Molecular and Cellular Biochemistry

, Volume 452, Issue 1–2, pp 133–140 | Cite as

Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential

  • Alla P. ToropovaEmail author
  • Andrey A. Toropov
  • Aleksandar M. Veselinović
  • Jovana B. Veselinović
  • Danuta Leszczynska
  • Jerzy Leszczynski


Mutagenicity is the ability of a substance to induce mutations. This hazardous ability of a substance is decisive from point of view of ecotoxicology. The number of substances, which are used for practical needs, grows every year. Consequently, methods for at least preliminary estimation of mutagenic potential of new substances are necessary. Semi-correlations are a special case of traditional correlations. These correlations can be named as “correlations along two parallel lines.” This kind of correlation has been tested as a tool to predict selected endpoints, which are represented by only two values: “inactive/active” (0/1). Here this approach is used to build up predictive models for mutagenicity of large dataset (n = 3979). The so-called index of ideality of correlation (IIC) has been tested as a statistical criterion to estimate the semi-correlation. Three random splits of experimental data into the training, invisible-training, calibration, and validation sets were analyzed. Two models were built up for each split: the first model based on optimization without the IIC and the second model based on optimization where IIC is involved in the Monte Carlo optimization. The statistical characteristics of the best model (calculated with taking into account the IIC) n = 969; sensitivity = 0.8050; specificity = 0.9069; accuracy = 0.8648; Matthews’s correlation coefficient = 0.7196 (using IIC). Thus, the use of IIC improves the statistical quality of the binary classification models of mutagenic potentials (Ames test) of organic compounds.


Semi-correlation SAR Monte Carlo method Mutagenicity Ames test Index of ideality of correlation 



APT and AAT are grateful for the contribution of the EU project LIFE-COMBASE (LIFE15 ENV/ES/000416). DL and JL were supported by the NSF CREST Interdisciplinary Nanotoxicity Center Grant # HRD-1547754.

Author Contributions

Authors have done equivalent contributions to this work.

Compliance with ethical standards

Conflict of interest

The authors confirm that this article content has no conflict of interest.

Supplementary material

11010_2018_3419_MOESM1_ESM.xlsx (346 kb)
Supplementary material 1 Supplementary materials section contains technical details of the best model: (i) distribution into the training, invisible training, calibration, and validation sets; (ii) observed and predicted values of the categorical endpoint; (iii) domain of applicability; and (iv) correlation weights used to calculated the DCW(T*, N*). (XLSX 346 KB)


