Complete Thermodynamic Description of H‐Bonding in the Framework of Multiplicative Approach

Raevsky, Oleg A.; Va, Grigor'ev; Kireev, Dmitri; Zefirov, N. S.

doi:10.1002/qsar.19920110109

Cited by 111 publications

(67 citation statements)

References 69 publications

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“…The 5998 2D structures were processed with SaSA, 33 HYBOT, 14 Kowwin 38 and Wskowwin. 40 Principal component analysis using block scaling for the eightyseven 2D descriptors was then performed using SIMCA.…”

Section: Discussionmentioning

confidence: 99%

“…The Wiener, Balaban, Randic and Motoc indices, as well as the Kier and Hall suite of connectivity descriptors 20 are also computed in SaSA. Hydrogen-bonding capacity is estimated using HYBOT 14 descriptors. Furthermore, SaSA uses simple counts for oxygen, nitrogen, H-bond donors and H-bond acceptors, positive and negative ionization centers, as well as the maximum positive and negative charge, as calculated using electronegativity methods.…”

Section: Sasa Descriptorsmentioning

confidence: 99%

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On the information content of 2D and 3D descriptors for QSAR

Oprea¹

2002

J. Braz. Chem. Soc.

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Com o objetivo de melhor entender as informações paramétricas contidas em descritores bidimensionais (2D) e tridimensionais (3D), os escores de 87 descritores 2D e 798 variáveis 3D (ALMOND) obtidos de uma série de 5998 compostos de interesse em química medicinal, foram analisados através de análise de componentes principais. A fração de variância explicada (r 2 ) e a validação cruzada (q 2 ) para sete grupos, em duas componentes PLS, foram de 40%. Uma análise individual dos componentes, mostra que as duas primeiras PCs obtidas a partir dos descritores 2D estão relacionadas com a primeira e terceira PCs dos descritores 3D. A primeira componente 3D é explicada (61%) por descritores relacionados ao tamanho, enquanto que o conteúdo da terceira é essencialmente hidrofóbico, mas com pequena variância (25%). Surpreendentemente, descritores relacionados a ligações hidrogênio não contribuíram de forma significativa para a análise final. Estes resultados não permitem, a priori, a escolha de um método em detrimento de outro, quando da realização de estudos em QSAR.To gain better understanding on the information content of two-dimensional (2D) vs. threedimensional (3D) descriptor systems, we analyzed principal component analysis scores derived from 87 2D descriptors and 798 3D (ALMOND) variables on a set of 5998 compounds of medicinal chemistry interest. The information overlap between ALMOND and 2D-based descriptors, as modeled by the fraction of explained variance (r 2 ) and by seven-groups cross-validation (q 2 ) in a two PLS components model was 40%. Individual component analysis indicates that the first and second principal components from the 2D-descriptors are related to the first and third dimensions from the ALMOND PCA model. The first ALMOND component is explained (61%) by size-related descriptors, whereas the third component is marginally explained (25%) by hydrophobicity-related descriptors. Surprisingly, 2D-based hydrogen-bonding descriptors did not contribute significantly in this analysis. These results do not a priori justify the choice of one methodology over the other, when performing QSAR studies. Keywords: ALMOND, cheminformatics, chemometrics, QSAR IntroductionThere are currently over 3000 molecular descriptors 1 that can be used in QSAR (Quantitative Structure Activity Relationship) studies.2 Their application to QSAR has been recently surveyed.3 Significant information about a QSAR dataset can be extracted using 2D-(two-dimensional) descriptors, i.e., descriptors that do not use information related to the three-dimensional characteristics of model compounds. Most of these descriptors can be classified as: i) Size-related: molecular weight -MW; calculated 4 molecular refractivity -CMR; molecular volume and molecular surface area, pre-computed from tabulated values (e.g., using Van The above types of descriptors have been successfully used to derive QSAR models for the past four decades. However, for the past 15 years, our ability to investigate the third dimension in a meaningful way, e.g., by analyz...

