Predicting the aggregation number of cationic surfactants based on ANN-QSAR modeling approaches: understanding the impact of molecular descriptors on aggregation numbers

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) m...

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Veröffentlicht in:	RSC advances 2022-11, Vol.12 (52), p.33666-33678
Hauptverfasser:	Abdous, Behnaz, Sajjadi, S. Maryam, Bagheri, Ahmad
Format:	Artikel
Sprache:	eng
Schlagworte:	Agglomeration Algorithms Aqueous solutions Artificial neural networks Cations Chemistry Correlation coefficients Molecular structure Root-mean-square errors Surfactants
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