Prediction of Aqueous Solvation Free Energies from Properties of Solute Molecular Surface Electrostatic Potentials

Murray, Jane S.; Abu-Awwad, Fakhr M.; Politzer, Peter

doi:10.1021/jp984271w

Cited by 65 publications

(58 citation statements)

References 33 publications

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“…Many macroscopic properties of bulk energetic materials can be determined from quantum mechanical information calculated for isolated molecules. Politzer and coworkers [24][25][26][27][28][29][30][31][32] have established the quantum mechanical electrostatic calculations of the isolated molecule to determine aqueous solvation free energies, lattice energies in ionic crystals, diffusion coefficients, solubilities, boiling points, partition coefficients, critical constants and impact sensitivities. Rice and coworkers [21,33] utilized these ideas to predict heats of formation of energetic materials in the gas, liquid and solid state as well as heats of detonation from quantum mechanical computation of isolated molecule.…”

Section: Group Additivity and Quantum Mechanical Methodsmentioning

confidence: 99%

Theoretical prediction of condensed phase heat of formation of nitramines, nitrate esters, nitroaliphatics and related energetic compounds

Keshavarz

2006

Journal of Hazardous Materials

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Section: Group Additivity and Quantum Mechanical Methodsmentioning

confidence: 99%

Theoretical prediction of condensed phase heat of formation of nitramines, nitrate esters, nitroaliphatics and related energetic compounds

Keshavarz

2006

Journal of Hazardous Materials

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“…Using a GIPF expression developed earlier [70], we calculated the free energies of solvation of these nonionic and zwitterionic tautomers. Our results, when combined with the computed differences in energy between the two forms, were in very satisfactory agreement with the available experimental data and confirmed that all three molecules are zwitterions in aqueous solution.…”

Section: Zwitterionsmentioning

confidence: 99%

Molecular surface electrostatic potentials in relation to noncovalent interactions in biological systems

Politzer

Murray

Peralta-Inga

2001

Int J of Quantum Chemistry

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226

146

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Noncovalent interactions are predominantly electrostatic in nature.It follows that an effective tool for their investigation and elucidation is the electrostatic potential on the molecular surface. We have shown that a variety of condensed phase macroscopic properties can be expressed quantitatively in terms of certain site-specific and global statistical quantities that characterize the overall pattern of the surface potential. We are now extending this approach to interactions in biological systems. Several applications will be discussed, including initial qualitative studies of dioxins, a series of anticonvulsants and some tetracyclines, the nucleotide bases, and a recent quantitative treatment of the anti-HIV activities of three groups of reverse transcriptase inhibitors.

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“…In both cases, the condensed system is represented by an assembly of interacting particles: the statistical distribution of any property, or its evolution in time, is obtained as a sum over all particles with appropriate rules. These techniques have been used by Duffy and Jorgensen 20 and by Murray and co-workers 21 to correlate the ∆ solv G°of different solutes in water to MC simulation-derived and molecular electrostatic potential (MEP)-derived descriptors.…”

Section: Prediction Of the Solvation Free Energymentioning

confidence: 99%

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

Bernazzani

Duce

Micheli

et al. 2006

J. Chem. Inf. Model.

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In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/ quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, ∆ solv G°, has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the ∆ solv G°in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R ) 0.9985, standard deviation S ) 0.68 kJ mol -1 , and mean absolute error MAE ) 0.46 kJ mol -1 . The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.

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Prediction of Aqueous Solvation Free Energies from Properties of Solute Molecular Surface Electrostatic Potentials

Cited by 65 publications

References 33 publications

Theoretical prediction of condensed phase heat of formation of nitramines, nitrate esters, nitroaliphatics and related energetic compounds

Theoretical prediction of condensed phase heat of formation of nitramines, nitrate esters, nitroaliphatics and related energetic compounds

Molecular surface electrostatic potentials in relation to noncovalent interactions in biological systems

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

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