A DFT and QSAR Study of Several Sulfonamide Derivatives in Gas and Solvent 


Vol. 60,  No. 4, pp. 225-234, Aug.  2016
10.5012/jkcs.2016.60.4.225


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  Abstract

The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.

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  Cite this article

[IEEE Style]

R. S. k. Abadi, A. Alizadehdakhel, S. T. Paskiabei, "A DFT and QSAR Study of Several Sulfonamide Derivatives in Gas and Solvent," Journal of the Korean Chemical Society, vol. 60, no. 4, pp. 225-234, 2016. DOI: 10.5012/jkcs.2016.60.4.225.

[ACM Style]

Robabeh Sayyadi kord Abadi, Asghar Alizadehdakhel, and Soghra Tajadodi Paskiabei. 2016. A DFT and QSAR Study of Several Sulfonamide Derivatives in Gas and Solvent. Journal of the Korean Chemical Society, 60, 4, (2016), 225-234. DOI: 10.5012/jkcs.2016.60.4.225.

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ISSN(Print) 1017-2548
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