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QSAR model toward the rational design of new agrochemical fungicides with a defined resistance risk using substructural descriptors

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Abstract

The increasing resistance of several phytopathogenic fungal species to the existing agrochemical fungicides has alarmed to the worldwide scientific community. There is no available methodology to predict in an efficient way if a new fungicide will have resistance risk due to fungal species which cause considerable crop losses. In an attempt to overcome this problem, a multi-resistance risk QSAR model, based on substructural descriptors was developed from a heterogeneous database of compounds. The purpose of this model is the classification, design, and prediction of agrochemical fungicides according to resistance risk categories. The QSAR model classified correctly 85.11% of the fungicides and the 85.07% of the inactive compounds in the training series, for an accuracy of 85.08%. In the prediction series, the percentages of correct classification were 85.71 and 86.55% for fungicides and inactive compounds, respectively, with an accuracy of 86.39%. Some fragments were extracted and their quantitative contributions to the fungicidal activity were calculated taking into consideration the different resistance risk categories for agrochemical fungicides. In the same way, some fragments present in molecules with fungicidal activity and with negative contributions were analyzed like structural alerts responsible of resistance risk.

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References

  1. Plimmer JR, Gammon DW, Ragsdale NN (2003) Encyclopedia of agrochemicals. Wiley, Hoboken

    Book  Google Scholar 

  2. Brent KJ, Hollomon DW (2007) Fungicide resistance in crop pathogens: how can it be managed? FRAC Monograph No. 1, second revised edn. Fungicide Resistance Action Committee, CROPLIFE INTERNATIONAL, Brussels. Available at: http://www.frac.info/frac/publication/publication.htm. Accessed on 2 Jan 2011

  3. Li M, Liu CL, Li L, Yang H, Li ZN, Zhang H, Li ZM (2009) Design, synthesis and biological activities of new strobilurin derivatives containing substituted pyrazoles. Pest Manag Sci 66: 107–112. doi:10.1002/ps.1837

    Article  Google Scholar 

  4. Lopez-Ruiz FJ, Perez-Garcia A, Fernandez-Ortuno D, Romero D, de Garcia E, Vicente A, Brown JK, Tores JA (2010) Sensitivities to DMI fungicides in populations of Podosphaera fusca in south central Spain. Pest Manag Sci 66: 801–808. doi:10.1002/ps.1948

    Article  PubMed  CAS  Google Scholar 

  5. Bolboacă SD, Jäntschi L (2007) Modelling the inhibition activity on carbonic anhydrase I of some substituted thiadiazole- and thiadiazoline-disulfonamides: integration of structure information. Comput Aided Chem Eng 24: 965–970. doi:10.1016/S1570-7946(07)80185-4

    Article  Google Scholar 

  6. Khambay BP, Batty D, Jewess PJ, Bateman GL, Hollomon DW (2003) Mode of action and pesticidal activity of the natural product dunnione and of some analogues. Pest Manag Sci 59: 174–182. doi:10.1002/ps.632

    Article  PubMed  CAS  Google Scholar 

  7. 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: 10785–10792. doi:10.1021/jf8022194

    Article  PubMed  CAS  Google Scholar 

  8. Balba H (2007) Review of strobilurin fungicide chemicals. J Environ Sci Health B 42: 441–451. doi:10.1080/03601230701316465

    Article  PubMed  CAS  Google Scholar 

  9. Estrada E, Uriarte E, Montero A, Teijeira M, Santana L, De Clercq E (2000) A novel approach for the virtual screening and rational design of anticancer compounds. J Med Chem 43: 1975–1985. doi:10.1021/jm991172d

    Article  PubMed  CAS  Google Scholar 

  10. Estrada E, Pena A, Garcia-Domenech R (1998) Designing sedative/hypnotic compounds from a novel substructural graph-theoretical approach. J Comput Aided Mol Des 12: 583–595. doi:10.1023/A:1008048003720

    Article  PubMed  CAS  Google Scholar 

  11. Viswanadhan VN, Reddy MR, Bacquet RJ, Erion MD (1993) Assessment of methods used for predicting lipophilicity: application to nucleosides and nucleoside bases. J Comput Chem 14: 1019–1026. doi:10.1002/jcc.540140903

