Prediction of boiling points of organic heterocyclic compounds using regression and neural network techniques

Egolf, Leanne M.; Jurs, Peter C.

doi:10.1021/ci00014a015

Cited by 57 publications

(58 citation statements)

References 5 publications

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“…Grigoras [132] furans, tetrahydrofurans the authors concluded that due to structural differences between nitrogen heterocycles and sulfur and oxygen heterocycles, various connectivity, electronic, constitutional and CPSA descriptors cannot adequately encode enough information for a combined set of heterocycles Stanton et al [134] furans, tetrahydrofurans, thiophenes, pyrans 299 MLR, NN both methods had the same quality of prediction for the training set Egolf and Jurs [135], Egolf et al [136] pyridines 572 for pyridines, in the case of the cross-validation set, the NNs outperformed conventional QSPR; descriptors that reflect hydrogen bonding and dipoledipole interactions improved the predictive models for the pyridines data set diverse organic compounds 298 for this set the back-propagation NN combination resulted in 1K improvement over the MLR alkanes 150 NN 10:7:1 architecture; the performance was slightly better in comparison with the MLR methods…”

Section: Overview Of Qspr Approachesmentioning

confidence: 99%

The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors

Katritzky

Fara²,

Petrukhin³

et al. 2002

CTMC

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Section: Overview Of Qspr Approachesmentioning

confidence: 99%

The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors

Katritzky

Fara²,

Petrukhin³

et al. 2002

CTMC

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“…Selection of descriptors. There are several possibilities for the definition of descriptors as input variables for a neural network: number of atoms, number of single bonds, molar mass, dipole moment and topological parameters concerning the connectivity between atoms [2]. In our investigation the descriptors for the input layer are the surface fractions of the functional groups within a molecule and the temperature.…”

Section: Generation and Description Of The Datamentioning

confidence: 99%

Neural Networks and Evolutionary Algorithms for the Prediction of Thermodynamic Properties for Chemical Engineering

Mandischer

Geyer

Ulbig

1999

Lecture Notes in Computer Science

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Abstract. In this paper 1 we report results for the prediction of thermodynamic properties based on neural networks, evolutionary algorithms and a combination of them. We compare backpropagation trained networks and evolution strategy trained networks with two physical models. Experimental data for the enthalpy of vaporization were taken from the literature in our investigation. The input information for both neural network and physical models consists of parameters describing the molecular structure of the molecules and the temperature. The results show the good ability of the neural networks to correlate and to predict the thermodynamic property. We also conclude that backpropagation training outperforms evolutionary training as well as simple hybrid training.

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“…With the increased need for reliable data for optimization of industrial processes, it is important to develop reliable QSPR models to estimate normal boiling points for compounds not yet synthesized or whose boiling points are unknown. Several references [7][8][9] were made to investigations regarding the relationship between the normal boiling-point (NBP) and molecular structure descriptors. Many methods for prediction of BPs have therefore been developed, including many quantitative structure-property relationship (QSPR) studies using multiple linear regression (MLR) [10][11][12][13][14] and neural network (NN) Methods [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Prediction the Normal Boiling Points of Primary, Secondary and Tertiary Liquid Amines from their Molecular Structure Descriptors

Saaidpour

Bahmani

Rostami

2015

CMST

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In this article, at first, a quantitative structure-property relationship (QSPR) model for estimation of the normal boiling point of liquid amines is developed. QSPR study based multiple linear regression was applied to predict the boiling points of primary, secondary and tertiary amines. The geometry of all amines was optimized by the semi-empirical method AM1 and used to calculate different types of molecular descriptors. The molecular descriptors of structures were calculated using Molecular Modeling Pro plus software. Stepwise regression was used for selection of relevance descriptors. The linear models developed with Molegro Data Modeller (MDM) allow accurate estimate of the boiling points of amines using molar mass (MM), Hansen dispersion forces (DF), molar refractivity (MR) and hydrogen bonding (HB) (1• and 2• amines) descriptors. The information encoded in the descriptors allows an interpretation of the boiling point studied based on the intermolecular interactions. Multiple linear regression (MLR) was used to develop three linear models for 1• , 2• and 3• amines containing four and three variables with a high precision root mean squares error, 15.92 K, 9.89 K and 15.76 K and a good correlation with the squared correlation coefficient 0.96, 0.98 and 0.96, respectively. The predictive power and robustness of the QSPR models were characterized by the statistical validation and applicability domain (AD).

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Prediction of boiling points of organic heterocyclic compounds using regression and neural network techniques

Cited by 57 publications

References 5 publications

The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors

The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors

Neural Networks and Evolutionary Algorithms for the Prediction of Thermodynamic Properties for Chemical Engineering

Prediction the Normal Boiling Points of Primary, Secondary and Tertiary Liquid Amines from their Molecular Structure Descriptors

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