Generalized molecular descriptors

Randić, Milan

doi:10.1007/bf01200821

Cited by 259 publications

(319 citation statements)

References 22 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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“…As it was shown on numerous examples, the method allows us to generate mathematical representations directly from DNA/RNA or protein sequences, and then to derive numerical and visual characteristics. Note that the basic requirements formulated earlier by Read (1983) and Randič (1991) The ICF based tree construction (i.e. the BOOL-AN) may become more relevant for phylogenetic studies since the method is able to extract additional biologically important structural information from DNA/RNA or protein sequences (such as orientation, positions and interrelations of elements).…”

Section: Discussionmentioning

confidence: 99%

BOOL-AN: A method for comparative sequence analysis and phylogenetic reconstruction

Jakó¹,

Ari²,

Ittzés³

et al. 2009

Molecular Phylogenetics and Evolution

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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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Generalized molecular descriptors

Cited by 259 publications

References 22 publications

Prediction of Hydrate and Solvate Formation Using Statistical Models

Prediction of Hydrate and Solvate Formation Using Statistical Models

BOOL-AN: A method for comparative sequence analysis and phylogenetic reconstruction

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

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