Automatic parametrization of implicit solvent models for the blind prediction of solvation free energies

摘要

In this work, a systematic protocol is proposed to automatically parametrizeimplicit solvent models with polar and nonpolar components. The proposedprotocol utilizes the classical Poisson model or the Kohn-Sham densityfunctional theory (KSDFT) based polarizable Poisson model for modeling polarsolvation free energies. For the nonpolar component, either the standard modelof surface area, molecular volume, and van der Waals interactions, or a modelwith atomic surface areas and molecular volume is employed. Based on theassumption that similar molecules have similar parametrizations, we developscoring and ranking algorithms to classify solute molecules. Four sets ofradius parameters are combined with four sets of charge force fields to arriveat a total of 16 different parametrizations for the Poisson model. A largedatabase with 668 experimental data is utilized to validate the proposedprotocol. The lowest leave-one-out root mean square (RMS) error for thedatabase is 1.33k cal/mol. Additionally, five subsets of the database, i.e.,SAMPL0-SAMPL4, are employed to further demonstrate that the proposed protocoloffers some of the best solvation predictions. The optimal RMS errors are 0.93,2.82, 1.90, 0.78, and 1.03 kcal/mol, respectively for SAMPL0, SAMPL1, SAMPL2,SAMPL3, and SAMPL4 test sets. These results are some of the best, to our bestknowledge.