    Article  CAS  Google Scholar 

  12. Talete-srl (2005) DRAGON for Windows (software for molecular descriptor calculations) Version 5.3. Available at: http://www.talete.mi.it/dragon.htm

  13. Estrada E, Gutierrez Y (1999) Modeling chromatographic parameters by a novel graph theoretical sub-structural approach. J Chromatogr A 858: 187–199. doi:10.1016/S0021-9673(99)00808-0

    Article  PubMed  CAS  Google Scholar 

  14. Estrada E, Gutierrez Y, Gonzalez H (2000) Modeling diamagnetic and magnetooptic properties of organic compounds with the TOSS-MODE approach. J Chem Inf Comput Sci 40: 1386–1399. doi:10.1021/ci000041e

    PubMed  CAS  Google Scholar 

  15. Estrada E (2000) A computer-based approach to describe the 13C NMR chemical shifts of alkanes by the generalized spectral moments of iterated line graphs. Comput Chem 24: 193–201. doi:10.1016/S0097-8485(99)00060-1

    Article  PubMed  CAS  Google Scholar 

  16. Estrada E, Uriarte E (2001) Quantitative structure–toxicity relationships using TOPS-MODE. 1. Nitrobenzene toxicity to Tetrahymena pyriformis. SAR QSAR Environ Res 12: 309–324. doi:10.1080/10629360108032919

    Article  PubMed  CAS  Google Scholar 

  17. Estrada E, Uriarte E, Gutierrez Y, Gonzalez H (2003) Quantitative structure–toxicity relationships using TOPS-MODE. 3. Structural factors influencing the permeability of commercial solvents through living human skin. SAR QSAR Environ Res 14: 145–163. doi:10.1080/1062936031000073162

    Article  PubMed  CAS  Google Scholar 

  18. Helguera AM, Gonzalez MP, Cordeiro MNDS, Perez MA (2007) Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds. Toxicol Appl Pharmacol 221: 189–202. doi:10.1016/j.taap.2007.02.021

    Article  PubMed  CAS  Google Scholar 

  19. Morales Helguera A, Perez Gonzalez M, Cordeiro MNDS, Cabrera Perez MA (2008) Quantitative structure–carcinogenicity relationship for detecting structural alerts in nitroso compounds: species, rat; sex, female; route of administration, gavage. Chem Res Toxicol 21: 633–642. doi:10.1021/tx700336n

    Article  PubMed  CAS  Google Scholar 

  20. Helguera AM, Perez-Machado G, Cordeiro MNDS, Combes RD (2010) Quantitative structure–activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach. SAR QSAR Environ Res 21: 277–304. doi:10.1080/10629361003773930

    Article  PubMed  CAS  Google Scholar 

  21. Estrada E, Peña A (2000) In silico studies for the rational discovery of anticonvulsant compounds. Bioorg Med Chem 8: 2755–2770. doi:10.1016/S0968-0896(00)00204-2

    Article  PubMed  CAS  Google Scholar 

  22. Estrada E, Quincoces JA, Patlewicz G (2004) Creating molecular diversity from antioxidants in Brazilian propolis. Combination of TOPS-MODE QSAR and virtual structure generation. Mol Divers 8: 21–33. doi:10.1023/B:MODI.0000006804.97390.40

    Article  PubMed  CAS  Google Scholar 

  23. Speck-Planche A, Scotti MT, García-López A, Emerenciano VP, Molina-Pérez E, Uriarte E (2009) Design of novel antituberculosis compounds using graph-theoretical and substructural approaches. Mol Divers 13: 445–458. doi:10.1007/s11030-009-9129-9

    Article  Google Scholar 

  24. Speck-Planche A, Scotti MT, Emerenciano VP, García-López A, Molina-Pérez E, Uriarte E (2010) Designing novel antitrypanosomal agents from a mixed graph-theoretical substructural approach. J Comput Chem 31: 882–894. doi:10.1002/jcc.21374

    Google Scholar 

  25. Estrada E (1996) Spectral moments of the edge adjacency matrix in molecular graphs. 1. Definition and applications for the prediction of physical properties of alkanes. J Chem Inf Comput Sci 36: 844–849. doi:10.1021/ci950187r

