Property Relationship Analysis Research Articles

Predicting the dissolution rate of borosilicate glasses using QSPR analysis based on molecular dynamics simulations

AbstractQuantitative Structure Property Relationship (QSPR) analysis based on molecular dynamics (MD) simulations is a promising approach for establishing the composition‐property relationships of glasses and other materials with complex structures. A series of 20 borosilicate, aluminosilicate, and boroaluminosilicate glasses have been modeled using MD simulations with recently developed effective potentials. Short‐ and medium‐range structures of these glasses were analyzed and, based on the structural information, QSPR analysis of the initial dissolution rates (r0) of these glasses that were measured at 90°C and pH 9 by using various structural descriptors such as percentage of bridging oxygen species, network connectivity, and average ring size. The structural descriptor, Fnet, which contains both energetic information such as single bond strength and structural information such as cation coordination number and Qn distribution, was also used. It was found that while the overall network connectivity, average ring size and Fnet provide reasonable correlations with r0 of studied glasses, Fnet gives the best correlation among the descriptors. For glasses that show incongruent dissolution, it was found that modification of glass compositions to account for preferential release of modifier cations is necessary to achieve best correlations. The findings were discussed with results of recent studies on evaluating the compositional dependence of glass dissolution behavior using the topological‐constraints‐based models.

A comprehensive analysis of RAS-effector interactions reveals interaction hotspots and new binding partners

RAS effectors specifically interact with GTP-bound RAS proteins to link extracellular signals to downstream signaling pathways. These interactions rely on two types of domains, called RAS-binding (RB) and RAS association (RA) domains, which share common structural characteristics. Although the molecular nature of RAS-effector interactions is well-studied for some proteins, most of the RA/RB-domain-containing proteins remain largely uncharacterized. Here, we searched through human proteome databases, extracting 41 RA domains in 39 proteins and 16 RB domains in 14 proteins, each of which can specifically select at least one of the 25 members in the RAS family. We next comprehensively investigated the sequence–structure–function relationship between different representatives of the RAS family, including HRAS, RRAS, RALA, RAP1B, RAP2A, RHEB1, and RIT1, with all members of RA domain family proteins (RASSFs) and the RB-domain-containing CRAF. The binding affinity for RAS-effector interactions, determined using fluorescence polarization, broadly ranged between high (0.3 μM) and very low (500 μM) affinities, raising interesting questions about the consequence of these variable binding affinities in the regulation of signaling events. Sequence and structural alignments pointed to two interaction hotspots in the RA/RB domains, consisting of an average of 19 RAS-binding residues. Moreover, we found novel interactions between RRAS1, RIT1, and RALA and RASSF7, RASSF9, and RASSF1, respectively, which were systematically explored in sequence–structure–property relationship analysis, and validated by mutational analysis. These data provide a set of distinct functional properties and putative biological roles that should now be investigated in the cellular context.

Multiplicative degree based topological indices of some chemical structures in drug

In quantitative structure property relationship analysis (QSPR) and quantitative structure property relationship analysis (QSAR) the correlation between different properties/activities and molecular structure of chemical compounds is investigated which is helpful in drug design. Topological index is an useful tool to predict different physical and chemical properties of molecule by collecting information from the molecular graph. In this article, multiplicative degree based topological indices are obtained for some chemical structures widely used in drug design, especially in anticancer drug discovery. To visualize the indices, the results are interpreted graphically.

Proton transfer reaction confined within carbon nanotubes: Density functional theory and quantitative structure–property relationship analysis

In this work, we focus our attention on chemical reactions confined within carbon nanotubes. As a result of the confinement within carbon nanotubes, novel physical and chemical properties are found for the confined materials. We consider the feasibility of proton transfer inside carbon nanotubes. To do that, we have chosen formamide as the simplest real model for exhibiting the tautomerization in DNA. We have used the quantitative structure–property relationship method, based on geometry optimization and quantum chemical structural descriptors, to illustrate the potential of using the confined space inside carbon nanotubes, which will provide comprehensive information about carbon nanotubes. All calculations have been carried out using density functional theory quantum calculations with the B3LYP functional. The geometries optimized by the Gaussian program were transferred to the computer software DRAGON to calculate pertinent descriptors that could be used in the quantitative structure–property relationship model.

