Prediction of Acute Toxicity to Fathead Minnow by Local Model Based QSAR and Global QSAR Approaches

  • Received : 2011.11.22
  • Accepted : 2011.12.20
  • Published : 2012.02.20


We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.



  1. Pedersen, F.; Bruijn, J. D.; Munn, S.; Leeuwen, K. V. Assessment of Additional Testing Needs Under REACH. Effects of (Q)SARS, Risk Based Testing and Voluntary Industry Initiatives; European Commission: 2003.
  2. Ankley, G. T.; Villeneuve, D. L. Aquat. Toxicol. 2006, 78(1), 91.
  3. Casalegno, M.; Benfenati, E.; Sello, G. Chem. Res. Toxicol. 2005, 18(4), 740.
  4. Martin, T. M.; Young, D. M. Chem. Res. Toxicol. 2001, 14(10), 1378.
  5. Hoover, K. R.; Acree, W. E., Jr.; Abraham, M. H. Chem. Res. Toxicol. 2005, 18(9), 1497.
  6. Gini, G.; Craciun, M. V.; Konig, C.; Benfenati, E. J. Chem. Inf. Comput. Sci. 2004, 44(6), 1897.
  7. Mazzatorta, P.; Benfenati, E.; Neagu, C. D.; Gini, G. J. Chem. Inf. Comput. Sci. 2003, 43(2), 513.
  8. Mazzatorta, P.; Benfenati, E.; Neagu, D.; Gini, G. J. Chem. Inf. Comput. Sci. 2002, 42(5), 1250.
  9. Mazzatorta, P.; Vraèko, M.; Jezierska, A.; Benfenati, E. J. Chem. Inf. Comput. Sci. 2003, 43(2), 485.
  10. Papa, E.; Villa, F.; Gramatica, P. J. Chem. Inf. Model. 2005, 45(5), 1256.
  11. Ren, S. Chemosphere 2003, 53(9), 1053.
  12. Russom, C. L.; Bradbury, S. P.; Broderius, S. J.; Hammermeister, D. E.; Drummond, R. A. Environ. Toxicol. Chem. 1997, 16(5), 948.<0948:PMOTAF>2.3.CO;2
  13. Council Directive 92/32/EEC of 30 April 1992 amending for the seventh time Directive 67/548/EEC on the approximation of the laws, regulations and administrative provisions relating to the classification, packaging and labelling of dangerous substances. 1992; Vol. Off. J. L 154.
  14. EPA Fathead Minnow Acute Toxicity Database (EPAFHM). http:/ / (accessed Apr., 2006).
  15. Lee, S. K.; Park, S. H.; Lee, I. H.; No, K. T. PreADMET 2.0; BMDRC: Seoul, Korea, 2007.
  16. Golbraikh, A.; Tropsha, A. Molecular Diversity 2000, 5(4), 231.
  17. Riedmiller, M.; Braun, H. In A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm; Ruspini, H., Ed.; Proceedings of the IEEE International Conference on Neural Networks (ICNN) San Francisco: San Francisco, 1993; p 586.
  18. Cerius2, 4.1; Accelrys: San Diego, USA, 2005.

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