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

TL;DR

This paper presents an automated protocol for parametrizing implicit solvent models to accurately predict solvation free energies, validated on a large experimental database with state-of-the-art results.

Contribution

It introduces a systematic, automated method for parametrizing implicit solvent models using scoring algorithms and extensive validation on large datasets.

Findings

Achieved lowest LOOCV RMS error of 1.33 kcal/mol.

Demonstrated superior prediction accuracy on multiple SAMPL test sets.

Validated protocol's effectiveness with extensive experimental data.

Abstract

In this work, a systematic protocol is proposed to automatically parametrize implicit solvent models with polar and nonpolar components. The proposed protocol utilizes the classical Poisson model or the Kohn-Sham density functional theory (KSDFT) based polarizable Poisson model for modeling polar solvation free energies. For the nonpolar component, either the standard model of surface area, molecular volume, and van der Waals interactions, or a model with atomic surface areas and molecular volume is employed. Based on the assumption that similar molecules have similar parametrizations, we develop scoring and ranking algorithms to classify solute molecules. Four sets of radius parameters are combined with four sets of charge force fields to arrive at a total of 16 different parametrizations for the Poisson model. A large database with 668 experimental data is utilized to validate the proposed protocol. The lowest leave-one-out root mean square (RMS) error for the database is 1.33k cal/mol. Additionally, five subsets of the database, i.e., SAMPL0-SAMPL4, are employed to further demonstrate that the proposed protocol offers 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 best knowledge.