QSPR Models for the prediction of octanol/water partition coefficient of organophosphorous insecticides

This study aims to predict the octanol/water partition coefficient (Kow) of 43 organophosphorous insecticides. Quantitative structure- property relationship analysis was performed on a series of 43 insecticides using Multiple Linear Regression (MLR) and Support Vector Machines (SVM) methods, which correlate octanol- water partition coefficient (Kow) values of these chemicals to their structural descriptors. At first, the data set was separated with duplex algorithm into a training set (22 chemicals) and a test set (21 chemicals) for statistical external validation. The IX'XI ratio for the two data sets was 0.9839 indicating that the volumes of the regions covered by the two data sets were approximately the same. Model with four descriptors was developed using as independent variables theoretical descriptors derived from DRAGON software when applying GA (Genetic Algorithm)- VSS (Variable Subset Selection) procedure . The values of statistical parameters R2, Q2ext, SDEPext and SDEC for MLR and SVM model were: (93.57%; 92.73%; 0.493; 0.463), (98.60%; 96.30%; 0.504; 0.316); obtained for the two approaches are very similar, which confirm that our four parameters model is stable, robust and significant.

Theoretical study on Pb2VO2F5: large birefringence derived from optical anisotropies of VO2F4 groups

The Pb2VO2F5 crystal was synthesized by hydrothermal method. The crystal structure consists of tetranuclear [Pb4O6F22] and twinborn V2O4F6 units, which share the O and F atoms forming a three-dimensional framework. The birefringence of the compound was investigated using the first principles. The calculations results show that the compound has a large birefringence of 0.336 at 1064 nm. Researches indicate that its large birefringence originates mainly from the distorted VO2F4 groups based on the real-space atom-cutting method and structure–property relationships analysis. Furthermore, the IR spectrum, thermal behaviors, and UV–Vis–IR diffuse reflectance spectroscopy of the compound are also presented.

Different encoding alternatives for the prediction of halogenated polymers glass transition temperature by quantitative structure–property relationships

ABSTRACTThe glass transition temperature, Tg, is one of the most important properties of amorphous polymers. The ability to predict the Tg value of a polymer preceding its synthesis is of enormous value. For this reason it is of great value to perform a predictive quantitative structure–property relationships analysis of Tg, in this case a new set of halogenated polymers was used for this purpose. In addition, to corroborate our previous findings, the best way to encode the polymers structure for this type of studies was further tested finding that the optimal option is once more to use three monomeric units. The best linear model constructed from 153 molecular structures incorporated seven molecular descriptors and showed excellent predictive ability. Furthermore, the method showed to be very simple and straightforward for the prediction of Tg since three-dimensional descriptors are not required.

Chlorophenol sorption on multi-walled carbon nanotubes: DFT modeling and structure-property relationship analysis.

The presence of chlorophenols in drinking water can be hazardous to human health. Understanding the mechanisms of adsorption under specific experimental conditions would be beneficial when developing methods to remove toxic substances from drinking water during water treatment in order to limit human exposure to these contaminants. In this study, we investigated the sorption of chlorophenols on multi-walled carbon nanotubes using a density functional theory (DFT) approach. This was applied to study selected interactions between six solvents, five types of nanotubes, and six chlorophenols. Experimental data were used to construct structure-adsorption relationship (SAR) models that describe the recovery process. Specific interactions between solvents and chlorophenols were taken into account in the calculations by using novel specific mixture descriptors.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure–property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation.

Encoding alternatives for the prediction of polyacrylates glass transition temperature by quantitative structure–property relationships

The glass transition temperature, Tg, is one of the most important properties of amorphous polymers. The ability to predict the Tg value of a polymer prior to its synthesis it is of great value. For this reason we performed a predictive Quantitative Structure–Property Relationships (QSPR) analysis of Tg. The study explored the best way to encode the polymers structure for this type of studies, finding that the optimal option is using three monomeric units. The best linear model constructed from 126 molecular structures incorporated eight molecular descriptors and showed very good predictive ability, being a very simple and straight forward method for the prediction of Tg for polyacrylates since three dimensional descriptors were not used.

