Catalytic Performance of Cycloalkyl-Fused Aryliminopyridyl Nickel Complexes toward Ethylene Polymerization by QSPR Modeling

Quantitative structure–property relationship (QSPR) modeling is performed to investigate the role of cycloalkyl-fused rings on the catalytic performance of 46 aryliminopyridyl nickel precatalysts. The catalytic activities for nickel complexes in ethylene polymerization are well-predicted by the obtained 2D-QSPR model, exploring the main contribution from the charge distribution of negatively charged atoms. Comparatively, 3D-QSPR models show better predictive and validation capabilities than that of 2D-QSPR for both catalytic activity (Act.) and the molecular weight of the product (Mw). Three-dimensional contour maps illustrate the predominant effect of a steric field on both catalytic properties; smaller sizes of cycloalkyl-fused rings are favorable to Act.y, whereas they are unfavorable to Mw. This study may provide assistance in the design of a new nickel complex with high catalytic performance.

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QSPR Study on n-Octanol/Water Partition Coefficient of PCDD/Fs by Three-Dimensional Holographic Vector of Atomic Interaction Field

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.356-360.83 ◽

2011 ◽

Vol 356-360 ◽

pp. 83-88 ◽

Cited By ~ 1

Author(s):

Shu Qiao ◽

Kun Xie ◽

Chuan Fu ◽

Jie Pan

Keyword(s):

Partition Coefficient ◽

Organic Pollutants ◽

Three Dimensional ◽

Qsar Model ◽

Atomic Interaction ◽

Structure Property ◽

Qspr Model ◽

Chemical Structures ◽

Interaction Field ◽

Water Partition Coefficient

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) are a group of important persistent organic pollutants. Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting the properties of environmental organic pollutants from their structure descriptors. In this study, a QSPR model is established for estimating n-octanol/water partition coefficient (log KOW) of PCDD/Fs. Three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) is used to describe the chemical structures, SMR-PLS QSAR model has been created and good correlation coefficients and cross-validated correlation coefficient is obtained. Predictive capability of the models has also been demonstrated by leave-one-out cross-validation. Moreover, the estimated values have been presented for those PCDD/Fs which are lack of experimentally data by the optimum model.

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Study of CXCR4 chemokine receptor inhibitors using QSPR and molecular docking methodologies

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633619500184 ◽

2019 ◽

Vol 18 (04) ◽

pp. 1950018

Author(s):

Tahereh Mostashari-Rad ◽

Roya Arian ◽

Houri Sadri ◽

Alireza Mehridehnavi ◽

Marzieh Mokhtari ◽

...

Keyword(s):

Molecular Docking ◽

Cancer Metastasis ◽

Docking Simulation ◽

Metabolic Stability ◽

Host Cells ◽

Structure Property ◽

Qspr Model ◽

Cxcr4 Receptor ◽

Stability And Instability ◽

Qspr Modeling

CXCR4 is involved in inflammation, cancer metastasis and also HIV-1 entry into immune host cells. In the present research, it was decided to investigate the efficacy of some CXCR4 inhibitors from both pharmacokinetics and pharmacodynamics points of view. Quantitative structure–property relationship (QSPR) approach was applied to model the metabolic stability and instability of the compounds. Using QSPR modeling, it was tried to predict the metabolic stability using new hybrid algorithm which consisted of three different steps: descriptor reduction (PCA), stable–instable classification (KNN) and biological stability prediction (PLS). In the QSPR step, it is shown that the descriptor reduction (PCA) affects the result of the classification procedure (KNN). Besides, the obtained QSPR model can predict the metabolic stability of the stable compounds with [Formula: see text] of 0.98 for train data and of 0.64 for test data. In other words, increment and decrement of stability were followed by the model. Molecular docking simulation was exploited to define the essential interactions of an effective inhibitor with CXCR4 receptor.

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Prediction of the half-lives of polychlorinated biphenyls based on the IEF-PCM calculations

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633619500330 ◽

2019 ◽

Vol 18 (07) ◽

pp. 1950033 ◽

Cited By ~ 1

Author(s):

Shiyao Liao ◽

Xinliang Yu ◽

Jianfang Chen ◽

Xianwei Huang

Keyword(s):

Density Functional ◽

Polychlorinated Biphenyl ◽

Three Dimensional ◽

Dft Method ◽

Polarizable Continuum Model ◽

Support Vector ◽

Structure Property ◽

Qspr Model ◽

The Density Functional Theory ◽

Svm Algorithm

Three-dimensional structures of 62 polychlorinated biphenyl (PCB) congeners were optimized with the integral equation formalism polarizable continuum model (IEF-PCM) in combination with the density functional theory (DFT) method at 6-31G(d) level. By applying support vector machine (SVM) algorithm, a nonlinear quantitative structure–property relationship (QSPR) model was built to predict half-lives (log [Formula: see text]) of 62 PCBs in juvenile rainbow trout. The optimal SVM model based on the parameters [Formula: see text] of 854.721 and [Formula: see text] of 0.0565 produces the root-mean-square (rms) errors of 0.0352 for the training set and 0.0446 for the test set, which are less than that of the previous models reported. The results suggest that it is feasible to build SVM models for the half-lives of PCBs with IEF-PCM and B3LYP/6-31G(d) for deriving structural descriptors.

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The key role of reduction process in enhancing the properties and catalytic performance of nanoscale copper particles anchored on three-dimensional macroporous graphene

Separation and Purification Technology ◽

10.1016/j.seppur.2020.117886 ◽

2021 ◽

Vol 257 ◽

pp. 117886

Author(s):

Yujia Yang ◽

Yujie Tao ◽

Wen Wen ◽

Qiaoru An ◽

Shuang Song ◽

...

