Prediction of toxicity of aliphatic carboxylic acids using adaptive neuro-fuzzy inference system
محورهای موضوعی : Journal of the Iranian Chemical Research
1 - Young Researchers Club, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
کلید واژه: Toxicity, Quantitative-structure-activity relationship, Adaptive neuro-fuzzy inference system, Aliphatic carboxylic acids, Tetrahymena pyriformis,
چکیده مقاله :
Toxicity of 38 aliphatic carboxylic acids was studied using non-linear quantitative structure-toxicityrelationship (QSTR) models. The adaptive neuro-fuzzy inference system (ANFIS) was used to construct thenonlinear QSTR models in all stages of study. Two ANFIS models were developed based upon differentsubsets of descriptors. The first one used log ow K and LUMO E as inputs and had good prediction ability; forthe training set of 28 compounds 2Training R was 0.86 and for the test set of 10 compounds, the correspondingstatistic was 2Test R =0.97. Two outliers were detected for this ANFIS model and removing them improved thequality of the model. Another ANFIS model was constructed based on PEOE_VSA_FPNEG and G3udescriptors chosen by exhaustive search of all two combinations of calculated descriptors by Dragon andMOE softwares. The later ANFIS model showed better performance than the former ( 2Training R =0.92 and2Test R =0.90) and no outlier was detected.
[1] M. Jorgensen, B. Vendelbo, N.E. Skakkebaek, H. Leffers, Environ. Health Perspect. 108 (2000) 403-412.
[2] J. Devillers, SAR QSAR Environ. Res. 15 (2004) 237-249.
[3] B. Hemmateenejad, M. Elyasi, Anal. Chim. Acta 646 (2009) 30-38.
[4] A. Mohajeri, M.H. Dinpajooh, Journal of Molecular Structure: THEOCHEM 855 (2008) 1-5.
[5] B. Hemmateenejad, A.R. Mehdipour, R. Miri, M. Shamsipur, Chemical Biology & Drug Design 75
(2010) 521-531.
[6] Z. Bayat, S. Vahdani, J. Chem. Pharm. Res. 3 (2011) 93-102.
[7] C.L. Russom, S.P. Bradbury, S.J. Broderius, D.E. Hammermeister, R.A. Drummond, Environ. Toxicol.
Chem. 16 (1997) 948-967.
V. Zare-Shahabadi, J. Iranian Chem. Res. 5 (3) (2012) 177-185
185
[8] H. Könemann, A. Musch, Toxicology 19 (1981) 223-228.
[9] J.R. Seward, T.W. Schultz, SAR QSAR Environ. Res. 10 (1999) 557-567.
[10] M. Kompany-Zareh, Med. Chem. Res. 18 (2009) 143-157.
[11] X.J. Cui, Z.Y. Zhang, X. Yuan, J.P. Zhang, S.D. Liu, L.P. Guo, P.D.B. Harrington, Chem. Res. Chinese.
U. 22 (2006) 439-442.
[12] S. Kar, K. Roy, J. Hazard. Mater. 177 (2010) 344-351.
[13] F. Abbasitabar, V. Zare-Shahabadi, SAR QSAR Environ. Res. 23 (2011) 1-15.
[14] V. Zare-Shahabadi, F. Abbasitabar, J. Comput. Chem. 31 (2010) 2354-2362.
[15] A. Niazi, S. Jameh-Bozorghi, D. Nori-Shargh, Chin. Chem. Lett. 18 (2007) 621-624.
[16] S. Funar-Timofei, D. Ionescu, T. Suzuki, Toxicol. In Vitro 24 (2010) 184-200.
[17] B. Xia, K. Liu, Z. Gong, B. Zheng, X. Zhang, B. Fan, Ecotoxicol. Environ. Saf. 72 (2009) 787-794.
[18] M. Jalali-Heravi, A. Kyani, Chemosphere 72 (2008) 733-740.
[19] A. Niazi, S. Jameh-Bozorghi, D. Nori-Shargh, J. Hazard. Mater. 151 (2008) 603-609.
[20] A. Khajeh, H. Modarress, Expet. Syst. Appl. 37 (2010) 3070-3074.
[21] P. Shahbazikhah, M. Asadollahi-Baboli, R. Khaksar, R. Fareghi Alamdari, V. Zare-Shahabadi, J. Braz.
Chem. Soc. 22 (2011) 1446-1451.
[22] R.K.H. Galvão, M.C.U. Araujo, G.E. José, M.J.C. Pontes, E.C. Silva, T.C.B. Saldanha, Talanta 67
(2005) 736-740.
[23] J. Gan, S.M. Zhou, A New Fuzzy Membership Function with Applications in Interpretability
Improvement of Neurofuzzy Models Computational Intelligence, in: DS Huang, K Li, G Irwin (Eds.),
Springer Berlin / Heidelberg2006, pp. 183-194.
[24] M. Setnes, R. Babuska, H.B. Verbruggen, International Journal of Human-Computer Studies 49 (1998)
159-179.
[25] M. Sugeno, G.T. Kang, Fuzzy Sets and Systems 28 (1988) 15-33.
[26] M. Jalali-Heravi, P. Shahbazikhah, A. Ghadiri-Bidhendi, QSAR & Combinatorial Science 27 (2008)
729-739.
[27] H. Takagi, M. Sugeno, IFAC Symposium on Fuzzy Information, Knowledge Representation and
Decision Analysis1983, p. 55.
[28] J.S.R. Jang, IEEE Trans. Syst. Man Cybern. 23 (1993) 665-685.
