Extended solvent-contact model approach to SAMPL4 blind prediction challenge for hydration free energies

Extended solvent-contact model was applied to the blind prediction of the hydration free energies of 47 organic molecules included in the SAMPL4 data set. To obtain a suitable prediction tool, we constructed a hydration free energy function involving three kinds of atomic parameters. With respect to total 34 atom types introduced to describe all SAMPL4 molecules, 102 atomic parameters were defined and optimized with a standard genetic algorithm in such a way to minimize the difference between the experimental hydration free energies and those calculated with the hydration free energy function. In this parameterization, we used a training set comprising 77 organic molecules with varying sizes and shapes. The estimated hydration free energies for the SAMPL4 molecules compared reasonably well with the experimental results with the associated squared correlation coefficient and root mean square deviation of 0.89 and 1.46 kcal/mol, respectively. Based on the comparative analysis of experimental and computational hydration free energies of the SAMPL4 molecules, the methods for further improvement of the present hydration model are suggested.