Quantitative Structure-property Relationship Study Research Articles

Analysis of the retention of tetracyclines on reversed-phase columns: Chemometrics, design of experiments and quantitative structure-property relationship (QSPR) study for interpretation and optimization

In this study, design of experiments was applied for the analysis of 6 reversed phase U-HPLC columns used for the separation of four tetracyclines (TCs): tetracycline, doxycycline, chlortetracycline and oxytetracycline in different elution conditions. In a first part, a fractional factorial design (24-1) was used to study the influence of four chromatographic parameters: column temperature, pH, flow rate and composition of the mobile phase (i.e. nature of the solvent used as the organic modifier), on the quality of the separation, which was evaluated in terms of peak width and resolution between two pairs of TCs. This experimental design revealed that the nature of the solvent: acetonitrile (ACN) or methanol (MeOH), and the mobile phase flow rate were the two main factors actually having the most influence on the quality of the separation. Moreover, these two factors presented an antagonistic influence according to the response considered: peak width or peak resolution. In order to understand this behavior, a Doehlert design was performed in the second part. It consisted in modeling the evolution of responses as a function of the two main factors: nature of the composition of the mobile phase (mix of ACN and MeOH, from 100% ACN to 100% MeOH) and mobile phase flow rate (from 0.3 to 0.8 mL min−1). For all the reversed phase columns studied, an inversion of the elution order of TCs and an increase of the retention factors was observed according to the composition of the organic mixture at the end of the gradient. To understand the modification of the interactions implied in the various retention modes related to the selectivity of the organic solvents used, a quantitative structure-property relationship (QSPR) study was achieved. In this final study, the molecular descriptors of each TCs were connected to its retention factor.

Internet Resources for Drug Discovery and Design.

The review describes online resources used for drug discovery and design. Internet resources can be classified into two classes. The first class of resources accumulates information about drugs, drug candidates, compounds, and bioassays. This information is a starting point in drug discovery and design. It is necessary for a training dataset composition. The data found at this step are needed in the search for the rules predicting a biological activity or recognizing active compounds among other molecules. The following databases can be used: ChEMBL, different databases of US National Institutes of Health, DrugBank, PDBind-CN Database, RCSB Protein Data Bank (PDB), BRENDA, etc. The second class of Internet resources includes web-portals performing online computations for drug discovery and design. The web-portals perform: 1) modelling of molecular structure such as geometry optimization and molecular docking; 2) online computations of various descriptors, physical-chemical and ADMET properties influencing the bioprocesses occurring in a living organism along the road of the drug therapeutic action; 3) quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies; 4) prognosis of bioactivities of compounds; 5) design of new drug candidates. These are, for example, ChemAxon, ACD/ I-lab, Mcule, OCHEM, eADMET, ChemoSophia, DockingServer, 1-click Docking, MDWeb, DockingServer, ZDOCK, etc. The role of docking online resources for modeling of "ligand-receptor" complexes, prognosis of bioactivities, and drug design is discussed. The review highlights the possibilities of Internet resources for a study of a drug action at the most important stages. A detailed assessment of the advantages of the reviewed Internet resources is done.

QSPR Study of the Retention/Release Property of Odorant Molecules in Water, Dairy and Pectin gels

Abstract As continuation of our research work on the development and prediction of retention/release property of odorant molecules in Water and pectin gels, in this research, on the one hand, we investigate to establish the quantitative structure property relationship (QSPR) of the retention/release property of odorant molecules in dairy gels using principal components analysis (PCA) and multiple linear regression (MLR). We propose quantitative models according to these analyses. The multiple linear regression (MLR) method showed a correlation coefficient of 0.901 and 0.917 for DG (dairy gel without pectin) and DG-P (dairy gel with pectin) medias, respectively. On the other hand, we compared these results with the other of our previous works to detect the pectin impacts of the retention/release property of studied compounds in different medias (with and without pectin). The comparison indicates that the effects of pectin vary according molecules: adding pectin increases the retention of some molecules, but increases the release of other molecules.

