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 R 2 = 0.891, Q 2 LOO = 0.851, lack-of-fit = 0.116, root mean square error (RMSE train ) = 0.267, R 2 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.
<p> A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R<sup>2</sup> = 0.95, q<sup>2 </sup>= 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R<sup>2</sup> = 0.87, q<sup>2</sup> = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.</p>
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