Prediction of toxicity of aliphatic carboxylic acids using adaptive neuro-fuzzy inference system

Journal of the Iranian Chemical Research

مقاله 7، دوره 5، شماره 3، آذر 2012، صفحه 177-185   

نویسنده

Vali Zare-Shahabadi^*

Young Researchers Club, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran

چکیده

Toxicity of 38 aliphatic carboxylic acids was studied using non-linear quantitative structure-toxicity
relationship (QSTR) models. The adaptive neuro-fuzzy inference system (ANFIS) was used to construct the
nonlinear QSTR models in all stages of study. Two ANFIS models were developed based upon different
subsets of descriptors. The first one used log ow K and LUMO E as inputs and had good prediction ability; for
the training set of 28 compounds 2
Training R was 0.86 and for the test set of 10 compounds, the corresponding
statistic was 2
Test R =0.97. Two outliers were detected for this ANFIS model and removing them improved the
quality of the model. Another ANFIS model was constructed based on PEOE_VSA_FPNEG and G3u
descriptors chosen by exhaustive search of all two combinations of calculated descriptors by Dragon and
MOE softwares. The later ANFIS model showed better performance than the former ( 2
Training R =0.92 and
2
Test R =0.90) and no outlier was detected.

کلیدواژه‌ها

Quantitative-structure-activity relationship؛ Adaptive neuro-fuzzy inference system؛ Aliphatic carboxylic acids؛ Toxicity؛ Tetrahymena pyriformis

مراجع

[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.