ArticleRSC advances2022
Predicting the aggregation number of cationic surfactants based on ANN-QSAR modeling approaches: understanding the impact of molecular descriptors on aggregation numbers.
Article in RSC advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Structural and biochemical insights into the antibiofilm activity of cationic lipoamino acid ester against methicillin-resistantRSC advances · 2026Article
- Fate of Benzalkonium Chloride in Nanofiltration and Reverse Osmosis: Mechanisms of Retention and Membrane Response.Molecules (Basel, Switzerland) · 2026Article
- The pursuit of accurate predictive models of the bioactivity of small molecules.Chemical science · 2024Review
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3 authors.
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Abstract
In this work, a quantitative structure-activity relationship (QSAR) study is performed on some cationic surfactants to evaluate the relationship between the molecular structures of the compounds with their aggregation numbers (AGGNs) in aqueous solution at 25 °C. An artificial neural network (ANN) model is combined with the QSAR study to predict the aggregation number of the surfactants. In the ANN analysis, four out of more than 3000 molecular descriptors were used as input variables, and the complete set of 41 cationic surfactants was randomly divided into a training set of 29, a test set of 6, and a validation set of 6 molecules. After that, a multiple linear regression (MLR) analysis was utilized to build a linear model using the same descriptors and the results were compared statistically with those of the ANN analysis. The square of the correlation coefficient (
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