QSAR modeling for predicting the antifungal activities of gemini imidazolium surfactants against Candida albicans using GA-MLR methods

This report presents a Quantitative Structure–-Activity Relationships (QSAR) analysis of gemini imidazolium surfactants against Candida albicans. Mordred software is used to calculate various types of molecular descriptors. The data set contains 70 structures of gemini imidazolium surfactants and is divided into training set (75) and test set (25) to perform cross-validation step. Genetic algorithm technique combined with multiple linear regression method (GA-MLR) was used to investigate the correlation between molecular descriptors and antifungal activity of gemini imidazolium surfactants. As a result, the best GA-MLR model consisting of two topological descriptors (GATS4se and BalabanJ) exhibits good fitting and internal validation with R2 = 0.9073, Q2 LOO = 0.8941, and Q2 LMO = 0.8908. Also, it was confirmed by the external validation procedure with R2 test = 0.8988 and RMSEtest = 0.3557, indicating that the obtained model was robust, reliable, and strong to predict the antifungal activity of gemini imidazolium surfactants. The GA-MLR-QSAR could be a useful tool for the initial development and design of novel gemini imidazolium surfactant as antifungal agents. © 2021 Ely Setiawan et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).