Quantitative structure-property relationship studies for the prediction of the vapor pressure of volatile organic compounds

A theoretical model (QSPR) using multiple linear regression analysis for predicting the vapor pressure (pv) of volatile organic compounds (VOCs) has been developed. A series of 51 compounds were analyzed by multiple linear regression analysis. First, the data set was separated arbitrarily into a training set (39 chemicals) and a test set (12 chemicals) for statistical external validation. A four-dimensional model was developed using as independent variables theoretical descriptors derived from Dragon software when applying the GA (genetic algorithm)?VSS (variable subset selection) procedure. The obtained model was used to predict the vapor pressure of the test set compounds, and an agreement between experimental and predicted values was verified. This model, with high statistical significance (R2 = 0.9090, Q2 LOO = 0.8748, Q2 ext = 0.8307, s = 0.24), could be used adequately for the prediction and description of the log pv value of other VOCs. The applicability domain of MLR model was investigated using a William?s plot to detect outliers and outsides compounds.

Conformation-independent quantitative structure-property relationships study on water solubility of pesticides

Water solubility is a key physicochemical parameter in pesticide control and regulation, although sometimes its experimental determination is not an easy task. In this study, we present Quantitative Structure-Property Relationships (QSPRs) for predicting the water solubility at 20 °C of 1211 approved heterogeneous pesticide compounds, collected from the online Pesticides Properties Data Base (PPDB). Validated and generally applicable Multivariable Linear Regression (MLR) models were established, including molecular descriptors carrying constitutional and topological aspects of the analyzed compounds. The most representative descriptors were selected from the exploration of a large number of about 18,000 structural variables. A hybrid approach that involves a molecular descriptor, a fingerprint, and a flexible descriptor showed the best predictive performance.

ADME properties evaluation in drug discovery: in silico prediction of blood-brain partitioning.

The absorption, distribution, metabolism and excretion properties are important for drugs, and prediction of these properties in advance will save the cost of drug discovery substantially. The ability to penetrate the blood-brain barrier is critical for drugs targeting central nervous system, which is represented by the ratio of its concentration in brain and in blood. Herein, a quantitative structure-property relationship study was carried out to predict blood-brain partitioning coefficient (logBB) of a data set consisting of 287 compounds. Four different methods including support vector machine, multivariate linear regression, multivariate adaptive regression splines and random forest were employed to build prediction models with 116 molecular descriptors selected by Boruta algorithm. The RF model had best performance in training set ([Formula: see text] = 0.938), test set ([Formula: see text] = 0.840) and tenfold cross-validation ([Formula: see text] = 0.788). Finally, we found that the polar surface area and octanol-water partition coefficient have the greatest influence on blood-brain partitioning. Results suggest that the proposed model is a useful and practical tool to predict the logBB values of drug candidates.

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.

A Quantitative Structure-Property Relationship Study of the Adsorption of Amino Acids on Kaolinite Surfaces

This article describes how the adsorption behaviors of various kinds of amino acids onto kaolinite surfaces were investigated by the quantum-chemical calculations and the quantitative structure-property relationships (QSPR). The QSPR results revealed that both adsorption energies of amino acids on tetrahedral Si-O and octahedral Al-O surfaces were mainly affected by the chemical potential and the negative of maximum negative charges of amino acids, which represent the electron flow and the hydrogen bonding between adsorbent-adsorbate interactions. The dispersion and polarization play a minor role in the amino acids adsorption on tetrahedral Si-O and octahedral Al-O surfaces, respectively.

