ConfSolv: Prediction of solute conformer free energies across a range of solvents
Author(s)
Pattanaik, Lagnajit
Menon, Angiras
Settels, Volker
Spiekermann, Kevin A.
Tan, Zipei
Vermeire, Florence
Sandfort, Frederik
Eiden, Philipp
Green Jr, William H
Date Issued
December 1, 2023
Journal
The Journal of Physical Chemistry B
Citation
Pattanaik, Lagnajit, Menon, Angiras, Settels, Volker, Spiekermann, Kevin A., Tan, Zipei et al. 2023. "ConfSolv: Prediction of solute conformer free energies across a range of solvents." The Journal of Physical Chemistry B.
Version
Author's final manuscript
Abstract
Predicting Gibbs free energy of solution is key to understanding solvent effects on
thermodynamics and reaction rates for kinetic modelling. Accurately computing solution
free energies requires enumeration and evaluation of relevant solute conformers in
solution. However, even after generation of relevant conformers, determining their free
energy of solution requires an expensive workflow consisting of several ab initio computational
chemistry calculations. To help address this challenge, we generate a large
dataset of solution free energies for nearly 44000 solutes with almost 9 million conformers
calculated in 41 different solvents using density functional theory and COSMO-RS
and quantify the impact of solute conformers on the solution free energy. We then
train a message passing neural network to predict the relative solution free energies of
a set of solute conformers, enabling identification of a small subset of thermodynamically
relevant conformers. The model offers substantial computational time savings
with predictions usually substantially within 1 kcal/mol of the free energy of solution
calculated using computational chemical methods.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
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