2015
DOI: 10.1016/j.ecoenv.2015.02.027
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Quantitative structure–activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids

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Cited by 67 publications
(23 citation statements)
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References 23 publications
(26 reference statements)
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“…Biological activity of ILs was shown to depend on their hydrophilicity and hydration state/hydration number. [8][9][10][11] Starting from the assumption 'more polar-less toxic', lower cytotoxicity of ILs with polar groups was presumed, which was thus tested. Accordingly, the aim of this study was to assess the anticancer potential of imidazolium-based ionic liquids with salicylate anion in human cancer cell lines using the MTT cell viability assay, as well as antimicrobial potential of these compounds against several bacterial and Candida strains by the standard double diluted antimicrobial procedure.…”
Section: Introductionmentioning
confidence: 99%
“…Biological activity of ILs was shown to depend on their hydrophilicity and hydration state/hydration number. [8][9][10][11] Starting from the assumption 'more polar-less toxic', lower cytotoxicity of ILs with polar groups was presumed, which was thus tested. Accordingly, the aim of this study was to assess the anticancer potential of imidazolium-based ionic liquids with salicylate anion in human cancer cell lines using the MTT cell viability assay, as well as antimicrobial potential of these compounds against several bacterial and Candida strains by the standard double diluted antimicrobial procedure.…”
Section: Introductionmentioning
confidence: 99%
“…41 Finally, we have employed an additional external dataset of eight compounds (not used for developing the models), but for judging the true external predictivity of the models. 42 2.2 Development of predictive QSTR models 2.2.1 Dataset division and descriptor pre-treatment. Variance and correlation based criteria were implemented for the thinning of the descriptor pool giving predictor variables with a variance >0.0001 and an inter-correlation (r) among descriptors <0.95.…”
Section: The Dataset and Descriptorsmentioning
confidence: 99%
“…With the same approach, Das and Roy (2014a) developed a model with R 2 of 0.918 (training set) and 0.861 (test set). Peric et al (2015) developed a model for aliphatic protic ILs using the group contribution method. Basant et al (2015) suggested nonlinear models based on machine-learning approaches; i.e., cascade correlation network (CCN) and support vector machines (SVMs).…”
Section: Introductionmentioning
confidence: 99%