Prediction of melting points of a diverse chemical set using fuzzy regression tree
محورهای موضوعی : Journal of the Iranian Chemical Research
Vali Zare-Shahabadi
1
(Department of Chemistry, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran)
کلید واژه: Classification, Ant colony system, Regression tree, Melting points,
چکیده مقاله :
The classification and regression trees (CART) possess the advantage of being able to handlelarge data sets and yield readily interpretable models. In spite to these advantages, they are alsorecognized as highly unstable classifiers with respect to minor perturbations in the training data.In the other words methods present high variance. Fuzzy logic brings in an improvement in theseaspects due to the elasticity of fuzzy sets formalism. ACS, which is a meta-heuristic algorithmand derived from the observation of real ants, was used to optimize fuzzy parameters. Thepurpose of this study was to explore the use of fuzzy regression tree (RT) for modeling ofmelting points of a large variety of chemical compounds. To test the ability of the resulted tree, aset of approximately 4173 structures and their melting points were used (3000 compounds astraining set and 1173 as validation set). Further, an external test set contains of 277 drugs wereused to validate the prediction ability of the tree. Comparison the results obtained from both treesshowed that the fuzzy RT performs better than that produced by recursive partitioning procedure.
[1] L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth,
Monterey, 1984.
[2] R. Jang, Neuro-Fuzzy and Soft Computing, Prentice Hall, NJ, 1997.
[3] S. Izrailev, D. Agrafiotis, J. Chem. Inf. Comput. Sci. 41 (2001) 176-180.
[4] V. Zare-Shahabadi, F. Abbasitabar, J. Compt. Chem. 31 (2010) 2354-2362.
[5] M. Shamsipur, V. Zare-Shahabadi, B. Hemmateenejad, M. Akhond, J. Chemometrics 20 (2006) 146-
157.
[6] S.H. Yalkowsky, S.C. Valvani, J. Pharm. Sci. 69 (1980) 912-922.
[7] S.H. Yalkowsky, J. Pharm. Sci. 70 (1981) 971-973.
[8] Y. Ran, S.H. Yalkowsky, J. Chem. Inf. Comput. Sci. 41 (2001) 354-357.
[9] A. Gavezzotti, J.Chem. Soc., Perkin Trans. 2 (1995) 1399-1404.
[10] M. Karthikeyan, R.C. Glen, A. Bender, J. Chem. Inf. Model. 45 (2005) 581-590.
[11] J.C. Dearden, Sci. Total Environ. 109/110 (1991) 59-68.
[12] M. Charton, B. Charton, J. Phys. Org. Chem. 7 (1994) 196-206.
[13] A.R. Katritzky, U. Maran, M. Karelson, V.S. Lobanov, J. Chem. Inf. Comput. Sci. 37 (1997) 913-
919.
[14] M. Charton, J. Comput.-Aided Mol. Des. 17 (2003) 197-209.
V. Zare-Shahabadi / J. Iran. Chem. Res. 4 (2011) 97-103
103
[15] C. A. Bergstrom, U. Norinder, K. Luthman, P. Artursson, J. Chem. Inf. Comput. Sci. 43 (2003)
1177-1185.
[16] L. Ma, C. Cheng, J. Chemom. 16 (2002) 75-80.
[17] K. J. Burch, E. G. Whitehead, J. Chem. Eng. Data 49 (2004) 858-863.
[18] 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.
[19] M. Dorigo, Optimization, Learning and Natural Algorithms, Ph.D. Thesis, Politecnico di Milano,
Italy, 1992.
[20] M. Dorigo, T. Stutzle, Ant Colony Optimization, MIT Press, New York, 2004.
[21] M. Shamsipur, V. Zare-Shahabadi, B. Hemmateenejad, M. Akhond, QSAR & Comb. Sci. 28 (2009)
1263-1275.
[22] V. Zare-Shahabadi, Ant colony optimization and its applications in analytical chemistry, Ph.D.
Thesis, Shiraz University, Shiraz, Iran, 2008.
