Quantitative structure - Property relationship (QSPR) prediction of solvation gibbs energy of bifunctional compounds by recursive neural networks

Journal of Chemical and Engineering Data

Volumn 55, Issue 12, 2010, Pages 5425-5428

  Bernazzani, Luca ^a   Duce, Celia ^a   Micheli, Alessio ^a   Mollica, Vincenzo ^a   Tiné, Maria Rosaria ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BIFUNCTIONAL COMPOUNDS; DATA SETS; GIBBS ENERGY; HALOALKANES; NITROALKANES; POLYFUNCTIONAL ORGANIC COMPOUNDS; PREDICTIVE CAPABILITIES; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS; RECURSIVE NEURAL NETWORKS; SOLVATION PROPERTIES; STANDARD DEVIATION; STANDARD GIBBS ENERGY; STATISTICAL ANALYSIS; TEST SETS; THIOETHERS;

ACETYLENE; ALDEHYDES; AMIDES; CARBOXYLIC ACIDS; CYANIDES; ESTERS; ETHERS; FORECASTING; FUNCTIONAL GROUPS; GIBBS FREE ENERGY; KETONES; OLEFINS; PARAFFINS; SOLVATION;

NEURAL NETWORKS;

EID: 78650276283     PISSN: 00219568     EISSN: 15205134     Source Type: Journal    
DOI: 10.1021/je100535p     Document Type: Article

References (19)

1. Bianucci, A. M.; Micheli, A.; Sperduti, A.; Starita, A. Application of Cascade Correlation Networks for Structures to Chemistry Appl. Int. J. 2000, 12, 117-146
2. Micheli, A.; Sperduti, A.; Starita, A.; Bianucci, A. M. Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines J. Chem. Inf. Comput. Sci. 2001, 41, 202-218
3. Bernazzani, L.; Duce, C.; Micheli, A.; Mollica, V.; Sperduti, A.; Starita, A.; Tiné, M. R. Predicting Physical-Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks J. Chem. Inf. Model. 2006, 46, 2030-2042
4. Bertinetto, C.; Duce, C.; Micheli, A.; Solaro, R.; Starita, A.; Tiné, M. R. Prediction of the glass transition temperature of (meth)acrylic polymers containing phenyl groups by recursive neural network Polymer 2007, 48, 7121-7129
5. Micheli, A.; Sperduti, A.; Starita, A.; Bianucci, A. M. A Novel Approach to QSPR/QSAR Based on Neural Networks for Structures. In Soft Computing Approaches in Chemistry; Sztandera, L. M.; Cartwright, H. M., Eds.; Springer-Verlag: Heidelberg, Germany, 2003; pp 265-296.
6. Duce, C.; Micheli, A.; Solaro, R.; Starita, A.; Tiné, M. R. Recursive neural networks prediction of glass transition temperature from monomer structure: an application to acrylic and methacrylic polymers J. Math. Chem. 2009, 46, 729-755
7. Bertinetto, C.; Duce, C.; Micheli, A.; Solaro, R.; Starita, A.; Tiné, M. R. Evaluation of hierarchical structured representations for QSPR studies of small molecules and polymers by recursive neural networks J. Mol. Graphics Modell. 2009, 27, 797-802
8. Bini, R.; Chiappe, C.; Duce, C.; Micheli, A.; Solaro, R.; Starita, A.; Tiné, M. R. Ionic liquids: prediction of their melting points by a recursive neural network model Green Chem. 2008, 10, 306-309
9. Bertinetto, C.; Duce, C.; Micheli, A.; Solaro, R.; Tiné, M. R. Toxicity prediction by a chemoinformatic approach Ind. Chim. (Rome) 2009, 27, 136-143
10. Kollman, P. A. Advances and continuing challenges in achieving realistic and predictive simulations of the properties of organic and biological molecules Acc. Chem. Res. 1996, 29, 461-469
11. Eisenberg, D.; McLachlan, A. D. Solvation Energy in Protein Folding and Binding Nature (London) 1986, 319, 199-203
12. Duffy, E. M.; Jorgensen, W. L. Prediction of Properties from Simulations: Free Energies of Solvation in Hexadecane, Octanol, and Water J. Am. Chem. Soc. 2000, 122, 2878-2888
13. Jorgensen, W. L.; Tirado-Rives, J. Free Energies of Hydration for Organic Molecules from Monte Carlo Simulations Perspect. Drug Discovery Des. 1995, 3, 123-138
14. Cabani, S.; Gianni, P.; Mollica, V.; Lepori, L. Group Contribution to the Thermodynamic properties of Non-Ionic Organic Solutes in Dilute Aqueous Solution J. Solution Chem. 1981, 10, 563-595
15. Abraham, M. H.; Andonian-Haftvan, J.; Whiting, G. S.; Leo, A.; Taft, R. The Factors that Influence The Solubility of Gases and Vapours in Water at 298 K, and a New Method for Its Determination J. Chem. Soc., Perkin. Trans. 1994, 2, 1777-1791
16. Yaffe, D.; Cohen, Y.; Espinosa, G.; Arenas, A.; Giralt, F. A Fuzzy ARTMAP-Based Quantitative Structure-Property Relationship (QSPR) for the Henry's Law Constant of Organic Compounds J. Chem. Inf. Comput. Sci. 2003, 43, 85-112
17. Guthrie, J. P. A group equivalents scheme for free energies of formation of organic compounds in aqueous solution Can. J. Chem. 1992, 70, 1042-1054
18. Playsunov, A. V.; Shock, E. L. Group Contribution Values of the Infinite Dilution Thermodynamic Functions of Hydration for Aliphatic Noncyclic Hydrocarbons, Alcohols, and Ketones at 298.15 K and 0.1 MPa J. Chem. Eng. Data 2001, 46, 1016-1019
19. Playsunov, A. V.; Shock, E. L. Thermodynamic functions of hydration of hydrocarbons at 298.15 K and 0.1 MPa Geochim. Cosmochim. Acta 2000, 64, 439-468