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Dielectrically balanced force fields improved prediction of water-to-cyclohexane partitioning of SAMPL5 molecules

Dielectrically balanced force fields improved prediction of water-to-cyclohexane partitioning of SAMPL5 molecules

Name:
Shanaka Paranahewage

Department:
Chemistry

Abstract:
Accurate prediction of liquid state properties and partitioning of molecules between different solvent environment plays a key role in the screening of drug candidate molecules in drug development process, and such computational predictions are highly dependent on the mathematical description of molecules. We have identified dielectric behavior as a key experimental parameter that can be used to increase the quality of molecular models. Using this knowledge, we developed a simple process to modulate molecular models to improve dielectric properties, and we apply this to the estimation cyclohexane-to-water distribution coefficients in the community-wide SAMPL5 event for blind prediction of molecular transfer. We found that this simple procedure is able to outperform much more complicated and computationally expensive modeling efforts, ranking as one of the top performers.