    Article  CAS  Google Scholar 

  22. Duchowicz PR, Castro EA (2008) Partial order theory applied to QSPR-QSAR studies. Comb Chem High Throughput Screen 11(10):783–793. doi:10.2174/138620708786734316

    Article  CAS  Google Scholar 

  23. Xiao Y-D, Harris R, Bayram E, Santago P II, Schmitt JD (2006) Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation. J Chem Inf Model 46(1):137–144. doi:10.1021/ci0500841

    Article  CAS  Google Scholar 

  24. Fox T, Kriegl JM (2006) Machine learning techniques for in silico modeling of drug metabolism. Curr Top Med Chem 6(15):1579–1591. doi:10.2174/156802606778108915

    Article  CAS  Google Scholar 

  25. Du H, Wang J, Hu Z, Yao X, Zhang X (2008) Prediction of fungicidal activities of rice blast disease based on least-squares support vector machines and project pursuit regression. J Agric Food Chem 56(22):10785–10792. doi:10.1021/jf8022194

    Article  CAS  Google Scholar 

  26. George CJ, Bennett GF, Simoneaux D, George WJ (1988) Polychlorinated biphenyls a toxicological review. J Hazard Mater 18(2):113–144. doi:10.1016/0304-3894(88)85018-0

    Article  CAS  Google Scholar 

  27. Hansen BG, Paya-Perez AB, Rahman M, Larsen BR (1999) QSARs for K(ow) and K(oc) of PCB congeners: A critical examination of data, assumptions and statistical approaches. Chemosphere 39(13):2209–2228. doi:10.1016/S0045-6535(99)00145-9

    Article  CAS  Google Scholar 

  28. Giri S, Roy DR, Van Damme S, Bultinck P, Subramanian V, Chattaraj PK (2008) An atom counting QSPR protocol. QSAR Comb Sci 27(2):208–230. doi:10.1002/qsar.200730109

    Article  CAS  Google Scholar 

  29. Ivanciuc T, Ivanciuc O, Klein DJ (2006) Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quatitative super-structure/activity relationships (QSSAR). Mol Divers 10:133–145. doi:10.1007/s11030-005-9003-3

    Article  CAS  Google Scholar 

  30. Jiang GX, Niu JF, Zhang SP, Zhang ZY, Xie B (2008) Prediction of biodegradation rate constants of hydroxylated polychlorinated biphenyls by fungal laccases from Trametes versicolor and Pleurotus ostreatus. Bull Environ Contam Toxicol 81(1):1–6. doi:10.1007/s00128-008-9433-6

    Article  CAS  Google Scholar 

  31. Zeng X, Wang Z, Ge Z, Liu H (2007) Quantitative structure-property relationships for predicting subcooled liquid vapor pressure (PL) of 209 polychlorinated diphenyl ethers (PCDEs) by DFT and the position of Cl substitution (PCS) methods. Atmos Environ 41(17):3590–3603. doi:10.1016/j.atmosenv.2006.12.039

    Article  CAS  Google Scholar 

  32. Jäntschi L, Bolboacă SD, Diudea MV (2007) Chromatographic retention times of polychlorinated biphenyls: From structural information to property characterization. Int J Mol Sci 8(11):1125–1157. doi:0.3390/i8111125

    Article  Google Scholar 

  33. Wei B, Xie S, Yu M, Wu L (2007) QSPR-based prediction of gas/particle partitioning of polychlorinated biphenyls in the atmosphere. Chemosphere 66(10):1807–1820. doi:10.1016/j.chemosphere.2006.09.029

    Article  CAS  Google Scholar 

  34. Borja J, Taleon DM, Auresenia J, Gallardo S (2005) Polychlorinated biphenyls and their biodegradation. Process Biochem 40(6):1999–2013. doi:10.1016/j.procbio.2004.08.006

    Article  CAS  Google Scholar 

  35. Hertz-Picciotto I, Charles MJ, James RA, Keller JA, Willman E, Teplin S (2005) In utero polychlorinated biphenyl exposures in relation to fetal and early childhood growth. Epidemiology 16(5):648–656. doi:10.1097/01.ede.0000173043.85834.f3

    Article  Google Scholar 

  36. Bodin N, Le Loc'h F, Caisey X, Le Guellec A-M, Abarnou A, Loizeau V, Latrouite D (2008) Congener-specific accumulation and trophic transfer of polychlorinated biphenyls in spider crab food webs revealed by stable isotope analysis. Environ Pollut 151(1):252–261. doi:10.1016/j.envpol.2007.01.051

    Article  CAS  Google Scholar 

  37. Ruiz P, Faroon O, Moudgal CJ, Hansen H, De Rosa CT, Mumtaz M (2008) Prediction of the health effects of polychlorinated biphenyls (PCBs) and their metabolites using quantitative structure-activity relationship (QSAR). Toxicol Lett 181(1):53–65. doi:10.1016/j.toxlet.2008.06.870

