scholarly journals 2D-QSPR Study of Olfactive Thresholds for Pyrazine Derivatives Using DFT and Statistical Methods

2019 ◽  
Vol 3 (3) ◽  
pp. 179-186
Author(s):  
Assia Belhassan ◽  
Samir Chtita ◽  
Tahar Lakhlifi ◽  
Mohammed Bouachrine
Keyword(s):  
Quantum Chemical ◽  
Density Functional ◽  
Training Set ◽  
Quantum Chemical Descriptors ◽  
Qspr Model ◽  
Functional Theory ◽  
The Density Functional Theory ◽  
Chemical Descriptors ◽  
External Consistency ◽  
And Training

In this study, we have established two-dimensional quantitative structure propriety relationships (2D-QSPR) model, for a group of 78 molecules based on pyrazine, these molecules were subjected to a 2D-QSPR analyze for their odors thresholds propriety using stepwise Multiple Linear Regression (MLR). The 35 parameters are calculated for the 78 studied compounds using the Gaussian 09W, ChemOffice and ChemSketch softwares. Quantum chemical calculations are used to calculate electronic and quantum chemical descriptors, using the density functional theory (B3LYP/6-31G (d) DFT) methods.The model was used to predict the odors thresholds propriety of the test and training set compounds, and the statistical results exhibited high internal and external consistency as demonstrated by the validation methods.

scholarly journals Kernel Methods for Predicting Yields of Chemical Reactions

2021 ◽  
Author(s):  
Alexe Haywood ◽  
Joseph Redshaw ◽  
Magnus Hanson-Heine ◽  
Adam Taylor ◽  
Alex Brown ◽  
...  
Keyword(s):  
Quantum Chemical ◽  
Density Functional ◽  
Aryl Halide ◽  
Reaction Yield ◽  
Support Vector ◽  
Quantum Chemical Descriptors ◽  
Functional Theory ◽  
Molecular Fingerprints ◽  
Chemical Descriptors ◽  
Synthetic Assessment

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reac?tivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate, and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models out-performed the quantum chemical SVR models, along the dimension of each reaction compo?nent. The applicability of the models was assessed with respect to similarity to training. Prospec?tive predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalisability of the models, with particular interest along the aryl halide dimension.

Prediction of the polarity parameter π for the radical derived from monomer

e-Polymers ◽  
2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Xinliang Yu ◽  
Wenhao Yu ◽  
Bing Yi ◽  
Xueye Wang
Keyword(s):  
Neural Network ◽  
Quantum Chemical ◽  
Density Functional ◽  
Ann Model ◽  
Quantum Chemical Descriptors ◽  
Functional Theory ◽  
The Neural Network ◽  
Polarity Parameter ◽  
Chemical Descriptors ◽  
Artificial Neural Network Ann

AbstractAn artificial neural network (ANN) model was successfully developed for the modelling and prediction of the polarity parameter π used in the revised patterns scheme for the prediction of monomers reactivity ratios in radical polymerizations. Four quantum chemical descriptors based on density functional theory (DFT) calculations were used to develop the ANN model. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network 4-4-1, the results show that the predicted parameter π values are in good agreement with the experimental ones, with the root mean square (rms) errors being 0.053 (R=0.960) for the training set and 0.070 (R=0.942) for the test set. The ANN model has better statistic quality than the MLR model, which indicates there are nonlinear relationships between these quantum chemical descriptors and the parameter π.

scholarly journals Kernel Methods for Predicting Yields of Chemical Reactions

2021 ◽  
Author(s):  
Alexe Haywood ◽  
Joseph Redshaw ◽  
Magnus Hanson-Heine ◽  
Adam Taylor ◽  
Alex Brown ◽  
...  
Keyword(s):  
Quantum Chemical ◽  
Density Functional ◽  
Aryl Halide ◽  
Reaction Yield ◽  
Support Vector ◽  
Quantum Chemical Descriptors ◽  
Functional Theory ◽  
Molecular Fingerprints ◽  
Chemical Descriptors ◽  
Synthetic Assessment

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reac?tivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate, and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models out-performed the quantum chemical SVR models, along the dimension of each reaction compo?nent. The applicability of the models was assessed with respect to similarity to training. Prospec?tive predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalisability of the models, with particular interest along the aryl halide dimension.

scholarly journals Voltammetric and theoretical studies of electrochemical behavior of cephalosporins at the mercury electrode

2015 ◽  
Vol 80 (8) ◽  
pp. 1035-1049 ◽  
Author(s):  
Katarina Nikolic ◽  
Mara Aleksic ◽  
Vera Kapetanovic ◽  
Danica Agbaba
Keyword(s):  
Density Functional ◽  
Mercury Electrode ◽  
Differential Pulse ◽  
Structure Property ◽  
Qspr Model ◽  
Functional Theory ◽  
Partial Charges ◽  
The Density Functional Theory ◽  
Structure Property Relationship ◽  
Qspr Study

