Environmental Science and Pollution Research

, Volume 25, Issue 35, pp 35420–35428 | Cite as

QSAR model for predicting the toxicity of organic compounds to fathead minnow

  • Qingzhu Jia
  • Yunpeng Zhao
  • Fangyou Yan
  • Qiang WangEmail author
Research Article


In this work, a new norm descriptor is proposed based on atomic properties. A quantitative structure-activity relationship (QSAR) model for predicting the toxicity of organic compounds to fathead minnow is further developed by norm descriptors. Results indicate that this new model based on the norm descriptors has satisfactory predictive results with the squared correlation coefficient (R2) and squared relation coefficient of the cross validation (Q2) of 0.8174 and 0.7923, respectively. Combining with Y-randomization test, applicability domain test, and comparison with other references, calculation results indicate that the QSAR model performs well both in the stability and the accuracy with wide application domain, which might be further used effectively for the safe and risk assessment of various organics.


QSAR Fathead minnow Norm index Toxicity Risk assessment 


Funding information

This work was supported by the National Natural Science Foundation of China (21676203 and 21808167).

Supplementary material

11356_2018_3434_MOESM1_ESM.xlsx (75 kb)
Table S1 (XLSX 74 kb)


  1. Anonymous ADAPTGoogle Scholar
  2. Anonymous CODESSAGoogle Scholar
  3. Anonymous DragonGoogle Scholar
  4. Barron MG, Lilavois CR, Martin TM (2015) MOAtox: a comprehensive mode of action and acute aquatic toxicity database for predictive model development. Aquat Toxicol 161:102–107CrossRefGoogle Scholar
  5. Basant N, Gupta S (2017) QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes. Environ Sci Pollut Res 24:14430–14444CrossRefGoogle Scholar
  6. Belanger SE, Brill JL, Rawlings JM, Price BB (2016) Development of acute toxicity quantitative structure activity relationships (QSAR) and their use in linear alkylbenzene sulfonate species sensitivity distributions. Chemosphere 155:18–27CrossRefGoogle Scholar
  7. Cassani S, Kovarich S, Papa E, Roy PP, van der Wal L, Gramatica P (2013) Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity–activity modelling. J Hazard Mater 258–259:50–60CrossRefGoogle Scholar
  8. Cassotti M, Ballabio D, Todeschini R, Consonni V (2015) A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas). SAR QSAR Environ Res 26:217–243CrossRefGoogle Scholar
  9. Colombo A, Benfenati E, Karelson M, Maran U (2008) The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity. Chemosphere 72:772–780CrossRefGoogle Scholar
  10. Drgan V, Župerl Š, Vračko M, Como F, Novič M (2016) Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm. SAR QSAR Environ Res 27:501–519CrossRefGoogle Scholar
  11. Fangyou Y, Wensi HE, Qingzhu J, Qiang W, Shuqian X, Peisheng MA. (2018) Prediction of ionic liquids viscosity at variable temperatures and pressures. Chem Eng SciGoogle Scholar
  12. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701CrossRefGoogle Scholar
  13. He WS, Yan FY, Jia QZ, Xia SQ, Wang Q (2018) QSAR models for describing the toxicological effects of ILs against Staphylococcus aureus based on norm indexes. Chemosphere 195:831–838CrossRefGoogle Scholar
  14. Jagiello K, Mostrag-Szlichtyng A, Gajewicz A, Kawai T, Imaizumi Y, Sakurai T, Yamamoto H, Tatarazako N, Mizukawa K, Aoki Y, Suzuki N, Watanabe H, Puzyn T (2015) Towards modelling of the environmental fate of pharmaceuticals using the QSPR-MM scheme. Environ Model Softw 72:147–154CrossRefGoogle Scholar
  15. Jia QZ, Cui X, Li L, Wang Q, Liu Y, Xia SQ, Ma PS (2015) Quantitative structure-activity relationship for high affinity 5-HT1A receptor ligands based on norm indexes. J Phys Chem B 119:15561–15567CrossRefGoogle Scholar
  16. Jin X, Jin M, Sheng L (2014) Three dimensional quantitative structure–toxicity relationship modeling and prediction of acute toxicity for organic contaminants to algae. Comput Biol Med 51:205–213CrossRefGoogle Scholar
  17. Levet A, Bordes C, Clément Y, Mignon P, Chermette H, Marote P, Cren-Olivé C, Lantéri P (2013) Quantitative structure–activity relationship to predict acute fish toxicity of organic solvents. Chemosphere 93:1094–1103CrossRefGoogle Scholar
  18. Lyakurwa F, Yang X, Li X, Qiao X, Chen J (2014a) Development and validation of theoretical linear solvation energy relationship models for toxicity prediction to fathead minnow (Pimephales promelas). Chemosphere 96:188–194CrossRefGoogle Scholar