  1. 1.
    Zhang J, Chang VWC, Giannis A, Wang J-Y (2013) Removal of cytostatic drugs from aquatic environment: a review. Sci Total Environ 445–446:281–298. CrossRefGoogle Scholar
  2. 2.
    Zhao L, Wang W, Sedykh A, Zhu H (2017) Experimental errors in QSAR modeling sets: what we can do and what we cannot do. ACS Omega 2(6):2805–2812. CrossRefGoogle Scholar
  3. 3.
    Debnath AK, Debnath G, Shusterman AJ, Hansch C (1992) A QSAR investigation of the role of hydrophobicity in regulating mutagenicity in the ames test: 1. Mutagenicity of aromatic and heteroaromatic amines in Salmonella typhimurium TA98 and TA100. Environ Mol Mutagen 19 (1): 37–52.
  4. 4.
    Klopman G, Rosenkranz HS (1994) Approaches to SAR in carcinogenesis and mutagenesis. Prediction of carcinogenicity/mutagenicity using MULTI-CASE. Mutat Res Fund Mol Mech Mut 305 (1): 33–46.
  5. 5.
    Basak SC, Mills DR, Balaban AT, Gute BD (2001) Prediction of mutagenicity of aromatic and heteroaromatic amines from structure: a hierarchical QSAR approach. J Chem Inf Comput Sci 41(3):671–678. CrossRefGoogle Scholar
  6. 6.
    Debnath AK, Lopez Compadre RL, Shusterman AJ, Hansch C (1992) Quantitative structure-activity relationship investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test: 2. Mutagenicity of aromatic and heteroaromatic nitro compounds in Salmonella typhimurium TA100. Environ Mol Mutagen 19(1):53–70. CrossRefGoogle Scholar
  7. 7.
    Chung K-T, Kirkovsky L, Kirkovsky A, Purcell WP (1997) Review of mutagenicity of monocyclic aromatic amines: quantitative structure–activity relationships. Mutat Res Rev Mutat 387(1):1–16. CrossRefGoogle Scholar
  8. 8.
    Tuppurainen K, Lötjönen S, Laatikainen R, Vartiainen T, Maran U, Strandberg M, Tamm T (1991) About the mutagenicity of chlorine-substituted furanones and halopropenals. A QSAR study using molecular orbital indices. Mutat Res Fund Mol Mech Mut 247(1):97–102. CrossRefGoogle Scholar
  9. 9.
    Benigni R, Passerini L, Rodomonte A (2003) Structure-Activity Relationships for the mutagenicity and carcinogenicity of simple and α–β unsaturated aldehydes. Environ Mol Mutagen 42(3):136–143. CrossRefGoogle Scholar
  10. 10.
    Smith CJ, Hansch C, Morton MJ (1997) QSAR treatment of multiple toxicities: the mutagenicity and cytotoxicity of quinolones. Mutat Res Fund Mol Mech Mut 379(2):167–175. CrossRefGoogle Scholar
  11. 11.
    Pérez-Garrido A, Helguera AM, Rodríguez FG, Cordeiro MNDS (2010) QSAR models to predict mutagenicity of acrylates, methacrylates and α,β-unsaturated carbonyl compounds. Dent Mater J 26(5):397–415. CrossRefGoogle Scholar
  12. 12.
    Toropov AA, Toropova AP (2014) Optimal descriptor as a translator of eclectic data into endpoint prediction: mutagenicity of fullerene as a mathematical function of conditions. Chemosphere 104:262–264. CrossRefGoogle Scholar
  13. 13.
    Nendza M, Gabbert S, Kühne R, Lombardo A, Roncaglioni A, Benfenati E, Benigni R, Bossa C, Strempel S, Scheringer M, Fernández A, Rallo R, Giralt F, Dimitrov S, Mekenyan O, Bringezu F, Schüürmann G (2013) A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH. Regul Toxicol Pharmacol 66(3):301–314. CrossRefGoogle Scholar
  14. 14.
    Reenu V (2014) Electron-correlation based externally predictive QSARs for mutagenicity of nitrated-PAHs in Salmonella typhimurium TA100. Ecotoxicol Environ Saf 101(1):42–50. CrossRefGoogle Scholar
  15. 15.
    Johann S, Seiler T-B, Tiso T, Bluhm K, Blank LM, Hollert H (2016) Mechanism-specific and whole-organism ecotoxicity of mono-rhamnolipids. Sci Total Environ 548–549:155–163. CrossRefGoogle Scholar
  16. 16.
    Sokolović D, Ranković J, Stanković V, Stefanović R, Karaleić S, Mekić B, Milenković V, Kocić J, Veselinović AM (2017) QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method. Med Chem Res 26(4):796–804. CrossRefGoogle Scholar
  17. 17.
    Veselinović AM, Veselinović JB, Nikolić GM, Toropova AP, Toropov AA (2016) QSPR models for estimating retention in HPLC with the p solute polarity parameter based on the Monte Carlo method. Struct Chem 27(3):821–828. CrossRefGoogle Scholar
  18. 18.
    Toropova AP, Toropov AA, Veselinović AM, Veselinović JB, Benfenati E, Leszczynska D, Leszczynski J (2016) Nano-QSAR: model of mutagenicity of fullerene as a mathematical function of different conditions. Ecotoxicol Environ Saf 124:32–36. CrossRefGoogle Scholar
  19. 19.
    Kumar A, Chauhan S (2017) QSAR differential model for prediction of SIRT1 modulation using Monte Carlo method. Drug Res 67(3):156–162. Google Scholar
  20. 20.
    Kumar A, Chauhan S (2017) Use of the Monte Carlo method for OECD principles-guided QSAR modeling of SIRT1 inhibitors. Arch Pharm.
  21. 21.
    Islam MA, Pillay TS (2016) Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors. Chemometr Intell Lab Syst 153:67–74. CrossRefGoogle Scholar
  22. 22.
    Heidari A, Fatemi MH (2017) A theoretical approach to model and predict the adsorption coefficients of some small aromatic molecules on carbon nanotube. J Chin Chem Soc 64(3):289–295. CrossRefGoogle Scholar
  23. 23.