Study of the adsorption and electroreduction behavior of cefpodoxime proxetil, cefotaxime, desacetylcefotaxime, cefetamet, ceftriaxone, ceftazidime, and cefuroxime axetile at the mercury electrode surface has been performed using Cyclic (CV), Differential Pulse (DPV), and Adsorptive Stripping Differential Pulse Voltammetry (AdSDPV). The Quantitative Structure Property Relationship (QSPR) study of the seven cephalosporins adsorption at the mercury electrode has been based on the density functional theory DFT-B3LYP/6-31G (d,p) calculations of molecular orbitals, partial charges and electron densities of analytes. The DFT-parameters and QSPR model explain well the process of adsorption of the examined cephalosporins. QSPR study defined that cefalosporins with lower charge of sulphur in the thiazine moiety, lower electron density on the nitrogen atom of the N-O bond, higher number of hydrogen bond accepting groups, and higher principal moment of inertia should express high adsorption on the mercury electrode.

scholarly journals Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory

2020 ◽  
Vol 21 (6) ◽  
pp. 2053 ◽  
Author(s):  
Bogusław Buszewski ◽  
Petar Žuvela ◽  
Gulyaim Sagandykova ◽  
Justyna Walczak-Skierska ◽  
Paweł Pomastowski ◽  
...  
Keyword(s):  
Electrostatic Interactions ◽  
Density Functional ◽  
Stationary Phases ◽  
Retention Mechanism ◽  
Quantitative Structure ◽  
Quantum Chemical Descriptors ◽  
Functional Theory ◽  
Dispersion Effects ◽  
Chemical Descriptors ◽  
Structure Retention

This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical ‘black box’ approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations.

scholarly journals Copper (IV) Stabilization in Macrocyclic Complexes with 3,7,11,15-Tetraazaporphine, Its Di[benzo]- or Tetra[benzo] Derivatives and Oxide Anion: Quantum-Chemical Research

Materials ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3162 ◽  
Author(s):  
Oleg V. Mikhailov ◽  
Denis V. Chachkov
Keyword(s):  
Quantum Chemical ◽  
Density Functional ◽  
Nbo Analysis ◽  
Macrocyclic Complexes ◽  
Molecular Structures ◽  
Chemical Research ◽  
Functional Theory ◽  
Chemical Modeling ◽  
The Density Functional Theory ◽  
Oxidation State Of Copper

Using the data of a quantum chemical modeling of molecular structures obtained by the density functional theory (DFT), the possibility of the existence of a copper macrocyclic complexes with 3,7,11,15-tetraazaaporphine, trans-di[benzo] 3,7,11,15-tetraazaaporphine or tetra[benzo] 3,7,11,15-tetraazaaporphine and oxide anion where oxidation state of copper is IV, was shown. The values of the parameters of molecular structures and NBO analysis for such complexes were presented, too.

scholarly journals Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

2008 ◽  
Vol 6 (2) ◽  
pp. 310-318 ◽  
Author(s):  
Gui-Ning Lu ◽  
Xue-Qin Tao ◽  
Zhi Dang ◽  
Xiao-Yun Yi ◽  
Chen Yang
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Partition Coefficients ◽  
Density Functional ◽  
Optimal Model ◽  
Quantum Chemical Descriptors ◽  
Polycyclic Aromatic ◽  
Pls Analysis ◽  
Chemical Descriptors

AbstractQuantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2) of the optimal model was 0.990, and the results of crossvalidation test (Q 2cum=0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.

scholarly journals Getting excited: challenges in quantum-classical studies of excitons in polymeric systems

2016 ◽  
Vol 18 (44) ◽  
pp. 30297-30304 ◽  
Author(s):  
Behnaz Bagheri ◽  
Björn Baumeier ◽  
Mikko Karttunen
Keyword(s):  
Molecular Dynamics ◽  
Density Functional Theory ◽  
Quantum Chemical ◽  
Density Functional ◽  
Phenylene Ethynylene ◽  
Many Body ◽  
Functional Theory ◽  
Polymeric Systems ◽  
The Density Functional Theory ◽  
Classical Studies

A combination of classical molecular dynamics (MM/MD) and quantum chemical calculations based on the density functional theory (DFT) and many-body Green's functions theory (GW-BSE) was performed to describe the conformational and optical properties of diphenylethyne (DPE), methylated-DPE and poly para phenylene ethynylene (PPE).

Sign in / Sign up

Export Citation Format

Share Document