Quantitative structure–activity relationship study to predict the antibacterial activity of gemini quaternary ammonium surfactants against Escherichia coli

Ely Setiawan; Mudasir Mudasir

Abstract

Gemini quaternary ammonium surfactants (GQAS) have a unique structure built of two conventional surfactants connected by a spacer group. In previous studies, it has been found that GQAS have potency as antimicrobial agents. Thus, we developed a quantitative structure–activity relationship (QSAR) model to predict the antibacterial activity of GQAS. A dataset containing 57 GQAS with antibacterial activity against Escherichia coli was chosen from the literature. After optimizing all structures of these compounds using the ab initio 6-311G basis sets at the Hartree–Fock level theory, the molecular descriptors were calculated using the Mordred program. The genetic algorithm (GA) and multiple linear regressions (MLR) were used for generating two QSAR models with different splitting techniques. The predictive powers of the obtained models were discussed using the leave-one-out (LOO) cross-validation and external test set. The best GA-MLR models were obtained with reliable value of R² = 0.891, Q² LOO = 0.851, lack-of-fit = 0.116, root mean square error (RMSE_train) = 0.267, R² _test = 0.834, and RMSE_test= 0.269. The GA-MLR methods were used to develop models that possess good predictive ability based on both internal and external validation parameters. The design of new molecules was done, and the antibacterial activity could be predicted using the resulting model with 16 compounds that showed potential as antibacterial agents.

Keyword: Multiple linear regression molecular descriptors genetic algorithm cationic surfactants gemini quaternary ammonium GA-MLR.

Citation:

Setiawan E, Mudasir M. Quantitative structure–activity relationship study to predict the antibacterial activity of gemini quaternary ammonium surfactants against Escherichia coli. J Appl Pharm Sci, 2022; 12(07):099–105.

Copyright: © The Author(s). This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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