Quantitative Structure-retention Relationship Study of Polychlorinated Dibenzothiophenes by Molecular Electronegativity Distance Vector(MEDV)

Polychlorinated dibenzothiophenes(PCDTs) are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship(QSRR) analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector(MEDV) descriptors and multiple linear regression(MLR) and partial least-squares regression(PLS) methods.The correlation coefficient R of established MLR,PLS models,leave-one-out(LOO) cross-validation(CV),Q2ext were 0.9951,0.9942,0.9839(MLR) and 0.9925,0.9915,0.9833(PLS),respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.

Quantitative Structure-retention Relationship Study of Volatile Components from Rosa Banksiae Ait

In the present study,(QSRR) study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression (MLR) stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients (R2≥0.734) and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute (ShpA) and logP value play a vital role in determining component's GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q+,and SEV is also embodied in models,but they are not dominant.

Three-dimensional holographic vector of atomic interaction field for quantitative structure-retention relationship of purine bases

A newly developed descriptor, three- dimensional holographic vector of atomic interaction field (3D-HoVAIF), was used to describe the chemical structures of purine bases. After variable screening by stepwise multiple regression (SMR) technique, a partial least square (PLS) regression model was built with 3D-HoVAIF. The model was satisfactory com- paring to reference since correlation coefficients of molecular modeling ( Rc 2um), cross- validation ( Qc 2um) and standard deviation of estimation (SD) were 0.966, 0.860 and 0.112, respectively, showing that the model had favorable estimation and prediction capa- bilities. It was illustrated that information related to retention data of purine bases could preferably be expressed by 3D-HoVAIF with definite physico- chemical meanings and easy structural interpretation for purine bases. It was illustrated that 3D-HoVAIF was to preferably express retention data of purine bases and had definite physicochemical significance. So 3D-HoVAIF was a useful structural expression technique for quantitative structure activity (or prop- erty or retention) relationships (QSAR/QSPR/QSRR) study, such as structural characterization and chro- matographic retention prediction.

A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships(QSRR) models.First,the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties(SGBP),involving physicochemical,quantum chemical,topological,spatial structural properties,etc.;thereafter,the sequence data are transformed into a uniform matrix by auto cross covariance(ACC).ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence;hence,this method adequately takes the neighboring effect into account.Then,a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonucleotides.Finally,a support vector machine is used to develop QSRR models to predict chromatographic retention behavior.The whole dataset is divided into pairs of training sets and test sets with different proportions;as a result,it has been found that the QSRR models using more than 26 training samples have an appropriate external power,and can accurately represent the relationship between the features of sequences and structures,and the retention times.The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information,easy manipulation and high characterization competence.Moreover,the method can further be applied to predict chromatographic retention behavior of oligonucleotides.

A novel molecular distance edge vector as applied to chemical modeling of quantitative structure-retention relationships:Various gas chromatographic retention behaviors of polychlorinated dibenzofurans on different polarity-varying stationary phases

Based on the identical group as a pseudo atom instead of a typical atom, a novel modified molecular dis-tance-edge (MDE) vector μ was developed in our laboratory to characterize chemical structure of polychlorinated diben-zofurans (PCDFs) congeners and/or isomers. Quantitative structure-retention relationships (QSRRs) between the new VMDE parameters and gas chromatographic (GC) retention behavior of PCDFs were then generated by multiple linear regression (MLR) method for non-polar, moderately polar, and polar stationary phases. Four excellent models with high correlation coefficients, R=0.984-0.995, were proposed for non-polar columns (DB-5, SE-54, OV-101). For the moder-ately polar columns (OV-1701), the correlation coefficient of the developed good model is only 0.958. For the polar col-umns (SP-2300), the QSRR model is poor with R=0.884. Then cross validation with leave-one out of procedure (CV) is performed in high correlation with the non-polar (Rcv=992-0.974) and weakly polar (Rcv=921) columns and in little cor-relation (Rcv=0.834) with the polar columns. These results show that the new μ vector is suitable for describing the re-tention behaviors of PCDFs on non-polar and moderately polar stationary phases and not for the various gas chroma-tographic retention behaviors of PCDFs on the different po-larity-varying stationary phases.