Keyword(s):

Three Dimensional ◽

Reduction Process ◽

Catalytic Performance ◽

Copper Particles

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QSPR Study on Aqueous Solubility of PCDD/Fs by Three-Dimensional Holographic Vector of Atomic Interaction Field

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.356-360.95 ◽

2011 ◽

Vol 356-360 ◽

pp. 95-100

Author(s):

Kun Xie ◽

Shu Qiao ◽

Chuan Fu ◽

Cong Cheng

Keyword(s):

Three Dimensional ◽

Aqueous Solubility ◽

Partial Least Square ◽

Least Square ◽

Atomic Interaction ◽

Structure Property ◽

Qspr Model ◽

Chemical Structures ◽

Interaction Field ◽

Leave One Out

A quantitative structure property relationship (QSPR) model is established for estimating aqueous solubility (log SW) of PCDD/Fs. Three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) is used to describe the chemical structures, the correlation between the 3D-HoVAIF descriptors of PCDD/Fs and aqueous solubility (log SW) was established by partial least square (PLS) regression. The predictive power of the model was validated by leave-one-out cross-validated analysis. Moreover, the estimated values have been presented for those PCDD/Fs which are lack of experimentally data by the optimum model.

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Catalytic performance of bis(imino)pyridine Fe/Co complexes toward ethylene polymerization by 2D‐/3D‐QSPR modeling

Journal of Computational Chemistry ◽

10.1002/jcc.25792 ◽

2019 ◽

Vol 40 (13) ◽

pp. 1374-1386 ◽

Cited By ~ 4

Author(s):

Wenhong Yang ◽

Zhifeng Ma ◽

Jun Yi ◽

Sadia Ahmed ◽

Wen‐Hua Sun

Keyword(s):

Ethylene Polymerization ◽

Catalytic Performance ◽

Qspr Modeling

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QSPR STUDY ON DIRECT PHOTOLYSIS HALF-LIVES OF PAHs IN WATER SURFACE

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633605001817 ◽

2005 ◽

Vol 04 (03) ◽

pp. 811-822 ◽

Cited By ~ 16

Author(s):

GUI-NING LU ◽

ZHI DANG ◽

XUE-QIN TAO ◽

PING-AN PENG ◽

DE-CONG ZHANG

Keyword(s):

Molecular Orbital ◽

Water Surface ◽

Density Functional ◽

Frontier Molecular Orbital ◽

Structure Property ◽

Quantum Chemical Descriptors ◽

Direct Photolysis ◽

Qspr Model ◽

Qspr Modeling ◽

Qspr Study

Quantitative structure-property relationship (QSPR) modeling is a helpful approach used to correlate the properties of pollutants with their structure descriptors. In this paper a QSPR model for direct photolysis half-lives of polycyclic aromatic hydrocarbons (PAHs) under sunlight on the water surface was developed using density functional theory (DFT) and direct photolysis half-lives of seven PAHs without reported observed values were predicted. The quantum chemical descriptors used in this study were computed at the level of B3LYP/6–311+G(d) and analyzed by partial least squares (PLS) method. The obtained QSPR model with a correlation coefficient of 0.963 was more significant than that derived from semi-empirical molecular orbital algorithm in literatures. It was found that the eigenvalues of the frontier molecular orbital (E HOMO , E LUMO , E NLUMO and E NHOMO ) are important in governing the photolysis half-lives of PAHs in water surface, while the molecular weight (MW) and molecular total energy (TE) also have great effects on photolysis half-lives. The importance of E NLUMO and E NHOMO in the model complicates the photolytic mechanism of PAHs and they might become two useful descriptors in QSPR study on photolysis.

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WHIM-3D-QSPR APPROACH FOR PREDICTING AQUEOUS SOLUBILITY OF CHLORINATED HYDROCARBONS

Indonesian Journal of Chemistry ◽

10.22146/ijc.21650 ◽

2010 ◽

Vol 8 (1) ◽

pp. 65-71

Author(s):

Oman Zuas

Keyword(s):

Three Dimensional ◽

Chlorinated Hydrocarbons ◽

Correlation Coefficients ◽

Aqueous Solubility ◽

Absolute Error ◽

Correlation Equation ◽

Structure Property ◽

Statistical Parameters ◽

Qspr Model ◽

3D Descriptors

The weighted holistic invariant molecular-three dimensional-quantitative structure property relationship (WHIM-3D-QSPR) approach has been applied to the study of the aqueous solubility (- log Sw) of chlorinated hydrocarbon compounds (CHC's). The obtained QSPR model is predictive and only requires four WHIM-3D descriptors in the calculation. The correlation equation of the model that is based on a training set of 50 CHC's compound has statistical parameters: standard coefficient correlation (R2) = 0.948; cross-validated correlation coefficients (Q2) = 0.935; Standard Error of Validation (SEV) = 0.35; and average absolute error (AAE) = 0.31. The application of the best model to a testing set of 50 CHC's demonstrates a reliable result with good predictability. Besides, it was possible to construct new model by applying WHIM-3D-QSPR approach without require any experimental physicochemical properties in the calculation of aqueous solubility. Keywords: WHIM-3D; QSPR; aqueous solubility; - Log Sw, chlorinated hydrocarbons, CHC's

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QSPR modeling of selectivity at infinite dilution of ionic liquids

Journal of Cheminformatics ◽

10.1186/s13321-021-00562-8 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Kyrylo Klimenko ◽

Gonçalo V. S. M. Carrera

Keyword(s):

Ionic Liquids ◽

Infinite Dilution ◽

External Validation ◽

Model Development ◽

Structure Property ◽

Qspr Model ◽

Internal Validation ◽

Mixture Separation ◽

Qspr Modeling ◽

Data Points

AbstractThe intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure–Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models’ predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.

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