QSPR Study of Critical Micelle Concentration of Anionic Surfactants Using Computational Molecular Descriptors

Katritzky, Alan R.; Pacureanu, Liliana; Dobchev, Dimitar A.; Karelson, Mati

doi:10.1021/ci600462d

Cited by 32 publications

(17 citation statements)

References 44 publications

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“…Authors also identified the octanolwater partition coefficient and the dipole moment as suitable measures of the cmc. Finally, the model used was as follows: [53] investigate a data set of 119 diverse anionic surfactants to relate the logarithm of critical micelle concentration (cmc) to the molecular structure using Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA Pro) software. The regression equation obtained contained variables that include features of both hydrophobic and hydrophilic fragments of the surfactant: In this equation the t-sum-Kier & Hall index of 0th order is the sum of the Kier & Hall index of zeroth order over all hydrophobic tails; the total dipole of the molecule is the AM1 calculated total dipole moment of the molecule; and the h-sum relative number of carbon atoms is the sum of the relative number of carbon atoms over all hydrophilic heads.…”

Section: Quantitative Structure Activity Rela-tionship (Qsar) Studiesmentioning

confidence: 99%

Self-Assembly Drugs: From Micelles to Nanomedicine

Messina¹,

Besada-Porto²,

Ruso

2014

CTMC

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Self-assembly has fascinated many scientists over the past few decades. Rapid advances and widespread interest in the study of this subject has led to the synthesis of an ever-increasing number of elegant and intricate functional structures with sizes that approach nano-and mesoscopic dimensions. Today, it has grown into a mature field of modern science whose interfaces with many disciplines have provided invaluable opportunities for crossing boundaries for scientists seeking to design novel molecular materials exhibiting unusual properties, and for researchers investigating the structure and function of biomolecules. Consequently, self-assembly transcends the traditional divisional boundaries of science and represents a highly interdisciplinary field including nanotechnology and nanomedicine. Basically, self-assembly focuses on a wide range of discrete molecules or molecular assemblies and uses physical transformations to achieve its goals. In this Review, we present a comprehensive overview of the advances in the field of drug self-assembly and discuss in detail the synthesis, self-assembly behavior, and physical properties as well as applications. We refer the reader to past reviews dealing with colloidal molecules and colloidal self-assembly. In the first part, we will discuss, compare, and link the various bioinformatic procedures: Molecular Dynamics and Quantitative Structure Activity Relationship. The second section deals with the self-assembly behavior in more detail, in which we focus on several experimental techniques, selected according to the depth of knowledge obtained. The last part will review the advances in drug-protein assembly. Nature provides many examples of proteins that form their substrate binding sites by bringing together the component pieces in a process of self-assembly. We will focus in the understanding of physical properties and applications developing thereof.

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Section: Quantitative Structure Activity Rela-tionship (Qsar) Studiesmentioning

confidence: 99%

Self-Assembly Drugs: From Micelles to Nanomedicine

Messina¹,

Besada-Porto²,

Ruso

2014

CTMC

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“…Both of these QSPR models were obtained using MLR approach over a set of descriptors based on molecular topology and constitution. Apart from the work of Anoune et al [91], QSPR models intended to the CMC prediction were trained over specific chemical families that are: nonionic [89,[92][93][94][95][96][97][98], cationic [99][100][101], and anionic [90,94,[102][103][104][105][106][107] surfactants. Table 1 presents statistical coefficients of QSPR models listed in the literature.…”

Section: Cmc Predictionmentioning

confidence: 99%

“…This table shows that most of these models have been developed using MLR and we can remark that the size of the database varied from few tens to less than two hundred compounds. Katritzky [96] and Katritzky et al [100,106] have published the QSPR models for the CMC learned using the most comprehensive databases. In these two last works, authors present comparisons between MLR and ANN models.…”