Prediction of the Normal Boiling Points and Enthalpy of Vaporizations of Alcohols and Phenols Using Topological Indices

Establishing quantitative correlations between various molecular properties and chemical structures is of great technological importance for environmental and medical aspects. These approaches are referred to as Quantitative Structure-Property Relationships (QSPR), which relate the physico-chemical or thermodynamic properties of compounds to their structures. The main goal of QSPR studies is to find a mathematical relationship between the property of interest and a number of molecular descriptors derived from the structure of the molecule. The current study presents the relationship between the Randic′ (1χ), Balaban (J),Wiener polarity (Wp), Hyper Wiener (WW), Szeged (Sz), Harary (H) and Wiener (W) indices to the normal boiling points (Tbp, K) and the standard enthalpies of vaporization (ΔH vap 0 , kJ/mol–1) of 227 alcohols and phenols. The multiple linear regression (MLR) and backward methods were employed to give the QSPR models. After MLR analysis, we studied the validation of linearity between the molecular descriptors in the best models for used properties. The results have shown that three descriptors (W, 1χ, J) could be efficiently used for estimating the normal boiling points, and two descriptors (1χ, J) could be used for modeling and predicting the standard enthalpies of vaporization of considered compounds.

Theoretical evaluation of some α-amino acids for corrosion inhibition of copper in acidic medium: DFT calculations, Monte Carlo simulations and QSPR studies

The quantitative structure–property relationship (QSPR) models of the inhibition efficiency of seventeen α-amino acids for copper in acidic medium to their calculated reactivity indicators were developed. DFT calculations and Monte Carlo simulations were employed to find out these indicators. Both multi-linear regression (MLR) and artificial neural network (ANN) methods were employed. The most relevant global descriptors were selected using the simulated annealing algorithm. The QSPR studies showed that the inhibiting performance of the investigated compounds was influenced by their electronegativity, LUMO energy, fraction of electron transferred and total negative charge. The results show that the ANN based model exhibits a great predictive performance compared with MLR model according to correlation coefficient and the root-mean-squared error. In addition, this indicates that the corrosion inhibition of copper by these α-amino acids is mainly a complex phenomenon. Moreover, by analysis of local reactivity indicators and using the ANN constructed model, ten new designed derivative compounds with their predicted inhibition efficiency were proposed.

Thin-layer chromatography in quantitative structure-activity relationship studies

ABSTRACTThe methods of correlating molecular structure of substances expressed as descriptors, to their biological activity are commonly denoted as Quantitative Structure-Activity Relationships (QSARs). This concept, typically applied in drug discovery processes, is also widely used for correlation of molecular structure and physicochemical properties of solutes in so-called quantitative structure-property relationship (QSPR) studies, as well as for explanation of chromatographic behavior, i.e., separation mechanisms of analytes, where is termed as quantitative structure-retention relationship (QSRR). Mathematical expressions of structural characteristics of substances, named as molecular descriptors, can be calculated by various computational techniques or experimentally determined by different analytical methods. Thin-layer chromatography as a rapid, sensitive, and economical liquid chromatographic method has been widely used for determination of various chromatographic descriptors applicable in QSAR/QSPR studies. An overview of recently published papers dealing with this concept is presented.

Second-Generation Nitazoxanide Derivatives: Thiazolides are Effective Inhibitors of the Influenza a Virus

The only small molecule drugs currently available for treatment of influenza A virus (IAV) are M2 ion channel blockers and sialidase inhibitors. The prototype thiazolide, nitazoxanide, has successfully completed Phase III clinical trials against acute uncomplicated influenza. We report the activity of seventeen thiazolide analogs against A/PuertoRico/8/1934(H1N1), a laboratory-adapted strain of the H1N1 subtype of IAV, in a cell culture-based assay. A total of eight analogs showed IC50s in the range of 0.14-5.0μM. Additionally a quantitative structure-property relationship study showed high correlation between experimental and predicted activity based on a molecular descriptor set. A range of thiazolides show useful activity against an H1N1 strain of IAV. Further evaluation of these molecules as potential new small molecule therapies is justified.