    Rescifina A, Floresta G, Marrazzo A, Parenti C, Prezzavento O, Nastasi G, Dichiara M, Amata E (2017) Sigma-2 receptor ligands QSAR model dataset. Data Brief 13:514–535. CrossRefGoogle Scholar
  24. 24.
    Trinh TX, Choi JS, Jeon H, Byun HG, Yoon TH, Kim J (2018) Quasi-SMILES-Based Nano-quantitative structure–activity relationship model to predict the cytotoxicity of multiwalled carbon nanotubes to human lung cells. Chem Res Toxicol 31(3):183–190. CrossRefGoogle Scholar
  25. 25.
    Toropov AA, Toropova AP (2017) The index of ideality of correlation: a criterion of predictive potential of QSPR/QSAR models? Mutat Res Genet Toxicol Environ Mutagen 819:31–37. CrossRefGoogle Scholar
  26. 26.
    Toropov AA, Toropova AP (2015) Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions. Chemosphere 139:18–22. CrossRefGoogle Scholar
  27. 27.
    Toropov AA, Toropova AP, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2012) Calculation of molecular features with apparent impact on both activity of mutagens and activity of anticancer agents. Anticancer Agents Med Chem 12(7):807–817. CrossRefGoogle Scholar
  28. 28.
    Toropov AA, Toropova AP, Martyanov SE, Benfenati E, Gini G, Leszczynska D, Leszczynski J (2011) Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines. Chemometr Intell Lab Syst 109(1):94–100. CrossRefGoogle Scholar
  29. 29.
    Toropov AA, Toropova AP (2001) Prediction of heteroaromatic amine mutagenicity by means of correlation weighting of atomic orbital graphs of local invariants. J Mol Struct THEOCHEM 538:287–293. CrossRefGoogle Scholar
  30. 30.
    Toropov AA, Carbó-Dorca R, Toropova AP (2018) Index of ideality of correlation: new possibilities to validate QSAR: a case study. Struct Chem 29:33–38. CrossRefGoogle Scholar
  31. 31.
    Toropova AP, Toropov AA (2017) The index of ideality of correlation: a criterion of predictability of QSAR models for skin permeability? Sci Total Environ 586:466–472. CrossRefGoogle Scholar
  32. 32.
    Kranthi Kumar K, Uma Devi B, Neeraja P (2017) Integration of in silico approaches to determination of endocrine-disrupting perfluorinated chemicals binding potency with steroidogenic acute regulatory protein. Biochem Biophys Res Commun 491(4):1007–1014. CrossRefGoogle Scholar
  33. 33.
    Ahlberg E, Amberg A, Beilke LD, Bower D, Cross KP, Custer L, Ford KA, Van Gompel J, Harvey J, Honma M, Jolly R, Joossens E, Kemper RA, Kenyon M, Kruhlak N, Kuhnke L, Leavitt P, Naven R, Neilan C, Quigley DP, Shuey D, Spirkl H-P, Stavitskaya L, Teasdale A, White A, Wichard J, Zwickl C, Myatt GJ (2016) Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: a case study using aromatic amine mutagenicity. Regul Toxicol Pharmacol 77:1–12. CrossRefGoogle Scholar
  34. 34.
    Ford KA, Ryslik G, Chan BK, Lewin-Koh S-C, Almeida D, Stokes M, Gomez SR (2017) Comparative evaluation of 11 in silico models for the prediction of small molecule mutagenicity: role of steric hindrance and electron-withdrawing groups. Toxicol Mech Method 27(1):24–35. CrossRefGoogle Scholar
  35. 35.
    Valencia A, Prous J, Mora O, Sadrieh N, Valerio LG (2013) A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities. Toxicol Appl Pharmacol 273(3):427–434. CrossRefGoogle Scholar
  36. 36.
    Zheng W, Tian D, Wang X, Tian W, Zhang H, Jiang S, He G, Zheng Y, Qu W (2013) Support vector machine: classifying and predicting mutagenicity of complex mixtures based on pollution profiles. Toxicology 314(2–3):151–159. CrossRefGoogle Scholar
  37. 37.
    Ono A, Takahashi M, Hirose A, Kamata E, Kawamura T, Yamazaki T, Sato K, Yamada M, Fukumoto T, Okamura H, Mirokuji Y, Honma M (2012) Validation of the (Q)SAR combination approach for mutagenicity prediction of flavor chemicals. Food Chem Toxicol 50(5):1538–1546. CrossRefGoogle Scholar
  38. 38.
    Gouveia DN, Costa JS, Oliveira MA, Rabelo TK, Silva AMDOE, Carvalho AA, Miguel-dos- Santos R, Lauton-Santos S, Scotti L, Scotti MT, Santos MRVD, Quintans-Júnior LJ, Albuquerque Junior RLCD, Guimarães AG (2018) α-Terpineol reduces cancer pain via modulation of oxidative stress and inhibition of iNOS. Biomed Pharmacother 105:652–661. CrossRefGoogle Scholar
  39. 39.
    Toropova AP, Toropov AA, Benfenati E, Castiglioni S, Bagnati R, Passoni A, Zuccato E, Fanelli R (2018) Quasi-SMILES as a tool to predict removal rates of pharmaceuticals and dyes in sewage. Process Saf Environ Prot 118:227–233. CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Alla P. Toropova
    • 1
    Email author
  • Andrey A. Toropov
    • 1
  • Aleksandar M. Veselinović
    • 2
  • Jovana B. Veselinović
    • 2
  • Danuta Leszczynska
    • 3
  • Jerzy Leszczynski
    • 4
  1. 1.Laboratory of Environmental Chemistry and ToxicologyIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanoItaly
  2. 2.Department of Chemistry, Faculty of MedicineUniversity of NišNišSerbia
  3. 3.Department of Civil and Environmental Engineering, Interdisciplinary Nanotoxicity CenterJackson State UniversityJacksonUSA
  4. 4.Department of Chemistry and Biochemistry, Interdisciplinary Nanotoxicity CenterJackson State UniversityJacksonUSA

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