Quantitative structure-retention relationship for polychlorinated dibenzofurans based on molecular interaction field analysis

A new quantitative structure-retention relationship (QSRR) model is developed for polychlorinated dibenzofurans (PCDFs) based on molecular interaction field (MIF) analysis. The MIF of all 135 PCDFs is calculated using DRY, C1= and C3 probe, characterizing the hydrophobic and steric interaction between PCDFs and different groups of stationary phase. Then QSRR model is constructed by multiblock partial least squares (MBPLS), and the significance of each block is evaluated by the block importance in the prediction (BIP) method. The model used for prediction is statistically significant, with calibration and cross-validation correlation coefficients 0.9990 and 0.9980 respectively, and relative error less than 1.0%. The results of MBPLS and BIP show that the steric properties have dominant influence on the retention behavior of PCDFs, and then the hydrophobic effects.

Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.

Quantitative relationship between surface sedimentary diatoms and water depth in North-Central Bohai Bay,China

To study the quantitative relationship between surface sedimentary diatoms and water depth,67 surface samples were collected for diatom analysis on eight profiles with water depth variation from the muddy intertidal zone to the shallow sea area in North-Central Bohai Bay,China.The results showed that the distribution of diatoms changed significantly in response to the change in water depth.Furthermore,the quantitative relationship between the distribution of dominant diatom species,their assemblages,and the water depth was established.The water depth optima for seven dominant species such as Cyclotella striata/stylorum,Paralia sulcata,and Coscinodiscus perforatus and the water depth indication range of seven diatom assemblages were obtained in the study area above the water depth(elevation)of-10 m.The quantitative relationship between surface sedimentary diatoms and water depth provides a proxy index for diatom-paleo-water depth reconstruction in the strata in Bohai Bay,China.

Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data

The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.

Genotoxicity of substituted nitrobenzenes and the quantitative structure－activity relationship

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Quantitative structure-activity relationship study on the biodegradation of acid dyestuffs

Quantitative structure-biodegradability relationships (QSBRs) were established to develop predictive models and mechanistic explanations for acid dyestu?s as well as biological activities. With a total of four descriptors, molecular weight (MW), energies of the highest occupied molecular orbital (EHOMO), the lowest unoccupied molecular orbital (ELUMO), and the excited state (EES), calculated using quantum chemical semi-empirical methodology, a series of models were analyzed between the dye biodegradability and each descriptor. Results showed that EHOMO and MW were the dominant parameters controlling the biodegradability of acid dyes. A statistically robust QSBR model was developed for all studied dyes, with the combined application of EHOMO and MW. The calculated biodegradations fitted well with the experimental data monitored in a facultative-aerobic process, indicative of the reliable prediction and mechanistic character of the developed model.

Study on 3D Quantitative Structure-Activity Relationships of Taxoid

A series of 94 taxol analogues exhibiting antitumor activity were investigated using comparative molecular ficld analsis (CoMFA) method. The most optimal CoMFA from 80 compounds selected randomly . yielded a two-components model, with siginficant cross-validation r cv of 0.640 and conventional r of 0.868. The robustness of the CoMFA model was further validatal by prediedon Of 14 test set componds. The CoMFA model explained present SAR and prtcularly the importance of the hydroxyl group at C-2’ position and the stereochemistry at C-2’ and C-3’

Quantitative relationship between flagellate abundance and suspended particle density in Huanghai Sea and East China Sea in summer

调查在 Huanghai 海和华东 Seato 学习被执行在许多之间的量的关系鞭打并且在 2001 的夏天的推迟的粒子的密度。许多鞭打的结果表演从 44 ～ 1 改变 2600 cell/cm~3，并且有时鞭打组成推迟的粒子的重要部分。推迟的粒子的尺寸光谱能被划分成四个范畴：扁平的光谱，弓着的光谱，浮游生物光谱和混合光谱。一般来说，许多鞭打与推迟的粒子的密度成比例变化。然而，他们的量的关系在不同类型 ofparticle 尺寸光谱的海水样品揭示不同特征。这仅仅是在之间的量的关系的初步的研究鞭打并且推迟了粒子，它可能导致一条潜在的方便途径到评价鞭打在海的丰富。

Quantitative Structure-activity Relationship Study on the Antioxidant Activity of Carotenoids

Carotenoids are a family of effective active oxygen scavengers, which can reduce the danger of occurrence of chronic diseases such as cardiovascular disease, cataract, cancer, and so on. The quantitative structure-activity relationship (QSAR) equation between carotenoids and antioxidant activity was established by quantum chemistry AM1, molecular mechanism (MM+) and stepwise regression analysis methods, and the model was evaluated by leave-one-out approach. The results showed that the significant molecular descriptors related to the antioxidant activity of carotenoids were the energy difference (EHL) between the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) and ionization energy (Eiso). The model showed a good predictive ability (Q2 > 0.5).