Evaluation of antimicrobial activity and retention behavior of newly synthesized vanilidene derivatives of Meldrum’s acids using QSRR approach

Increased antimicrobial resistance together with the lack of new antimicrobial drugs suggest on an urgent need for new therapeutics in this field. Vanilidene derivatives of Meldrum’s acid present one of the possible approaches. In this work lipophilicity of 13 vanilidene derivatives of Meldrum’s acid as well as their predicted antimicrobial activity towards several characteristic species has been evaluated.10 vanilidene derivatives have been previously synthesized and 3 new compounds are synthetized afterwards following the same procedure. These selected 13 candidates were examined using thin layer chromatography in two different solvent systems. Gained retention parameters were a starting point for further Quantitative Structure Property Relationships (QSRR) studies in which minimum inhibitory concentration (MIC) for Candida albicans, Trichoderma viride, Penicillium italicum, Fuscarium oxysporum, Pseudomonas aeruginosa and Escherichia coli were determined. Statistically significant QSRR models were established and clustering of the compounds was performed with the help of principal component analysis (PCA) and hierarchical cluster analysis (HCA). Absorption, Distribution, Metabolism, and Excretion (ADME) properties of investigated molecules were subjected to sum of ranking differences (SRD) analysis in order to explore their pharmacokinetic properties. SRD analysis was also performed for the ranking of the established QSRR models.It was shown that compounds 6, 8 and 9 possess a significant antimicrobial activity, satisfied ADME properties and these candidates should be further optimized in order to utilize unexplored potential of Meldrum's acid in synthesis of novel antifungal compounds.

Prediction of density of energetic cocrystals based on QSPR modeling using artificial neural network

Among the most important factors affecting the destructive power of explosive materials is density. In order to correlate the molecular structure of energetic cocrystals (ECs) with their density (ρ), a quantitative structure-property relationship (QSPR) study was undertaken. An artificial neural network (ANN) model was developed to predict the density of cocrystals by using three out of more than 1600 molecular descriptors, computed by Dragon software, as input variables. The complete set of 26 ECs was randomly divided into a training set of 16, a test set of 5, and a validation set of 5 compounds. Also, multiple linear regression (MLR) analysis was utilized to build a linear model by using the same descriptors. Correlation coefficient (R2) of the ANN and MLR models (for the whole dataset) was 0.9716 and 0.9309, respectively. The ANN model was further investigated, and average absolute relative deviation for the complete dataset was 2.48%, indicating good accuracy and reliability of the model.

Synthesis, quantitative structure-property relationship study of novel fluorescence active 2-pyrazolines and application.

A variety of fluorescence-active fluorinated pyrazolines 13–33 was synthesized in good yields through cyclocondensation reaction of propenones 1–9 with aryl hydrazines 10–12. Some of the synthesized compounds provided promising fluorescence properties with quantum yield (Φ) higher than that of quinine sulfate (standard reference). Quantitative structure–property relationship studies were undertaken supporting the exhibited fluorescence properties and estimating the parameters governing properties. Five synthesized fluorescence-active pyrazolines (13, 15, 18, 19 and 23) with variable Φ were selected for treating two types of paper sheets (Fabriano and Bible paper). These investigated fluorescence compounds, especially compounds 19 and 23, provide improvements in strength properties of paper sheets. Based on the observed performance they can be used as markers in security documents.

A penalized quantitative structure–property relationship study on melting point of energetic carbocyclic nitroaromatic compounds using adaptive bridge penalty

A penalized quantitative structure–property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator () as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter (). The PBridge based model was internally and externally validated based on , , , , , , the Y-randomization test, , , , and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest of 0.959, of 0.953, of 0.949 and of 0.959, and the lowest and . For the test dataset, PBridge shows a higher of 0.945 and of 0.948, and a lower and , indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.

