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.     

Novel distance-based atom-type topological indices DAI for QSPR/QSAR studies of alcohols

In this work, we propose a distance-based atom-type topological index (DAI) for quantitative structure-property/activity relationship (QSPR/QSAR) studies. The newly constructed index, which codes the structural environment of each atom type in a molecule, can be calculated simply. These atom-type topological indices, along with our recently proposed Lu index, were used to construct QSPR/QSAR models for several representative physical properties and biological activities of several data sets of alcohols with a range of non-hydrogen atoms by using multiple linear regression (MLR) analysis. The efficiency of these indices is verified by high quality QSPR models. The results indicate that the combined use of Lu and DAI indices promises to be a useful method for QSPR/QSAR analysis of complex compounds.