QSPR modeling of flash points: An update

Katritzky, Alan R.; Stoyanova-Slavova, Iva B.; Dobchev, Dimitar A.; Karelson, Mati

doi:10.1016/j.jmgm.2007.03.006

Cited by 101 publications

(89 citation statements)

References 13 publications

(13 reference statements)

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“…This method, already successfully used in previous works [22,50,43], consists in analyzing the correlation improvement with the number of variables in the model. By plotting the R² values as functions of the number of descriptors, an asymptotic behavior was observed and the improvement of correlation became less significant after a certain rank (∆R² < 0.02-0.03).…”

Section: Discussionmentioning

confidence: 99%

“…Widely used in biology [13,14], toxicology [15,16] and drug design [17,18], their applications for physico-chemical properties increased since many years [19,20], particularly for the properties of energetic materials [21][22][23][24][25][26][27][28][29][30][31][32]. Their principle consists in developing a mathematic relationship connecting a macroscopic property of a compounds series to microscopic descriptors derived from their molecular structures, using a reliable experimental data set.…”

Section: Introductionmentioning

confidence: 99%

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Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms

et al. 2010

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms

et al. 2010

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“…QSPR methods have been widely applied in the field of safety of energetic materials [25][26][27][28][29][30]. They have also been increasingly used for many years to determine physicochemical properties [31,32], especially of energetic materials [33][34][35][36][37][38][39][40][41][42][43]. QSPR is based on developing a mathematical relationship between a macroscopic property of a series of compounds and microscopic descriptors derived from their molecular structures, using an experimental data set.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Density of Energetic Materials on the Basis of their Molecular Structures

Rahimi

Keshavarz

Akbarzadeh

2016

Cent. Eur. J. Energ. Mater.

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Abstract:The density of an energetic compound is an essential parameter for the assessment of its performance. A simple method based on quantitative structureproperty relationship (QSPR) has been developed to give an accurate prediction of the crystal density of more than 170 polynitroarenes, polynitroheteroarenes, nitroaliphatics, nitrate esters and nitramines as important classes of energetic compounds, by suitable molecular descriptors. The evaluation techniques included cross-validation, validation through an external test set, and Y-randomization for multiple linear regression (MLR) and training state analysis for artificial neural network (ANN), and were used to illustrate the accuracy of the proposed models. The predicted MLR results are close to the experimental data for both the training and the test molecular sets, and for all of the molecular sets, but not as close as the ANN results. The ANN model was also used with 20 hidden neurons that gave good result. The results showed high quality for nonlinear modelling according to the squared regression coefficients for all of the training, validation and the test sets (R 2 = 0.999, 0.914 and 0.931, respectively). The calculated results have also been compared with those from several of the best available predictive methods, and were found to give more reliable estimates.

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“…At the present time, quantitative structure-property relationships (QSPR) are increasingly employed in the prediction of chemical and physical properties of different types of molecules [1][2][3][4][5][6][7][8]. The main task of QSPR is to obtain a reliable model for the prediction of properties/ behaviors of new chemical substances and analytical systems.…”

Section: Introductionmentioning

confidence: 99%

QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network

et al. 2009

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Abstract:The half-wave potential (E 1/2 ) is an important electrochemical property of organic compounds. In this work, a quantitative structureproperty relationship (QSPR) analysis has been conducted on the half-wave reduction potential (E 1/2 ) of 40 substituted benzoxazines by means of both a heuristic method (HM) and a non-linear radial basis function neural network (RBFNN) modeling method. The statistical parameters provided by the HM model (R 2 =0.946; F=152.576; RMSCV=0.0141) and the RBFNN model (R 2 =0.982; F=1034.171 and RMS =0.0209) indicated satisfactory stability and predictive ability. The obtained models showed that benzoxazines with larger Min valency of a S atom (MVSA), lower Relative number of H atom (RNHA) and Min n-n repulsion for a C-H bond (MnnRCHB) and Minimal Electrophilic Reactivity Index for a C atom (MERICA) can be more easily reduced . This QSPR approach can contribute to a better understanding of structural factors of the organic compounds that contribute to the E 1/2 , and can be useful in predicting the E 1/2 of other compounds. Warsaw and Springer-Verlag Berlin Heidelberg. © Versita

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QSPR modeling of flash points: An update

Cited by 101 publications

References 13 publications

Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms

Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms

Prediction of the Density of Energetic Materials on the Basis of their Molecular Structures

QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network

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