Prediction of High Performance Liquid Chromatography Retention Time for Some Organic Compounds Based on Ab initio QSPR Study

Analysis of the quantitative relationship between structure and characteristics of 18 polyaromatic hydrocarbons has been done by quantum Ab initio Quantitative Structure-Property Relationship (QSPR) study at Hartree Fock level of theory. Moment dipole was used as the quantum chemical descriptors, whereas molecular weight and number of rings were applied for constitutional descriptor and the valence connectivity index as steric descriptors. The compound’s electronic structure was studied by molecular modeling and retention time (Tr) data were obtained from the literature. Multi-linear regression analysis has been performed by randomly splitting the initial data set into on fitting data set and a test data set. The best result provided by QSPR analysis is the following model equation:
 log tR = 1.276 + 0.016MW+0.323Rc-0.423χ1-0.147χ2 with n =18 r=0.917 r2 =0.841 SE=0.182 Fcalc/Ftable = 5.408.
 The retention times of PAH compounds with two and three rings were successfully predicted by QSPR models.

First reformulated Zagreb indices of some classes of graphs

A topological index of a graph is a parameter related to the graph; it does not depend on labeling or pictorial representation of the graph. Graph operations plays a vital role to analyze the structure and properties of a large graph which is derived from the smaller graphs. The Zagreb indices are the important topological indices found to have the applications in Quantitative Structure Property Relationship(QSPR) and Quantitative Structure Activity Relationship(QSAR) studies as well. There are various study of different versions of Zagreb indices. One of the most important Zagreb indices is the reformulated Zagreb index which is used in QSPR study.
 In this paper, we obtain the first reformulated Zagreb indices of some derived graphs such as double graph, extended double graph, thorn graph, subdivision vertex corona graph, subdivision graph and triangle parallel graph. In addition, we compute the first reformulated Zagreb indices of two important transformation graphs such as the generalized transformation graph and generalized Mycielskian graph.

The Edge Version of Degree Based Topological Indices of HAC5C6C7[p, q] Nanotube

Let G be a simple molecular graph with vertex set V (G) and edge set E(G) respectively. The degree deg(v) of the vertex v 2 V (G) is the number of vertices adjacent with vertex v. A graph can be recognized by a numeric number, a polynomial, a sequence of numbers or a matrix. A topological index is a numeric quantity associated with a graph which characterize the topology of graph and is invariant under graph automorphism. Topological indices play important role in mathematical chemistry especially in the quantitative structureproperty relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies. In this paper we compute the edge version of some important degree based topological indices like Augmented Zagreb Index, Hyper-Zagreb Index, Harmonic Index and Sum-Connectivity Index of HAC5C6C7[p; q] Nanotube.

QSPR Study of the Retention/release Property of Odorant Molecules in Water Using Statistical Methods

An integrated approach physicochemistry and structures property relationships has been carried out to study the odorant molecules retention/release phenomenon in the water. This study aimed to identify the molecular properties (molecular descriptors) that govern this phenomenon assuming that modifying the structure leads automatically to a change in the retention/release property of odorant molecules. ACD/ChemSketch, MarvinSketch, and ChemOffice programs were used to calculate several molecular descriptors of 51 odorant molecules (15 alcohols, 11 aldehydes, 9 ketones and 16 esters). A total of 37 molecules (2/3 of the data set) were placed in the training set to build the QSPR models, whereas the remaining, 14 molecules (1/3 of the data set) constitute the test set. The best descriptors were selected to establish the quantitative structure property relationship (QSPR) of the retention/release property of odorant molecules in water using multiple linear regression (MLR), multiple non-linear regression (MNLR) and an artificial neural network (ANN) methods. We propose a quantitative model according to these analyses. The models were used to predict the retention/release property of the test set compounds, and agreement between the experimental and predicted values was verified. The descriptors showed by QSPR study are used for study and designing of new compounds. The statistical results indicate that the predicted values are in good agreement with the experimental results. To validate the predictive power of the resulting models, external validation multiple correlation coefficient was calculated and has both in addition to a performant prediction power, a favorable estimation of stability. DOI: http://dx.doi.org/10.17807/orbital.v9i4.978