Search for all self-avoiding paths for molecular graphs

Randić, Milan; Brissey, Gregory M.; Spencer, Robert B.; Wilkins, Charles L.

doi:10.1016/0097-8485(79)80002-9

Cited by 123 publications

(63 citation statements)

References 7 publications

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“…In other words, it is equal to the number of bonds between non-hydrogen atoms in the molecular graph. [45][46] Both spectral moments and path counts increase exponentially with the size of the molecule, so their values were subject to a logarithmic transformation [x' = ln (1+x)], i.e., SM3_H2 is obtained by the logarithmic transformation of the spectral moment, and MPC01 by the logarithmic transformation of the count described above.…”

Section: The Dichloromethane Modelmentioning

confidence: 99%

Prediction of Hydrate and Solvate Formation Using Statistical Models

Takieddin

Khimyak

Fábián

2015

Crystal Growth & Design

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Novel, knowledge-based models for the prediction of hydrate and solvate formation are introduced, which require only the molecular formula as input. A dataset of more than 19,000 organic, non-ionic and non-polymeric molecules was extracted from the Cambridge Structural Database. Molecules that formed solvates were compared with those that did not using molecular descriptors and statistical methods, which allowed the identification of chemical properties that contribute to solvate formation. The study was conducted for five types of solvates: ethanol, methanol, dichloromethane, chloroform and water solvates. The identified properties were all related to the size and branching of the molecules and to the hydrogen bonding ability of the molecules. The corresponding molecular descriptors were used to fit logistic regression models to predict the probability of any given molecule to form a solvate. The established models were 2 able to predict the behavior of ~80% of the data correctly using only two descriptors in the predictive model.

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Section: The Dichloromethane Modelmentioning

confidence: 99%

Prediction of Hydrate and Solvate Formation Using Statistical Models

Takieddin

Khimyak

Fábián

2015

Crystal Growth & Design

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“…These descriptors are topological descriptors, which do encode important information on adjacency, branching and relative distance among different functionalities in a numerical form. These molecular descriptors determine a wide range of physico-chemical properties of molecules [42,43]. Two other descriptors in the model are CH 3 R/CH 4 (C-001) and Hattached to heteroatom (H-050) [44].…”

Section: Resultsmentioning

confidence: 99%

Quantitative structure-retention relationship prediction of Kováts retention index of some organic acids

Fatemi

Elyasi

2013

Acta Chromatographica

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Summary.In this work, quantitative structure-retention relationship (QSRR) approaches were applied for modeling and prediction of the gas chromatographic retention indices of some amino acids (AAs) and carboxylic acids (CAs). The genetic algorithm (GA) method was used to select the most relevant descriptors, which are responsible for the retention of these compounds. Then, multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM) were utilized to construct the nonlinear and linear quantitative structure-retention relationship models. The obtained results revealed that the GA-ANN developed model was better than other models. This model has the average absolute relative errors of 0.043, 0.052 and 0.045 for training, internal and external test set. Applying the 10-fold cross-validation procedure on GA-AAN model obtained the statistics of Q 2 = 0.941 which revealed the reliability of this model.

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“…In recent years chemical applications of non-numerical computational methods are becoming important in several areas (29)(30)(31)(32). This paper considers another such application to molecular spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Computer generation of nuclear spin species and nuclear spin statistical weights

Balasubramanian¹

1982

J Comput Chem

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This article develops computer programs for computer generation of nuclear spin species and nuclear spin statistical weights of rovibronic levels. The programs developed here generate nuclear spin species and statistical weights from the group structures known as generalized character cycle indices (GCCIs) which are computed easily from the character table of the PI group of the molecule under consideration. Procedures are illustrated with examples.

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Search for all self-avoiding paths for molecular graphs

Cited by 123 publications

References 7 publications

Prediction of Hydrate and Solvate Formation Using Statistical Models

Prediction of Hydrate and Solvate Formation Using Statistical Models

Quantitative structure-retention relationship prediction of Kováts retention index of some organic acids

Computer generation of nuclear spin species and nuclear spin statistical weights

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