Quantitative Structure−Activity Relationship for Cyclic Imide Derivatives of Protoporphyrinogen Oxidase Inhibitors:  A Study of Quantum Chemical Descriptors from Density Functional Theory

2004 ◽  
Vol 44 (6) ◽  
pp. 2099-2105 ◽  
Author(s):  
Jian Wan ◽  
Li Zhang ◽  
Guangfu Yang ◽  
Chang-Guo Zhan
Keyword(s):  
Quantum Chemical ◽  
Density Functional ◽  
Quantitative Structure Activity Relationship ◽  
Cyclic Imide ◽  
Quantum Chemical Descriptors ◽  
Functional Theory ◽  
Protoporphyrinogen Oxidase ◽  
Structure Activity ◽  
Chemical Descriptors ◽  
Derivatives Of

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 QUANTUM CHEMICAL STUDY FOR THE TOXICITY PREDICTION OF SULFONAMIDE ANTIBIOTICS WITH QUANTITATIVE STRUCTURE – ACTIVITY RELATIONSHIP

2020 ◽  
Vol 51 (1) ◽  
pp. 7-13
Author(s):  
S. Aydogdu ◽  
Arzu Hatipoglu
Keyword(s):  
Correlation Coefficient ◽  
Quantum Chemical ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Quantum Chemical Descriptors ◽  
Structure Activity ◽  
Qsar Models ◽  
Chemical Descriptors

Sulfonamides are one of the most important classes of chemicals found in the aquatic environment as a pollutant due to excessive consumption. The DFT- B3LYP method with the basis set 6-311++G (d,p) was employed to calculate various quantum chemical descriptors of sulfonamide molecules. A quantitative structure activity relationship (QSAR) study was performed for the toxicity value LD50 of sulfonamides with their quantum chemical descriptors by multi linear regression. The QSAR models were validated by internally and externally. The best multilinear equation with correlation coefficient, R and the cross-validation leave-one-out correlation coefficient, Q2 values were 0.9528 ,0.8556 respectively The results show that the QSAR models have both favourable estimation stability and good prediction power.

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 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.

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