Section: Cmc Predictionmentioning

confidence: 99%

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

Creton

Nieto‐Draghi

Pannacci

2012

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Oil & Gas Science and Technology -Rev. IFP Energies nouvelles, Vol. 67 (2012), No. 6, pp. 969-982 Copyright c 2013, IFP Energies nouvelles DOI: 10.2516 Prediction of Surfactants' Properties using Multiscale Molecular Modeling Tools: A Review B. Creton, C. Nieto-Draghi and N. Pannacci Bois-Préau, 92852 Rueil-Malmaison -France e-mail: benoit.creton@ifpen.fr -carlos.nieto@ifpen.fr -nicolas.pannacci@ifpen.fr Résumé -Prédiction de propriétés des tensioactifs à l'aide d'outils de modélisation moléculaire : une revue -Une des voies possibles de récupération assistée du pétrole, l'EOR (Enhanced Oil Recovery), consiste en l'injection d'un fluide ASP (Alkaline/Surfactant/Polymer) dans le réservoir dans le but de déplacer le pétrole piégé vers le puits de production. La conception et/ou l'optimisation de mélanges ASP, de tensioactifs ou de mélanges de tensioactifs est donc d'un intérêt premier pour améliorer l'efficacité d'un tel procédé. Les codes de simulation moléculaire développés et largement validés durant ces dernières décennies apparaissent comme des outils incontournables pour la compréhension des effets microscopiques, la prédiction de propriétés de tensioactifs complexes ou encore l'optimisation des structures voire de la composition de mélanges de tensioactifs. Dans cet article, nous présentons une revue des travaux de la littérature sur le potentiel de diverses techniques de simulation moléculaire pour la prédiction de propriétés structurales ou thermophysiques des tensioactifs. Les techniques de simulation auxquelles nous nous sommes intéressés sont la dynamique moléculaire (MD), les simulations Monte Carlo (MC), la dissipative particle dynamics (DPD) ainsi que des approches statistiques faisant un lien direct entre structure et propriété (QSPR, pour Quantitative Structure-Property Relationship). IFP Energies nouvelles, 1-4 Avenue de Abstract -Prediction of Surfactants' Properties using Multiscale Molecular Modeling Tools: A Review -During one of the existing Enhanced Oil Recovery (EOR) procedures, a mixture of Alkaline/Surfactant/Polymer (ASP) is injected into wells in order to INTRODUCTIONThe actual capacity of oil extraction still remains limited and can be roughly estimated to 30-60%, or more, of the reservoir's original oil within the considered field [1]. The production process is typically split into tree distinct phases: -the primary recovery which is the consequence of natural effects such as the pressure of the reservoir; -the secondary recovery which consists in injecting water or gas to move the oil to the wellbore; -the tertiary recovery or Enhanced Oil Recovery (EOR), which gathers techniques such as thermal recovery, chemical or microbial injection [2][3][4]. The chemical injection technique can involve combinations of Alkaline/Surfactant/Polymer (ASP) in which the alkali reacts with some of the crude oil components decreasing the water/oil InterFacial/surface Tension (IFT) [5]. Surfactants are used to reduce the water/oil IFT and the role of Multiscale molecular modeling tools from qua...

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“…Among all the class of surfactants, anionic surfactants are used in greater volume due to the ease and low cost of manufacture and their wide application in industry and domestic area like detergency, emulsifying agents, solubilizing agents, dispersing agents, fire fighting foams, antistating agents, antifogging agents, adhesives, coatings, pharmaceutical adjuvants etc. (Katritzky et al, 2007;Attwood and Florence, 1983). The most commonly used anionic surfactants are the salts of linear and branched alkyl sulfates and alkane sulfonates, substituted alkane sulfonates, heteroatom containing sulfates and sulfonates (O, N, S), phosphate esters, etc.…”

Section: Introductionmentioning

confidence: 99%

“…The most commonly used anionic surfactants are the salts of linear and branched alkyl sulfates and alkane sulfonates, substituted alkane sulfonates, heteroatom containing sulfates and sulfonates (O, N, S), phosphate esters, etc. (Katritzky et al, 2007).…”

Section: Introductionmentioning

confidence: 99%