    CAS  Google Scholar 

  26. Estrada E (1997) Spectral moments of the edge adjacency matrix in molecular graphs. 2. Molecules containing heteroatoms and QSAR applications. J Chem Inf Comput Sci 37: 320–328. doi:10.1021/ci960113v

    CAS  Google Scholar 

  27. Estrada E (1998) Spectral moments of the edge adjacency matrix in molecular graphs. 3. Molecules containing cycles. J Chem Inf Comput Sci 38: 23–27. doi:10.1021/ci970030u

    CAS  Google Scholar 

  28. Perez Gonzalez M, Gonzalez Diaz H, Molina Ruiz R, Cabrera MA, Ramos de Armas R (2003) TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new herbicides. J Chem Inf Comput Sci 43: 1192–1199. doi:10.1021/ci034039+

    PubMed  CAS  Google Scholar 

  29. Estrada E, Molina E, Perdomo-Lopez I (2001) Can 3D structural parameters be predicted from 2D (topological) molecular descriptors. J Chem Inf Comput Sci 41: 1015–1021. doi:10.1021/ci000170v

    PubMed  CAS  Google Scholar 

  30. Wood A (Copyright © 1995–2010) Compendium of Pesticide Common Names. Classified List of Pesticides. Compendium of Pesticide Common Names. Classified List of Pesticides. Available at: http://www.alanwood.net/pesticides/class_pesticides.html. Accessed on 2 Jan 2011

  31. FRAC Code List 2010 (2010) Fungicide Resistance Action Committee, CROPLIFE INTERNATIONAL. FRAC edn. Available at: http://www.frac.info/frac/publication/publication.htm. Accessed on 2 Jan 2011

  32. O’Neill MJ, Heckelman PE, Koch CB, Roman KJ (2006) The Merck index, an encyclopedia of chemicals drugs, and biologicals. Merck & Co Inc., Whitehouse Station

    Google Scholar 

  33. Estrada E, Gutiérrez Y (2002–2004) MODESLAB 1.5. Available at: www.modeslab.com

  34. Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2010) Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 18: 2225–2231. doi:10.1016/j.bmc.2010.01.068

    Article  PubMed  CAS  Google Scholar 

  35. Marrero-Ponce Y, Casanola-Martin GM, Khan MT, Torrens F, Rescigno A, Abad C (2010) Ligand-based computer-aided discovery of tyrosinase inhibitors Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds. Curr Pharm Des 16: 2601–2624

    Article  PubMed  CAS  Google Scholar 

  36. Concu R, Dea-Ayuela MA, Perez-Montoto LG, Bolas-Fernandez F, Prado-Prado FJ, Podda G, Uriarte E, Ubeira FM, Gonzalez-Diaz H (2009) Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins. J Proteome Res 8: 4372–4382. doi:10.1021/pr9003163

    Article  PubMed  CAS  Google Scholar 

  37. StatSoft, Inc. (2001) STATISTICA (data analysis software system), version 6. Available at: www.statsoft.com

  38. Hanczar B, Hua J, Sima C, Weinstein J, Bittner M, Dougherty ER (2010) Small-sample precision of ROC-related estimates. Bioinformatics 26: 822–830. doi:10.1093/bioinformatics/btq037

    Article  PubMed  CAS  Google Scholar 

  39. van de Waterbeemd H (1995) Chemometrics methods in molecular design. VCH Publishers, Weinheim

    Book  Google Scholar 

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Authors and Affiliations

  1. Faculty of Natural Sciences, Department of Chemistry, University of Oriente, 90500, Santiago de Cuba, Cuba

    Alejandro Speck-Planche & Julio A. Rojas-Vargas

  2. Faculty of Technology and Production Management, Moscow State University of Food Production, Volokolamskoe shosse 11, 125080, Moscow, Russia

    Valeria V. Kleandrova

Authors
  1. Alejandro Speck-Planche
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  2. Valeria V. Kleandrova
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  3. Julio A. Rojas-Vargas
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Corresponding author

Correspondence to Alejandro Speck-Planche.

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Speck-Planche, A., Kleandrova, V.V. & Rojas-Vargas, J.A. QSAR model toward the rational design of new agrochemical fungicides with a defined resistance risk using substructural descriptors. Mol Divers 15, 901–909 (2011). https://doi.org/10.1007/s11030-011-9320-7

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