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Section: Discussionmentioning

confidence: 99%

Section: Sasa Descriptorsmentioning

confidence: 99%

On the information content of 2D and 3D descriptors for QSAR

Oprea¹

2002

J. Braz. Chem. Soc.

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“…Nevertheless, correlation of the estimated C and E factor values have been carried out. 20 The next correlation was obtained for H-bond donors:…”

Section: H-bond Strength Scalesmentioning

confidence: 99%

“…24 We estimated 20 The majority of the measurements (7793 in the current version of HYBOT.DataBase) are based on measurements in tetrachloromethane (CCl 4 ). This solvent is non-polar, and is thus suitable for the estimation of H-bond parameters; 414 H-bond donors and 1298 H-bond acceptors that had data from the interaction of more than one partner were selected for the estimation of C a and C d values.…”

Section: H-bond Strength Scalesmentioning

confidence: 99%

Quantification of Non-Covalent Interactions on the Basis of the Thermodynamic Hydrogen Bond Parameters

Raevsky

1997

J. Phys. Org. Chem.

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This paper describes how empirical free energy and/or enthalpy values for hydrogen binding strength are derived from thousands of corresponding measurements between H-bond donors and acceptors, mostly in carbon tetrachloride, and how they can be used to construct common scales or factor values (increments) also for other reactions involving electron donor and acceptor ability of functions. The corresponding databases and programs (HYBOT) allow one to predict thermodynamic values for experimentally unknown equilibria, including also ionophore complexes with crown ethers or cryptands. Applications in QSAR involve the prediction of lipophilicity from any structure on the basis of only two variables, e.g. 234 systems are described this way with a correlation coefficient r = 0·96. Similarly, permeabilities and some biological properties such as narcotic activities of chemicals and anti-HIV-1 activity of some porphyrins are evaluated.

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“…Hydrogen bonds are described adequately by densityfunctional theory exchange-correlation functionals as well as by post-Hartree-Fock (HF) methods (30)(31)(32). In the classical approach, the free energy of hydrogen-bond formation can be calculated with a product of parameters characteristic of acidity and basicity of molecules (33,34). Solvent-accessible surface (SAS) area models are popular choices for nonpolar solutesolvent interactions, such as dispersion, repulsion, and cavitation (35)(36)(37)(38)(39).…”

mentioning

confidence: 99%

A generalized G-SFED continuum solvation free energy calculation model

Lee

Cho

Kang

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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An empirical continuum solvation model, solvation free energy density (SFED), has been developed to calculate solvation free energies of a molecule in the most frequently used solvents. A generalized version of the SFED model, generalized-SFED (G-SFED), is proposed here to calculate molecular solvation free energies in virtually any solvent. G-SFED provides an accurate and fast generalized framework without a complicated description of a solution. In the model, the solvation free energy of a solute is represented as a linear combination of empirical functions of the solute properties representing the effects of solute on various solute-solvent interactions, and the complementary solvent effects on these interactions were reflected in the linear expansion coefficients with a few solvent properties. G-SFED works well for a wide range of sizes and polarities of solute molecules in various solvents as shown by a set of 5,753 solvation free energies of diverse combinations of 103 solvents and 890 solutes. Octanol-water partition coefficients of small organic compounds and peptides were calculated with G-SFED with accuracy within 0.4 log unit for each group. The G-SFED computation time depends linearly on the number of nonhydrogen atoms (n) in a molecule, O(n).implicit solvent | macromolecules | linear time A n accurate description of the effect of solvent on solvation is crucial for understanding chemical and biological phenomena. Because many peptide and protein drugs are currently being developed (1, 2), computational efficiency as well as accuracy are very important. Among the different approaches that have been used, continuum solvation models are appealing because of the simplified yet accurate description of the solvent effect (3-8).Continuum solvent models have been developed based on the early work of Born (9), Bell (10), Kirkwood (11), and Onsanger (12). They focus primarily on the description of the solute either at the quantum mechanical level or as a classical collection of point charges and try to represent the influence of the solvent based on an approximate treatment as a dielectric continuum medium. The charge distribution of the solute induces electric polarization of the surrounding solvent, and the electric field generated by the polarized solvent, the reaction field, in turn perturbs the solute, leading to a modification of the charge distribution of the solute. This electrostatic problem of the mutual polarization between solute and solvent can be recast by using boundary conditions at the cavity surface, leading to significant simplifications in the associated equations.In the classical approaches, most work is concerned with describing the electrostatic contribution to the solvation in terms of the Poisson-Boltzmann equation (PBE) (13). Several different computational techniques for solving the PBE have been developed, for example, finite difference methods in which the dielectric property of the solvent is described in terms of a 3D grid around the solute (14-17) and boundary element methods i...

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Complete Thermodynamic Description of H‐Bonding in the Framework of Multiplicative Approach

Cited by 111 publications

References 69 publications

On the information content of 2D and 3D descriptors for QSAR

On the information content of 2D and 3D descriptors for QSAR

Quantification of Non-Covalent Interactions on the Basis of the Thermodynamic Hydrogen Bond Parameters

A generalized G-SFED continuum solvation free energy calculation model

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