Quantitative structure–property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase

A quantitative structure–property relationship (QSPR) was developed for modeling the retention index of 1184 flavor and fragrance compounds measured using a Carbowax 20M glass capillary gas chromatography column. The 4885 molecular descriptors were calculated using Dragon software, and then were simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceeded in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptor blocks, and the last one by analyzing only 3D-descriptor families. The models were validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-many-out were applied, together with Y-randomization and applicability domain analysis. The developed model was used to estimate the I of a set of 22 molecules. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the Randić-like index from reciprocal squared distance matrix has a high relevance for this purpose.

Correlation of Drug and Carbon Nanotube Size in Encapsulation and Free Energy Calculation: A Molecular Insight

Spontaneous encapsulation of drugs into carbon nanotubes (CNTs) has attracted great interest because of their importance in biological and biomedical devices. In this work, the diameter dependence of 20 drugs on the carbon nanotube size was explored via molecular dynamics (MD) simulation. Negative free energy of interaction, reduced potential of mean force, reduced CNT–drug distance, and reduced number of water molecules after encapsulation confirm the encapsulation of the drug into the smallest possible size of CNT. The variations of the radius of gyration of the CNTs and drugs were compared to explore the correlation trend. One of the factors influencing the encapsulation and insertion of drug is the size of the nanotube. In addition, the linear correlation between the drug and the nanotube size was confirmed by quantitative structure–property relationship (QSPR) analysis, and the multiple linear regression (MLR) method was applied to develop the correlation model. The regression parameter provided by the MLR method was R2 = 0.99 for prediction of the drug gyration radius. The MLR prediction confirms that the larger drug molecule prefers to locate inside a larger CNT, which agrees with MD data. It was found that there is π–π interaction between the oxygen atoms and the aromatic ring of some of the drugs and the aromatic rings of CNTs are conjugated; this helps the drug molecule to locate inside the CNT. These theoretical methods provide a simple, detailed, and alternative method to obtain optimal tube size for encapsulation.

Multivariable linear models of structural parameters to predict methane uptake in metal–organic frameworks

A key challenge preventing CH4-driven energy future is the lack of effective, economic and safe on-board CH4 storage systems. In this study, computational tools were utilized to examine CH4 storage capacity of metal–organic frameworks (MOFs) under practical operating conditions. Grand Canonical Monte Carlo (GCMC) simulations were performed to calculate CH4 uptake capacity of 45 MOFs. Results were confirmed with experimental data available in the literature. Motivated from the good agreement between experiments and simulations, a quantitative structure–property relationship (QSPR) analysis was performed. Making use of this analysis, multivariable linear models with one-, two-, three-, and four-variables that can accurately predict CH4 uptake of MOFs at room temperature and pressures ranging from 1 to 65bar were developed. Model parameters were based on easily measurable/computable structural properties, such as pore volume, surface area, and density. Models that predict CH4 uptake at 5 and 35bar were studied in detail to investigate the viability of reaching CH4 storage target for vehicular systems set by DOE. At both pressures the models with four-variables outperformed other models at the same pressures, and void fraction (Vf) and isosteric heat of adsorption (Qst) were found to be the most significant parameters. In order for a material to exceed the DOE target of 0.5g/g CH4 uptake at a storage pressure of 35bar, the material should have high gravimetric surface area (Sg), reaching 6000m2/g, void fraction (Vf), as high as 0.9, dominant pore diameter (DPD) approximately 30Å, and Qst value around 30kJ/mol. Results of this work reveal the structural properties controlling the CH4 storage capacity of MOFs. Such information can guide experimental studies to tune the MOFs in the way of designing new materials with desirable structural properties that will reach the storage targets.

