Quantitative structure–activity relationship study to predict the antibacterial activity of gemini quaternary ammonium surfactants against Escherichia coli
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) m...
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| Main Authors: | , |
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| 格式: | Other NonPeerReviewed |
| 語言: | English |
| 出版: |
Proceedings - 2022 8th International Conference on Science and Technology, ICST 2022
2022
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| 主題: | |
| 在線閱讀: | https://repository.ugm.ac.id/284208/1/160.Quantitative-structureactivity-relationship-study-to-predict-the-antibacterial-activity-of-gemini-quaternary-ammonium-surfactants-against-Escherichia-coliJournal-of-Applied-Pharmaceutical-Science.pdf https://repository.ugm.ac.id/284208/ https://japsonline.com/abstract.php?article_id=3703&sts=2 |
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| 機構: | Universitas Gadjah Mada |
| 語言: | English |
| 總結: | 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 (RMSEtrain) = 0.267, R2
test = 0.834, and RMSEtest = 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. |
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