Quantitative structure‐property relationships for prediction of boiling point, vapor pressure, and melting point

Dearden, John C.

doi:10.1897/01-363

Cited by 124 publications

(104 citation statements)

References 140 publications

(236 reference statements)

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“…Intermolecular interactions such as dispersion, electrostatic and hydrogen bonding interactions influence the magnitude of binding affinity. Entropic factors including molecular symmetry may also stabilize the ligand-receptor interaction 67 . Ramification index (Ram) is a topological descriptor that encodes the degree of branching and cyclization of a molecule 37 .…”

Section: Important Molecular Descriptorsmentioning

confidence: 99%

PLS and shape-based similarity analysis of maleimides – GSK-3 inhibitors

Crisan

Pacureanu

Avram

et al. 2013

Journal of Enzyme Inhibition and Medicinal Chemistry

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Context: Glycogen synthase kinase-3 (GSK-3) overactivity was correlated with several pathologies including type 2 diabetes mellitus, Alzheimer's disease, cancer, inflammation, obesity, etc. Objective: The aim of the current investigation was to model the inhibitory activity of maleimide derivatives -inhibitors of GSK-3, to evaluate the impact of alignment on statistical performances of the Quantitative Structure-Activity Relationship (QSAR) and the effect of the template on shape-similarity -binding affinity relationship. Materials and methods: Dragon descriptors were used to generate Projection to Latent Structures (PLS) models in order to identify the structural prerequisites of maleimides to inhibit GSK-3. Additionally, shape/volume structural analysis of binding site interactions was evaluated. Results: Reliable statistics R 2 YðCUMÞ ¼ 0.938/0.920, Q 2 YðCUMÞ ¼ 0.866/0.838 for aligned and alignment free QSAR models and significant (Pearson, Kendall and Spearman) correlations between shape/volume similarity and affinities were obtained. Discussion and conclusions: The crucial structural features modulating the activity of maleimides include topology, charge, geometry, 2D autocorrelations, 3D-MoRSE as well as shape/volume and molecular flexibility.

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Section: Important Molecular Descriptorsmentioning

confidence: 99%

PLS and shape-based similarity analysis of maleimides – GSK-3 inhibitors

Crisan

Pacureanu

Avram

et al. 2013

Journal of Enzyme Inhibition and Medicinal Chemistry

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“…These factors include ionic, polar and hydrogen bonding forces, crystal packing, and positional, expansional, rotational, and conformational entropy effects. 1 Consequently, property prediction techniques are significantly less reliable when applied to solid properties compared to their reliability in predicting liquid and gas phase properties. [1][2][3][4][5][6] The most widely used methods for predicting T m are the "group contribution (GC)" methods.…”

Section: Introductionmentioning

confidence: 99%

“…1 Consequently, property prediction techniques are significantly less reliable when applied to solid properties compared to their reliability in predicting liquid and gas phase properties. [1][2][3][4][5][6] The most widely used methods for predicting T m are the "group contribution (GC)" methods. 7 Some of the GC methods have been already introduced into commercial software packages for predicting T m on a routine basis (e.g., the Dorthmund Data Bank, DDBST, 2011 release, http:// www.ddbst.de, and CRANIUM, Molecular Knowledge Systems, http://www.molecularknowledge.com/).…”

Section: Introductionmentioning

confidence: 99%

“…Shacham et al 17 have recently shown that properties of compounds in two homologous series can be represented (at least locally) by a linear QPPR y t 5b 0 1b 1 y r n C n C;min (1) where y r is the property value of a compound in the reference series, y t is the property of a compound (related to the reference compounds in terms of the number of carbon atoms n C ) in the target series, and b 0 and b 1 are parameters.…”

Section: Basic Principles and An Introductory Examplementioning

confidence: 99%

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Prediction of normal melting point of pure substances by a reference series method

Brauner

Shacham

2013

AIChE Journal

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The "Reference Series" method of Shacham et al. is modified to enable prediction of normal melting point temperatures (T m ) of pure substances within experimental error level. A homologous series for which large amount and high precision T m data are available is used as a "reference" series. To predict T m for a "target" series quantitative property-property relationships (QPPRs) are derived to represent the predicted T m values of the "target" series in terms of the T m values of the reference series. Two QPPRs are necessary in order to match the odd and even carbon number (n C ) oscillations of T m in the low n C region. In the high n C region, the QPPR is adjusted to represent correctly the asymptotic behavior of T m . It is shown that the method is very useful for consistency analysis of T m data and enables a reliable prediction of T m in both the low n C and the high n C regions.

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“…Pure component vapor pressure data are essential for phase equilibrium computation, process and product design, in assessing the environmental impact of a chemical compound and in modeling some types of toxicity (Dearden [2]). At present, vapor pressure data are available only for a small fraction of the compounds of interest to the chemical industry.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Refinement of the TRC-QSPR Method for Vapor Pressure Prediction

Paster¹

2011

TOTHERJ

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Abstract:Various aspects associated with the use of the TRC-QSPR method (Shacham et al., Ind. Eng. Chem. Res. 49, 900-912, 2010, Ref. [1]) for the prediction of vapor pressure are investigated using a test set of 12 compounds from the nalkane series. This test set is used to check the consistency of the parameter values of the Wagner and Riedel equations and the resulting vapor pressure values in the full range between the triple point and critical point. Inconsistency has been detected in the parameters of the commonly used version of the Riedel equation as well as the calculated vapor pressure values near the critical point, T R >0.9.Vapor pressures prediction studies are carried out for the cases of interpolation, short and long range extrapolation and using either the acentric factor ( ), or number of C atoms (n C ), or the VEA1 descriptor in the TRC-QSPR equation. It is concluded that the prediction error is the lowest and within the experimental error limits over the entire temperature range, using the Wagner's equation and within the TRC-QSPR framework. Replacing by n C or by the descriptor VEA1 increases the prediction error, however good prediction accuracy is retained in the regions where experimental data are available for the predictive compounds. It is demonstrated that reliable vapor pressure predictions can be obtained using only n C for characterization of the target compound.

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Quantitative structure‐property relationships for prediction of boiling point, vapor pressure, and melting point

Cited by 124 publications

References 140 publications

PLS and shape-based similarity analysis of maleimides – GSK-3 inhibitors

PLS and shape-based similarity analysis of maleimides – GSK-3 inhibitors

Prediction of normal melting point of pure substances by a reference series method

Analysis and Refinement of the TRC-QSPR Method for Vapor Pressure Prediction

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