Specific Catalysis of Asparaginyl Deamidation by Carboxylic Acids: Kinetic, Thermodynamic, and Quantitative Structure–Property Relationship Analyses

Asparaginyl (Asn) deamidation could lead to altered potency, safety, and/or pharmacokinetics of therapeutic protein drugs. In this study, we investigated the effects of several different carboxylic acids on Asn deamidation rates using an IgG1 monoclonal antibody (mAb1*) and a model hexapeptide (peptide1) with the sequence YGKNGG. Thermodynamic analyses of the kinetics data revealed that higher deamidation rates are associated with predominantly more negative ΔS and, to a lesser extent, more positive ΔH. The observed differences in deamidation rates were attributed to the unique ability of each type of carboxylic acid to stabilize the energetically unfavorable transition-state conformations required for imide formation. Quantitative structure property relationship (QSPR) analysis using kinetic data demonstrated that molecular descriptors encoding for the geometric spatial distribution of atomic properties on various carboxylic acids are effective determinants for the deamidation reaction. Specifically, the number of O-O and O-H atom pairs on carboxyl and hydroxyl groups with interatomic distances of 4-5 Å on a carboxylic acid buffer appears to determine the rate of deamidation. Collectively, the results from structural and thermodynamic analyses indicate that carboxylic acids presumably form multiple hydrogen bonds and charge-charge interactions with the relevant deamidation site and provide alignment between the reactive atoms on the side chain and backbone. We propose that carboxylic acids catalyze deamidation by stabilizing a specific, energetically unfavorable transition-state conformation of l-asparaginyl intermediate II that readily facilitates bond formation between the γ-carbonyl carbon and the deprotonated backbone nitrogen for cyclic imide formation.

17β-carboxamide steroids – in vitro prediction of human skin permeability and retention using PAMPA technique

In this paper, twenty-two 17β-carboxamide steroids were synthesized from five corticosteroids (hydrocortisone, prednisolone, methylprednisolone, dexamethasone and betamethasone) in two steps. The first step was periodic acid oxydation of these corticosteroids to corresponding cortienic acids and the second step was amidation of thus obtained cortienic acids with esterified l-amino acids. These compounds are potential soft corticosteroids with local anti-inflammatory activity in the skin. Parallel artificial membrane permeability assay (PAMPA) was applied in order to predict permeability and retention of these compounds in human skin. Comparison of permeability and retention parameters between 17β-carboxamide steroids and corresponding corticosteroids was performed. Compounds with significantly higher retention were identified and the derivative that does not have significantly higher permeability was underlined. Molecular structures of all compounds were optimized by use of Gaussian semiempirical/PM3 method. Geometrical, thermodynamic, physicochemical and electronical molecular parameters of the optimized structures were calculated and quantitative structure–property relationship (QSPR) analysis was performed in order to explain permeability and retention of these compounds. ANN-, PLS- and MLR-QSPR models were created. Quality of these models was evaluated by commonly used statistical parameters and the most reliable models were selected. Analyzing descriptors in the selected models, main molecular properties that influence permeability and retention in the PAMPA artificial membrane were identified. Based on these data, further structural modifications could be applied in order to increase retention without significant increase of permeability, which can positively affect potential local anti-inflammatory activity of these compounds. Selected QSPR models could be used as in silico tool for predicting human skin permeability and retention of novel 17β-carboxamide steroids without performing PAMPA experiments.

Newly synthesized benzanthrone derivatives as prospective fluorescent membrane probes

Fluorescence spectral properties of a series of novel benzanthrone derivatives have been explored in lipid bilayers composed of zwitterionic lipid phosphatidylcholine (PC) and its mixtures with cholesterol (Chol) and anionic phospholipid cardiolipin (CL). Analysis of partition coefficients showed that all the examined compounds possess rather high lipid-associating ability, with the amidino derivatives exhibiting stronger membrane partitioning compared with the aminobenzanthrones. To understand how benzanthrone partition properties correlate with their structure, quantitative structure property relationship (QSPR) analysis was performed involving a range of quantum chemical molecular descriptors.