  19. Lyakurwa FS, Yang X, Li X, Qiao X, Chen J (2014b) Development of in silico models for predicting LSER molecular parameters and for acute toxicity prediction to fathead minnow (Pimephales promelas). Chemosphere 108:17–25CrossRefGoogle Scholar
  20. Martin TM, Young DM (2001) Prediction of the acute toxicity (96-h LC50) of organic compounds to the fathead minnow (Pimephales promelas) using a group contribution method. Chem Res Toxicol 14:1378–1385CrossRefGoogle Scholar
  21. Papa E, Battaini F, Gramatica P (2005) Ranking of aquatic toxicity of esters modelled by QSAR. Chemosphere 58:559–570CrossRefGoogle Scholar
  22. Ren YY, Zhou LC, Yang L, Liu PY, Zhao BW, Liu HX (2016) Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis. SAR QSAR Environ Res 27:721–746CrossRefGoogle Scholar
  23. Roy K, Das RN (2012) QSTR with extended topochemical atom (ETA) indices. 15. Development of predictive models for toxicity of organic chemicals against fathead minnow using second-generation ETA indices. SAR QSAR Environ Res 23:125–140CrossRefGoogle Scholar
  24. Roy K, Das RN, Popelier PLA (2015a) Predictive QSAR modelling of algal toxicity of ionic liquids and its interspecies correlation with Daphnia toxicity. Environ Sci Pollut Res 22:6634–6641CrossRefGoogle Scholar
  25. Roy K, Kar S, Das R (2015b): Chapter 7—validation of QSAR models. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment, Academic Press, 231–289Google Scholar
  26. Rucker C, Rucker G, Meringer M (2007) y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model 47:2345–2357CrossRefGoogle Scholar
  27. Russom CL, Bradbury SP, Broderius SJ, Hammermeister DE, Drummond RA (1997) Predicting modes of toxic action from chemical structure: acute toxicity in the fathead minnow (Pimephales promelas). Environ Toxicol Chem 16:948–967CrossRefGoogle Scholar
  28. Sabljić A, Piver WT (1992) Quantitative modeling of environmental fate and impact of commercial chemicals. Environ Toxicol Chem 11:961–972CrossRefGoogle Scholar
  29. Schuurmann G, Ebert RU, Kuhne R (2011) Quantitative read-across for predicting the acute fish toxicity of organic compounds. Environ Sci Technol 45:4616–4622CrossRefGoogle Scholar
  30. Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77CrossRefGoogle Scholar
  31. Verhaar HJM, Leeuwen CJV, Hermens JLM (1992) Classifying environmental pollutants. Chemosphere 25:471–491CrossRefGoogle Scholar
  32. Wang Q, Jia QZ, Yan LH, Xia SQ, Ma PS (2014) Quantitative structure-toxicity relationship of the aquatic toxicity for various narcotic pollutants using the norm indexes. Chemosphere 108:383–387CrossRefGoogle Scholar
  33. Wang Y, Yan F, Jia Q, Dai Y, Wang Q (2017): Quantitative structure-activity relationship of anti-HIV integrase and reverse transcriptase inhibitors using norm indexes. SAR QSAR Environ Res, 1–20Google Scholar
  34. Wang YL, Yan FY, Jia QZ, Wang Q (2018) Quantitative structure-property relationship for critical micelles concentration of sugar-based surfactants using norm indexes. J Mol Liq 253:205–210CrossRefGoogle Scholar
  35. Wu X, Zhang Q, Hu J (2016) QSAR study of the acute toxicity to fathead minnow based on a large dataset. SAR QSAR Environ Res 27:147–164CrossRefGoogle Scholar
  36. Xu XY, Li L, Yan FY, Jia QZ, Wang Q, Ma PS (2016) Predicting solubility of fullerene C-60 in diverse organic solvents using norm indexes. J Mol Liq 223:603–610CrossRefGoogle Scholar
  37. Yali W, Fangyou Y, Qingzhu J, Qiang W (2017) Assessment for multi-endpoint values of carbon nanotubes: quantitative nanostructure-property relationship modeling with norm indexes. J Mol Liq:248Google Scholar
  38. Yan FS, He WS, Jia QZ, Wang Q, Xia SQ, Ma PS (2018) Prediction of ionic liquids viscosity at variable temperatures and pressures. Chem Eng Sci 184:134–140CrossRefGoogle Scholar
  39. Yin JC, Jia QZ, Yan FY, Wang Q (2017) Predicting heat capacity of gas for diverse organic compounds at different temperatures. Fluid Phase Equilib 446:1–8CrossRefGoogle Scholar
  40. Yuan H, Wang Y-Y, Cheng Y-Y (2007) Mode of action-based local QSAR modeling for the prediction of acute toxicity in the fathead minnow. J Mol Graph Model 26:327–335CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Marine and Environmental ScienceTianjin University of Science and TechnologyTianjinPeople’s Republic of China
  2. 2.School of Chemical Engineering and Material ScienceTianjin University of Science and TechnologyTianjinPeople’s Republic of China

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