Molecular connectivity indices for modeling the critical micelle concentration of cationic (chloride) Gemini surfactants

Mozrzymas, Anna

doi:10.1007/s00396-016-3979-3

Cited by 19 publications

(13 citation statements)

References 31 publications

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“…These observations ensure that all these functions are positive-valued for À 1 � a < 0 and negative-valued for 0 < a � 2 (in what follows, we use the same technique for identifying the signs of all those functions which can be expressed as a linear combination of the exponential functions, without giving detail), and hence from Eq. (7) it follows that…”

Section: Resultsmentioning

confidence: 99%

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Estimating Some General Molecular Descriptors of Saturated Hydrocarbons

Ali

Shehzadi

2019

Molecular Informatics

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Three general molecular descriptors, namely the general sum-connectivity index, general Platt index and ordinary generalized geometric-arithmetic index, are studied here. Best possible bounds for the aforementioned descriptors of arbitrary saturated hydrocarbons are derived under certain constraints. These bounds are expressed in terms of number of carbon atoms and number of carboncarbon bonds of the considered hydrocarbons.

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Section: Resultsmentioning

confidence: 99%

“…[1,2] In the quantitative structure-property relationship studies, topological indices are often used to model the physicochemical properties of molecules. [6][7][8][9][10] The Platt index (Pl), which was proposed for predicting paraffin properties, [11,12] is one of the oldest topological indices. This index is defined as:…”

Section: Introductionmentioning

confidence: 99%

Estimating Some General Molecular Descriptors of Saturated Hydrocarbons

Ali

Shehzadi

2019

Molecular Informatics

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“…Furthermore, with numerous experimental data and their physicochemical properties parameters, various QSPR models have been developed for cmc prediction of cationic surfactants 16,17,18 . However, the QSPR models for cmc prediction of gemini cationic surfactants based on structurally diverse of their hydrophilic head groups (quaternary ammonium, pyridinium, imidazolium, pyrrolidinium, and piperidinium) have not been reported.…”

Section: Introductionmentioning

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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“…Several models such as multiple linear regressions (MLR) or artificial neural networks are used for developing the QSPR model 15 . Furthermore, with numerous experimental data and their physicochemical properties parameters, various QSPR models have been developed for cmc prediction of cationic surfactants 16,17,18 . However, the QSPR models for cmc prediction of gemini cationic surfactants based on structurally diverse of their hydrophilic head groups (quaternary ammonium, pyridinium, imidazolium, pyrrolidinium, and piperidinium) have not been reported.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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 R2 = 0.95, q2 = 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R2 = 0.87, q2 = 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.

show abstract

Molecular connectivity indices for modeling the critical micelle concentration of cationic (chloride) Gemini surfactants

Cited by 19 publications

References 31 publications

Estimating Some General Molecular Descriptors of Saturated Hydrocarbons

Estimating Some General Molecular Descriptors of Saturated Hydrocarbons

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

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