Prediction of the aqueous solvation free energy of organic compounds by using autocorrelation of molecular electrostatic potential surface properties combined with response surface analysis.

Several quantitative structure-property relationship (QSPR) approaches have been explored for the prediction of aqueous solubility or aqueous solvation free energies, DeltaG(sol), as crucial parameter affecting the pharmacokinetic profile and toxicity of chemical compounds. It is mostly accepted that aqueous solvation free energies can be expressed quantitatively in terms of properties of the molecular surface electrostatic potentials of the solutes. In the present study we have introduced autocorrelation molecular electrostatic potential (autoMEP) vectors in combination with nonlinear response surface analysis (RSA) as alternative 3D-QSPR strategy to evaluate the aqueous solvation free energy of organic compounds. A robust QSPR model (r(cv)=0.93) has been obtained by using a collection of 248 organic chemicals. An external test set based on 23 molecules confirmed the good predictivity of the autoMEP/RSA model suggesting its further applicability in the in silico prediction of water solubility of large organic compound libraries.

[1]  Magdalena Bacilieri,et al.  Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as alternative attractive tool to generate ligand-based 3D-QSARs. , 2005, Current drug discovery technologies.

[2]  I. Kuntz,et al.  Inclusion of Solvation in Ligand Binding Free Energy Calculations Using the Generalized-Born Model , 1999 .

[3]  L. Rosenfeld,et al.  Theory of electrons , 1951 .

[4]  Magdalena Bacilieri,et al.  The application of a 3D-QSAR (autoMEP/PLS) approach as an efficient pharmacodynamic-driven filtering method for small-sized virtual library: application to a lead optimization of a human A3 adenosine receptor antagonist. , 2006, Bioorganic & medicinal chemistry.

[5]  Johann Gasteiger,et al.  Berechnung der Ladungsverteilung in konjugierten Systemen durch eine Quantifizierung des Mesomeriekonzeptes , 1985 .

[6]  J. Gasteiger,et al.  ITERATIVE PARTIAL EQUALIZATION OF ORBITAL ELECTRONEGATIVITY – A RAPID ACCESS TO ATOMIC CHARGES , 1980 .

[7]  J. Hine,et al.  Structural effects on rates and equilibriums. XIX. Intrinsic hydrophilic character of organic compounds. Correlations in terms of structural contributions , 1975 .

[8]  Andreas Zell,et al.  Locating Biologically Active Compounds in Medium-Sized Heterogeneous Datasets by Topological Autocorrelation Vectors: Dopamine and Benzodiazepine Agonists , 1996, J. Chem. Inf. Comput. Sci..

[9]  J. Zupan,et al.  REPRESENTATION OF MOLECULAR ELECTROSTATIC POTENTIALS BY TOPOLOGICAL FEATURE MAPS , 1994 .

[10]  Jonathan W. Essex,et al.  Prediction of Properties from Simulations: A Re-examination with Modern Statistical Methods , 2005, J. Chem. Inf. Model..

[11]  Magdalena Bacilieri,et al.  Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as new strategy for the prediction of the activity of human A(3) adenosine receptor antagonists. , 2005, Journal of medicinal chemistry.

[12]  Stefano Moro,et al.  Tandem 3D-QSARs approach as a valuable tool to predict binding affinity data: design of new Gly/NMDA receptor antagonists as a key study. , 2007, Journal of chemical information and modeling.

[13]  A. Ben-Naim,et al.  A possible involvement of solvent-induced interactions in drug design. , 1996, Journal of medicinal chemistry.

[14]  C. Cramer,et al.  Implicit Solvation Models: Equilibria, Structure, Spectra, and Dynamics. , 1999, Chemical reviews.

[15]  Polina V. Oliferenko,et al.  A General Treatment of Solubility. 1. The QSPR Correlation of Solvation Free Energies of Single Solutes in Series of Solvents , 2003, J. Chem. Inf. Comput. Sci..

[16]  F. J. Luque,et al.  Theoretical Methods for the Description of the Solvent Effect in Biomolecular Systems. , 2000, Chemical reviews.

[17]  Hwanho Choi,et al.  Prediction of Molecular Solvation Free Energy Based on the Optimization of Atomic Solvation Parameters with Genetic Algorithm , 2007, J. Chem. Inf. Model..

[18]  W. L. Jorgensen,et al.  Prediction of Properties from Simulations: Free Energies of Solvation in Hexadecane, Octanol, and Water , 2000 .

[19]  Arup K. Ghose,et al.  Prediction of Solvation Free Energies of Small Organic Molecules: Additive-Constitutive Models Based on Molecular Fingerprints and Atomic Constants , 1999, J. Chem. Inf. Comput. Sci..

[20]  J. Gasteiger,et al.  Autocorrelation of Molecular Surface Properties for Modeling Corticosteroid Binding Globulin and Cytosolic Ah Receptor Activity by Neural Networks , 1995 .

[21]  Stephen R. Johnson,et al.  Recent progress in the computational prediction of aqueous solubility and absorption , 2006, The AAPS Journal.