A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices (χ), modified molecular connectivity indices ( mχth ) and valance molecular connectivity indices ( mχv ), with mχv calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression (MLR) method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

Estimation of surface tension of organic compounds using quantitative structure-property relationship

A novel quantitative structure-property relationship(QSPR) model for estimating the solution surface tension of 92 organic compounds at 20 °C was developed based on newly introduced atom-type topological indices.The data set contained non-polar and polar liquids,and saturated and unsaturated compounds.The regression analysis shows that excellent result is obtained with multiple linear regression.The predictive power of the proposed model was discussed using the leave-one-out(LOO) cross-validated(CV) method.The correlation coefficient(R) and the leave-one-out cross-validation correlation coefficient(RCV) of multiple linear regression model are 0.991 4 and 0.991 3,respectively.The new model gives the average absolute relative deviation of 1.81% for 92 substances.The result demonstrates that novel topological indices based on the equilibrium electro-negativity of atom and the relative bond length are useful model parameters for QSPR analysis of compounds.

Study on the Quantitative Structure-toxicity Relationships for the Selected Esters by Using Molecular Electronegativity Interaction Vector (MEIV)

The molecular electronegativity interaction vector (MEIV) was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression (MLR) and partial least-squares regression (PLS). The correlation coefficients (R) of the two models were 0.945 and 0.941, respectively. The models were evaluated by performing the cross validation with the leave-one-out (LOO) procedure. The cross-verification correlation coefficients (RCV) of the two models were 0.921 and 0.919, respectively. The results showed that the models constructed in this work could provide estimation stability and favorable predictive ability.

Relationship of quantitative structure and pharmacokinetics in fluoroquinolone antibacterials

AIM: To study the relationship between quantitative structure and pharmacokinetics (QSPkR) of fluoroquinolone antibacterials. METHODS: The pharmacokinetic (PK) parameters of oral fluoroquinolones were collected from the litera- ture. These pharmacokinetic data were averaged, 19 compounds were used as the training set, and 3 served as the test set. Genetic function approximation (GFA) module of Cerius2 software was used in QSPkR analysis. RESULTS: A small volume and large polarizability and surface area of substituents at C-7 contribute to a large area under the curve (AUC) for fluoroquinolones. Large polarizability and small volume of substituents at N-1 contribute to a long half life elimination. CONCLUSION: QSPkR models can contribute to some fluoroquinolones antibacterials with excellent pharmacokinetic properties.

Simulation of ^(13)C NMR chemical shifts of carbinol carbon atoms using quantitative structure-spectrum relationships

A quantitative structure-spectrum relationship (QSSR) model was developed to simulate 13C nuclear magnetic resonance (NMR) spectra of carbinol carbon atoms for 55 alcohols. The proposed model,using multiple linear regression,contained four descriptors solely extracted from the molecular structure of compounds. The statistical results of the final model show that R2= 0.982 4 and S=0.869 8 (where R is the correlation coefficient and S is the standard deviation). To test its predictive ability,the model was further used to predict the 13C NMR spectra of the carbinol carbon atoms of other nine compounds which were not included in the developed model. The average relative errors are 0.94% and 1.70%,respectively,for the training set and the predictive set. The model is statistically significant and shows good stability for data variation as tested by the leave-one-out (LOO) cross-validation. The comparison with other approaches also reveals good performance of this method.

New Descriptors of Amino Acids and Its Applications to Peptide Quantitative Structure-activity Relationship

A new set of descriptors,HSEHPCSV (component score vector of hydrophobic,steric,and electronic properties together with hydrogen bonding contributions),were derived from principal component analyses of 95 physicochemical variables of 20 natural amino acids separately according to different kinds of properties described,namely,hydrophobic,steric,and electronic properties as well as hydrogen bonding contributions. HSEHPCSV scales were then employed to express structures of angiotensin-converting enzyme inhibitors,bitter tasting thresholds and bactericidal 18 peptide,and to construct QSAR models based on partial least square (PLS). The results obtained are as follows:the multiple correlation coefficient (R2cum) of 0.846,0.917 and 0.993,leave-one-out cross validated Q2cum of 0.835,0.865 and 0.899,and root-mean-square error for estimated error (RMSEE) of 0.396,0.187and 0.22,respectively. Satisfactory results showed that,as new amino acid scales,data of HSEHPCSV may be a useful structural expression methodology for the studies on peptide QSAR (quantitative structure-activity relationship) due to many advantages such as plentiful structural information,definite physical and chemical meaning and easy interpretation.