Prediction of liquid‐crystalline property using support vector machine classification

A quantitative structure–property relationships analysis applied on a large dataset of aromatic organic compounds was performed using a methodology based on support vector machine (SVM). The liquid‐crystalline property was correlated with a series of structural and geometric molecular descriptors: length of the rigid and flexible sections, molecular mass, and an asymmetry parameter that quantifies the ratio between the total length and molecule diameter. The SVM classification was achieved, analyzing the influence of characteristic elements of this tool: type of kernel and parameters of the method. A comparison with other techniques (eager learners and lazy learners algorithms and neural networks) was made using the same dataset and working conditions. The accurate results provided by the SVM method, the small number of parameters, and ease of handling recommend the SVM tool as an appropriate technique for classification. Copyright © 2013 John Wiley & Sons, Ltd.

Large-Scale Quantitative Structure–Property Relationship (QSPR) Analysis of Methane Storage in Metal–Organic Frameworks

Metal–organic frameworks (MOFs) present a combinatorial design challenge. The structural building blocks of MOFs can be combined to synthesize a nearly infinite number of materials. This suggests that computational tools, rather than experimental trial and error, can be used for high-throughput screening. Here, in the context of methane storage, we report the first large-scale, quantitative structure–property relationship (QSPR) analysis of MOFs. We investigated the effect of geometrical features, such as pore size and void fraction, on the simulated methane storage capacities of ∼130 000 hypothetical MOFs at 1, 35, and 100 bar at 298 K. From these data we developed models that can predict methane storage with high accuracy, based only on knowledge of the geometric features. Several models were developed: multilinear regression (MLR) models, decision trees (DTs), and nonlinear support vector machines (SVMs). In each case, 10 000 MOF structures were used to “train” the QSPR regression models, and the accur...

Molecular recognition of indole derivatives by polymers imprinted with indole-3-acetic acid: A QSPR study

Three molecularly imprinted polymers (MIPs) were prepared using the phytohormone indole-3-acetic acid (IAA) as a template molecule, 4-vinylpyridine (MIP-1 and MIP-2) or N,N-dimethylaminoethyl methacrylate (MIP-3) as functional monomers, ethylenglycol dimethacrylate as a cross linker and acetonitrile (MIP-1), a methanol–water mixture (MIP-2) or chloroform (MIP-3) as porogens. Retention factors for IAA and 29 indole derivatives were determined by high-performance liquid chromatography, using the molecularly imprinted polymers as stationary phases and acetonitrile as an eluent. High correlations between selectivity factors of above mentioned polymers indicate that their retention mechanisms are basically the same. A quantitative structure–property relationships analysis revealed that the presence of the terminal carboxyl group on the 3-side chain plays an essential role in the binding of the indole derivatives to the polymers. The derivatives without the carboxyl group exhibit a drastically lower affinity toward the polymers. Another factor which favors the binding is electronic density of indole nucleus. Substituents with electro-withdrawing properties enhance the binding, while electro-donating substituents have the opposite effect. The length of the 3-side chain also affects the binding. Indole-3-carboxylic acid having the carboxyl group directly attached to the ring as well as the derivatives whose side chain is longer than that of IAA bind to the polymers with a lower affinity.

Analysis of amino acid–water interactions by partitioning in aqueous two-phase systems. I—Amino acids with non-polar side-chains

Partition ratios of 10 l-amino acids with non-polar side chains (Gly, Ala, Val, nor-Val, Ile, Leu, nor-Leu, Phe, Trp and Pro) were measured in ten different polymer/polymer aqueous two-phase systems (ATPS) containing 0.15M NaCl in 0.01M phosphate buffer, pH 7.4. The solute-specific coefficients representing the solute dipole–dipole, hydrogen bonding and electrostatic interactions with aqueous environment for the compounds examined were determined by the multiple linear regression analysis using the modified linear solvation energy relationship equation. It is shown that linear combinations of these coefficients are correlated with the amino acid lipophilicity/hydrophobicity scales reported in the literature. The results obtained imply that the solute-specific coefficients may be used as solute descriptors for quantitative structure–property relationship (QSPR) analysis.