Antimicrobial Activity and <scp>SAR</scp> Study of New Gemini Imidazolium‐Based Chlorides

Pałkowski, Łukasz; Błaszczyński, Jerzy; Skrzypczak, Andrzej; Błaszczak, Jan; Kozakowska, Karolina; Wróblewska, Joanna; Kozuszko, Sylwia; Gospodarek, Eugenia; Krysiński, Jerzy; Słowiński, Roman

doi:10.1111/cbdd.12236

Cited by 33 publications

(24 citation statements)

References 27 publications

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“…The cmc dataset of gemini cationic surfactant were collected from multiple publications 20,21,22,23,24,25 . This dataset containing 231 compounds of gemini cationic surfactants including with their cmc value.…”

Section: Experimental Materials and Methods Data Preparationmentioning

confidence: 99%

QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

Setiawan¹,

Mudasir²,

Wijaya³

2020

Preprint

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A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R 2 = 0.95, q 2 = 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R 2 = 0.87, q 2 = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.

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Section: Experimental Materials and Methods Data Preparationmentioning

confidence: 99%

QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

Setiawan¹,

Mudasir²,

Wijaya³

2020

Preprint

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“…We analyzed 10 homologous series of gemini imidazolium chlorides with hydrophobic chain ranging from CH 3 to C 16 H 33 and with the length of spacer from C 2 to C 12 . Synthesis, surface active properties, and antimicrobial activity of a part of 140 3,3′-( α , ω -dioxaalkyl)bis(1-alkylimidazolium) chlorides were described earlier [ 22 ]. Moreover, we determined molecular descriptors for synthetized structures.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Antifungal Activity of Gemini Imidazolium Compounds

Pałkowski

Błaszczyński

Skrzypczak

et al. 2015

BioMed Research International

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The progress of antimicrobial therapy contributes to the development of strains of fungi resistant to antimicrobial drugs. Since cationic surfactants have been described as good antifungals, we present a SAR study of a novel homologous series of 140 bis-quaternary imidazolium chlorides and analyze them with respect to their biological activity against Candida albicans as one of the major opportunistic pathogens causing a wide spectrum of diseases in human beings. We characterize a set of features of these compounds, concerning their structure, molecular descriptors, and surface active properties. SAR study was conducted with the help of the Dominance-Based Rough Set Approach (DRSA), which involves identification of relevant features and relevant combinations of features being in strong relationship with a high antifungal activity of the compounds. The SAR study shows, moreover, that the antifungal activity is dependent on the type of substituents and their position at the chloride moiety, as well as on the surface active properties of the compounds. We also show that molecular descriptors MlogP, HOMO-LUMO gap, total structure connectivity index, and Wiener index may be useful in prediction of antifungal activity of new chemical compounds.

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“…Their most recent publication analyzing the SAR of antimicrobial activity of imidazolium chloride compounds was published in 2014. 99 Despite his many reports, Krysinski's work does not seem to have been taken up much by the wider SAR community. It has however been successfully used in the synthesis of new compounds with desirable antimicrobial properties.…”

Section: Applicationsmentioning

confidence: 99%

Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis

Gardiner

Gillet

2015

J. Chem. Inf. Model.

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Knowledge Discovery in Databases (KDD) refers to the use of methodologies from machine learning, pattern recognition, statistics, and other fields to extract knowledge from large collections of data, where the knowledge is not explicitly available as part of the database structure. In this paper, we describe four modern data mining techniques, Rough Set Theory (RST), Association Rule Mining (ARM), Emerging Pattern Mining (EP), and Formal Concept Analysis (FCA), and we have attempted to give an exhaustive list of their chemoinformatics applications. One of the main strengths of these methods is their descriptive ability. When used to derive rules, for example, in structure-activity relationships, the rules have clear physical meaning. This review has shown that there are close relationships between the methods. Often apparent differences lie in the way in which the problem under investigation has been formulated which can lead to the natural adoption of one or other method. For example, the idea of a structural alert, as a structure which is present in toxic and absent in nontoxic compounds, leads to the natural formulation of an Emerging Pattern search. Despite the similarities between the methods, each has its strengths. RST is useful for dealing with uncertain and noisy data. Its main chemoinformatics applications so far have been in feature extraction and feature reduction, the latter often as input to another data mining method, such as an Support Vector Machine (SVM). ARM has mostly been used for frequent subgraph mining. EP and FCA have both been used to mine both structural and nonstructural patterns for classification of both active and inactive molecules. Since their introduction in the 1980s and 1990s, RST, ARM, EP, and FCA have found wide-ranging applications, with many thousands of citations in Web of Science, but their adoption by the chemoinformatics community has been relatively slow. Advances, both in computer power and in algorithm development, mean that there is the potential to apply these techniques to larger data sets and thus to different problems in the future.

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Antimicrobial Activity and SAR Study of New Gemini Imidazolium‐Based Chlorides

Cited by 33 publications

References 27 publications

QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

Prediction of Antifungal Activity of Gemini Imidazolium Compounds

Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis

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