    Article  CAS  Google Scholar 

  38. Jäntschi L, Bolboacă SD (2006) Molecular Descriptors Family on Structure Activity Relationships 6. Octanol-Water Partition Coefficient of Polychlorinated Biphenyls. Leonardo Electron J Pract Technol 8:71–86 Available via: http://lejpt.utcluj.ro/A08/71_86.htm. Accessed 15 April 2009

    Google Scholar 

  39. Jäntschi L, Bolboacă SD (2007) Integrated Structural Investigations on Biological Active Compounds (Research Report; in Romanian). Available via: http://lori.academicdirect.org/research/grants/Raport_Cercetare_ET036_2007.pdf. Accessed 15 April 2009

  40. Connor MS (1985) Comment on „fish/sediment concentration ratios for organic compounds”. Environ Sci Technol 19(2):198–199. doi:10.1021/es00132a015

    Article  Google Scholar 

  41. Eisler R, Belisle AA (1996) Planar PCB Hazards to Fish, Wildlife, and Invertebrates: A Synoptic Review. Contaminant Hazard Reviews 1–96. Available via: http://www.pwrc.usgs.gov/infobase/eisler/chr_31_planar_pcbs.pdf. Accessed 18 April 2009

  42. Hoffmann R (1963) An extended Hückel theory. I. Hydrocarbons. J Chem Phys 39(6):1397–1412. doi:10.1063/1.1734456

    Article  CAS  Google Scholar 

  43. Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP (1985) Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J Am Chem Soc 107(13):3902–3909. doi:10.1021/ja00299a024

    Article  CAS  Google Scholar 

  44. Fisher RA (1918) The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edin 52:399–433

    Google Scholar 

  45. Weismann A (1893) The germ-plasm: a theory of heredity. C. Scribner's Sons, New York

    Google Scholar 

  46. de Veies H (1902) The origin of species by mutation. Science 15(384):721–729. doi:10.1126/science.15.384.721

    Article  Google Scholar 

  47. Auerbach C, Robson JM, Carr JG (1947) The chemical production of mutations. Science 105(2723):243–247. doi:10.1126/science.105.2723.243

    Article  CAS  Google Scholar 

  48. Cairns J, Overbaugh J, Miller S (1988) The origin of mutants. Nature 335(6186):142–145. doi:10.1038/335142a0

    Article  CAS  Google Scholar 

  49. Darwin CR (1859) On the origin of species by means of natural selection. J Murray, London

    Google Scholar 

  50. Jarque CM, Bera AK (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ Lett 6(3):255–259. doi:10.1016/0165-1765(80)90024-5

    Article  Google Scholar 

  51. Kleinbaum DG, Kupper LL, Muller KE, Nizam A (2008) Applied regression analysis and multivariable methods, 4th edn. Duxbury (Thomson Higher Educatio), Canada, pp 141–146

    Google Scholar 

  52. Steiger JH (1980) Tests for comparing elements of a correlation matrix. Psychol Bull 87:245–251

    Article  Google Scholar 

  53. Bolboacă SD, Jäntschi L (2008) Modelling the property of compounds from structure: statistical methods for models validation. Environ Chem Lett 6:175–181. doi:10.1007/s10311-007-0119-9

    Article  Google Scholar 

  54. Daren Z (2001) QSPR studies of PCBs by the combination of genetic algorithms and PLS analysis. Comput Chem 25(2):197–204. doi:10.1016/S0097-8485(00)00081-4

    Article  CAS  Google Scholar 

  55. Todeschini R, Consonni V, Mauri A, Pavan M (2004) Detecting "bad" regression models: Multicriteria fitness functions in regression analysis. Anal Chim Acta 515(1):199–208. doi:10.1016/j.aca.2003.12.010

    Article  CAS  Google Scholar 

  56. Pavan M, Mauri A, Todeschini R (2004) Total ranking models by the genetic algorithm variable subset selection (GA-VSS) approach for environmental priority settings. Anal Bioanal Chem 380:430–444. doi:10.1007/s00216-004-2762-3

    Article  CAS  Google Scholar 

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Acknowledgments

UEFISCSU Romania partially supported this research through project (ID-202/01.10.2007 & ID-206/01.10.2007).

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Authors and Affiliations

  1. Technical University of Cluj-Napoca, 103-105 Muncii Bvd, 400641, Cluj-Napoca, Romania

    Lorentz Jäntschi

  2. Department of Medical Informatics and Biostatistics, “Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, 6 Louis Pasteur, 400349, Cluj-Napoca, Romania

    Sorana D. Bolboacă

  3. University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 3-5 Mănăştur, 400372, Cluj-Napoca, Romania

    Radu E. Sestraş

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  1. Lorentz Jäntschi
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  2. Sorana D. Bolboacă
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Correspondence to Lorentz Jäntschi.

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Jäntschi, L., Bolboacă, S.D. & Sestraş, R.E. Meta-heuristics on quantitative structure-activity relationships: study on polychlorinated biphenyls. J Mol Model 16, 377–386 (2010). https://doi.org/10.1007/s00894-009-0540-z

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